From 173db7057a189d054d533b28434f434cb39ad37d Mon Sep 17 00:00:00 2001
From: Jnan-py <jnanyalla@gmail.com>
Date: Sat, 30 Aug 2025 15:41:20 +0530
Subject: [PATCH] Add drone route optimization tutorial in optimization folder

---
 optimization/drone_opt_tutorial_math.ipynb | 1867 ++++++++++++++++++++
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 create mode 100644 optimization/drone_opt_tutorial_math.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cd121fad",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Drone Delivery Optimization using QAOA\n",
+    "\n",
+    "This tutorial demonstrates how to apply **Quantum Approximate Optimization Algorithm (QAOA)** to a \n",
+    "**drone delivery optimization problem**, which can be modeled as a variant of the \n",
+    "**Traveling Salesman Problem (TSP)**.\n",
+    "\n",
+    "We will walk through the following:\n",
+    "\n",
+    "1. **Introduction**\n",
+    "2. **Background Concepts**\n",
+    "   - The Drone Delivery Problem\n",
+    "   - Mapping to TSP and QUBO\n",
+    "   - Quantum Optimization with QAOA\n",
+    "3. **Implementation in Qiskit**\n",
+    "   - Problem Setup\n",
+    "   - Building the Cost Hamiltonian\n",
+    "   - Applying QAOA\n",
+    "   - Evaluating Results\n",
+    "4. **Analysis & Visualization**\n",
+    "5. **Conclusions and Next Steps**\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f1259ce",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## 1. Introduction\n",
+    "\n",
+    "Drone delivery services are becoming increasingly relevant in logistics and transportation. \n",
+    "The main challenge lies in optimizing routes to minimize distance, time, or energy consumption, \n",
+    "while adhering to operational constraints.\n",
+    "\n",
+    "This tutorial shows how we can use **quantum algorithms** to approach such optimization problems, \n",
+    "with a focus on QAOA in Qiskit.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a95e1b9",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## 2. Background Concepts\n",
+    "\n",
+    "### 2.1 The Drone Delivery Problem\n",
+    "The core task is to determine the optimal route for drones to deliver packages efficiently. \n",
+    "This is closely related to the **Traveling Salesman Problem (TSP)**.\n",
+    "\n",
+    "### 2.2 Mapping to TSP and QUBO\n",
+    "- **TSP**: Visit all required nodes (delivery locations) exactly once with minimum cost.\n",
+    "- **QUBO (Quadratic Unconstrained Binary Optimization)**: Encode constraints and cost function into a binary quadratic form.\n",
+    "\n",
+    "By representing drone delivery as a TSP instance, we can transform it into a **QUBO problem**.\n",
+    "\n",
+    "### 2.3 Quantum Optimization with QAOA\n",
+    "QAOA is a **hybrid quantum-classical algorithm** for solving combinatorial optimization problems.\n",
+    "- Uses a **cost Hamiltonian** encoding the optimization objective.\n",
+    "- Alternates between cost and mixing operators.\n",
+    "- Parameters are optimized with a classical optimizer.\n",
+    "\n",
+    "This makes QAOA well-suited for problems like drone routing.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2189e2de",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "### 2.4 Mathematical Formulation (TSP → QUBO → Ising)\n",
+    "\n",
+    "#### Notation\n",
+    "\n",
+    "* Let the delivery locations (including depot if used) be\n",
+    "\n",
+    "  $$\n",
+    "  V = \\{0,1,\\dots,n-1\\}.\n",
+    "  $$\n",
+    "* We use a **position-based** (one-hot) encoding with binary variables\n",
+    "\n",
+    "  $$\n",
+    "  x_{i,t} \\in \\{0,1\\} \\quad \\text{meaning \"city \\(i\\) is visited at tour position \\(t\\)\"},\n",
+    "  $$\n",
+    "\n",
+    "  for $i \\in V$ and $t \\in \\{0,1,\\dots,n-1\\}$.\n",
+    "* Let $d_{ij} \\ge 0$ denote the distance/energy/time cost from city $i$ to $j$.\n",
+    "  Define\n",
+    "\n",
+    "  $$\n",
+    "  t \\oplus 1 := (t+1)\\bmod n\n",
+    "  $$\n",
+    "\n",
+    "  for cyclic tours.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "#### Objective (tour length / energy)\n",
+    "\n",
+    "$$\n",
+    "C_{\\text{dist}}(x) = \\sum_{t=0}^{n-1} \\sum_{i=0}^{n-1} \\sum_{j=0}^{n-1} d_{ij}\\, x_{i,t}\\, x_{j,t \\oplus 1}.\n",
+    "$$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "#### Constraints (one-hot feasibility)\n",
+    "\n",
+    "1. **Each city is visited exactly once**\n",
+    "\n",
+    "   $$\n",
+    "   \\sum_{t=0}^{n-1} x_{i,t} = 1 \\quad \\forall i \\in V.\n",
+    "   $$\n",
+    "\n",
+    "2. **Exactly one city is chosen at each tour position**\n",
+    "\n",
+    "   $$\n",
+    "   \\sum_{i=0}^{n-1} x_{i,t} = 1 \\quad \\forall t \\in \\{0,\\dots,n-1\\}.\n",
+    "   $$\n",
+    "\n",
+    "(If a fixed depot at city $0$ is required, enforce $x_{0,0} = 1$ and remove its column/row accordingly.)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "#### QUBO construction\n",
+    "\n",
+    "We convert equality constraints into quadratic penalties with positive weights $A$ (constraints) and $B$ (objective scale):\n",
+    "\n",
+    "$$\n",
+    "H_{\\text{visit}}(x) = A \\sum_{i=0}^{n-1}\\left(1 - \\sum_{t=0}^{n-1} x_{i,t}\\right)^2,\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "H_{\\text{pos}}(x) = A \\sum_{t=0}^{n-1}\\left(1 - \\sum_{i=0}^{n-1} x_{i,t}\\right)^2,\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "H_{\\text{obj}}(x) = B \\sum_{t=0}^{n-1} \\sum_{i=0}^{n-1} \\sum_{j=0}^{n-1} d_{ij}\\, x_{i,t}\\, x_{j,t \\oplus 1}.\n",
+    "$$\n",
+    "\n",
+    "The full QUBO objective is:\n",
+    "\n",
+    "$$\n",
+    "H_{\\text{QUBO}}(x) = H_{\\text{visit}}(x) + H_{\\text{pos}}(x) + H_{\\text{obj}}(x).\n",
+    "$$\n",
+    "\n",
+    "**Penalty choice.**\n",
+    "A practical rule:\n",
+    "\n",
+    "$$\n",
+    "A \\gtrsim n \\cdot \\max_{i,j} d_{ij}\n",
+    "$$\n",
+    "\n",
+    "so that feasibility is prioritized over distance minimization.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "#### Ising mapping (binary $\\rightarrow$ Pauli-$Z$)\n",
+    "\n",
+    "Binary variables map to spins via:\n",
+    "\n",
+    "$$\n",
+    "x = \\frac{1 - Z}{2} \\quad \\Rightarrow \\quad \n",
+    "x_a x_b = \\tfrac{1}{4}\\left(1 - Z_a - Z_b + Z_a Z_b\\right).\n",
+    "$$\n",
+    "\n",
+    "Replacing every $x_{i,t}$ with $(1 - Z_{i,t})/2$ yields a Hamiltonian of the form:\n",
+    "\n",
+    "$$\n",
+    "H_C = \\sum_{q} h_q Z_q + \\sum_{q<r} J_{qr} Z_q Z_r + \\text{const},\n",
+    "$$\n",
+    "\n",
+    "acting on $n^2$ qubits (one per $x_{i,t}$).\n",
+    "\n",
+    "---\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3feafdff",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## 3. Implementation in Qiskit\n",
+    "\n",
+    "In this section, we implement the drone delivery optimization using Qiskit.\n",
+    "We will follow these steps:\n",
+    "\n",
+    "1. Define the delivery graph (nodes = delivery points, edges = costs/distances).\n",
+    "2. Encode the optimization as a QUBO problem.\n",
+    "3. Construct the cost Hamiltonian in Qiskit.\n",
+    "4. Solve using QAOA with a classical optimizer.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "facaa42b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### 3.1 Problem Setup\n",
+    "\n",
+    "We start by defining the delivery graph, representing drone delivery locations and travel costs.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a306e5d",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### 3.2.1 From QUBO to Pauli-$Z$ Hamiltonian\n",
+    "\n",
+    "Start with a quadratic polynomial in binary variables:\n",
+    "\n",
+    "$$\n",
+    "H_{\\text{QUBO}}(x) \\;=\\; \\sum_k a_k x_k \\;+\\; \\sum_{k<\\ell} b_{k\\ell}\\, x_k x_\\ell \\;+\\; c.\n",
+    "$$\n",
+    "\n",
+    "Using the mapping\n",
+    "\n",
+    "$$\n",
+    "x_k = \\frac{1 - Z_k}{2},\n",
+    "$$\n",
+    "\n",
+    "we substitute and regroup terms. The resulting Hamiltonian takes the form\n",
+    "\n",
+    "$$\n",
+    "H_C \\;=\\;\n",
+    "\\underbrace{\\left(c + \\tfrac{1}{2}\\sum_k a_k + \\tfrac{1}{4}\\sum_{k<\\ell} b_{k\\ell}\\right)}_{\\text{const}}\n",
+    "+ \\sum_k \\underbrace{\\left(-\\tfrac{1}{2}a_k - \\tfrac{1}{4}\\sum_{\\ell\\neq k} b_{k\\ell}\\right)}_{h_k}\\, Z_k\n",
+    "+ \\sum_{k<\\ell} \\underbrace{\\tfrac{1}{4} b_{k\\ell}}_{J_{k\\ell}} \\, Z_k Z_\\ell.\n",
+    "$$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "* The **constant term** only shifts the energy spectrum and is irrelevant for QAOA evolution.\n",
+    "* The **linear terms** give local fields $h_k$ on each qubit.\n",
+    "* The **quadratic terms** give couplings $J_{k\\ell}$ between qubits.\n",
+    "\n",
+    "Thus the QUBO is mapped to an Ising Hamiltonian suitable for QAOA’s cost unitary:\n",
+    "\n",
+    "$$\n",
+    "H_C = \\sum_k h_k Z_k + \\sum_{k<\\ell} J_{k\\ell} Z_k Z_\\ell + \\text{const}.\n",
+    "$$\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95af3b11",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "### 3.3.1 QAOA Ansatz and Mixers\n",
+    "\n",
+    "Let $H_C$ be the Ising cost Hamiltonian, and choose a mixer Hamiltonian $H_B$.\n",
+    "\n",
+    "* **Standard mixer (unconstrained one-hot):**\n",
+    "\n",
+    "  $$\n",
+    "  H_B \\;=\\; \\sum_q X_q.\n",
+    "  $$\n",
+    "\n",
+    "* **One-hot preserving XY mixer (optional):**\n",
+    "\n",
+    "  $$\n",
+    "  H_B^{(\\text{XY})} \\;=\\; \\sum_{\\substack{\\text{within each}\\\\ \\text{one-hot block}}} \n",
+    "  \\bigl(X_u X_v + Y_u Y_v \\bigr),\n",
+    "  $$\n",
+    "\n",
+    "  which preserves Hamming weight within blocks (useful when strictly enforcing one-hot constraints).\n",
+    "\n",
+    "---\n",
+    "\n",
+    "For QAOA depth $p$ and parameters $(\\boldsymbol\\gamma,\\boldsymbol\\beta)\\in \\mathbb{R}^p \\times \\mathbb{R}^p$:\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "U_C(\\gamma) &= e^{-i\\gamma H_C}, \n",
+    "&\\quad U_B(\\beta) &= e^{-i\\beta H_B}, \\\\[6pt]\n",
+    "\\lvert \\boldsymbol\\gamma, \\boldsymbol\\beta \\rangle \n",
+    "&= U_B(\\beta_p)\\, U_C(\\gamma_p)\\, \\cdots \\, U_B(\\beta_1)\\, U_C(\\gamma_1)\\, \\lvert + \\rangle^{\\otimes n^2}.\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "---\n",
+    "\n",
+    "The variational objective is:\n",
+    "\n",
+    "$$\n",
+    "E(\\boldsymbol\\gamma, \\boldsymbol\\beta) \\;=\\; \n",
+    "\\langle \\boldsymbol\\gamma, \\boldsymbol\\beta \\,\\vert\\, H_C \\,\\vert\\, \\boldsymbol\\gamma, \\boldsymbol\\beta \\rangle.\n",
+    "$$\n",
+    "\n",
+    "A classical optimizer searches over parameters $(\\boldsymbol\\gamma, \\boldsymbol\\beta)$ to minimize $E$.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8aed9a05",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "### 3.4.1 Decoding, Feasibility & Tour Cost\n",
+    "\n",
+    "After measurement, a bitstring\n",
+    "\n",
+    "$$\n",
+    "z \\in \\{0,1\\}^{n^2}\n",
+    "$$\n",
+    "\n",
+    "is reshaped into an $n \\times n$ assignment matrix\n",
+    "\n",
+    "$$\n",
+    "X = [x_{i,t}],\n",
+    "$$\n",
+    "\n",
+    "where $x_{i,t}$ indicates whether city $i$ is visited at position $t$.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "#### Validity Check\n",
+    "\n",
+    "* **Valid one-hot assignment:**\n",
+    "  Each **row** (city) and each **column** (position) must sum to $1$.\n",
+    "  A valid assignment corresponds to a permutation matrix, from which the tour $\\pi$ is extracted by reading the unique city at each position $t$.\n",
+    "* **Invalid assignment:**\n",
+    "\n",
+    "  * **Repair:** project each row/column to a valid one-hot encoding (heuristic correction).\n",
+    "  * **Discard:** drop infeasible samples (if strict feasibility is required).\n",
+    "\n",
+    "---\n",
+    "\n",
+    "#### Tour Cost\n",
+    "\n",
+    "For a valid permutation $\\pi$, the cyclic tour cost is\n",
+    "\n",
+    "$$\n",
+    "\\text{cost}(\\pi) = \\sum_{t=0}^{n-1} d_{\\pi(t),\\, \\pi(t \\oplus 1)},\n",
+    "$$\n",
+    "\n",
+    "where $t \\oplus 1 := (t+1) \\bmod n$.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "#### Quality Metrics\n",
+    "\n",
+    "* **Feasibility rate:** fraction of measured samples that yield valid one-hot solutions.\n",
+    "* **Best / mean cost:** evaluate the minimum and average cost among feasible tours.\n",
+    "* **Approximation ratio:**\n",
+    "\n",
+    "  $$\n",
+    "  \\frac{\\min_{\\text{samples}} \\text{cost}(\\pi)}{\\text{OPT}},\n",
+    "  $$\n",
+    "\n",
+    "  if the true optimum cost (OPT) is known.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea802cce",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### 4.1 Penalty Scaling & Practical Tips\n",
+    "\n",
+    "* **Penalty weight selection:**\n",
+    "  A good starting rule is\n",
+    "\n",
+    "  $$\n",
+    "  A \\;\\in\\; \\bigl[n \\cdot d_{\\max}, \\; 5n \\cdot d_{\\max}\\bigr],\n",
+    "  \\qquad \n",
+    "  B = 1,\n",
+    "  $$\n",
+    "\n",
+    "  where\n",
+    "\n",
+    "  $$\n",
+    "  d_{\\max} = \\max_{i,j} d_{ij}.\n",
+    "  $$\n",
+    "\n",
+    "* **Balancing feasibility vs. optimization:**\n",
+    "\n",
+    "  * If many **infeasible samples** are observed → **increase $A$** so constraints dominate.\n",
+    "  * If nearly all samples are feasible but routes are suboptimal → increase **QAOA depth $p$** or improve the classical optimizer.\n",
+    "\n",
+    "* **Scaling to larger $n$:**\n",
+    "\n",
+    "  * **Warm-starts:** initialize QAOA parameters from smaller problem instances or classical heuristics.\n",
+    "  * **Problem symmetries:** fix a reference city (e.g., depot at position $0$) to reduce redundancy.\n",
+    "  * **Coarse-to-fine graphs:** solve on a reduced graph (clustering cities) and refine on the full graph.\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1beff00",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### 3.2 Building the QUBO and Hamiltonian\n",
+    "\n",
+    "Next, we encode the problem as a QUBO and map it to a Hamiltonian suitable for QAOA.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cb8221f",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### 3.3 Applying QAOA\n",
+    "\n",
+    "We configure QAOA in Qiskit, setting parameters like number of layers (`p`) and optimizer type.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "201aa07d",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### 3.4 Evaluating Results\n",
+    "\n",
+    "After running QAOA, we interpret the measurement results, extracting the optimal route for the drone.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6d74ee2",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## 4. Analysis & Visualization\n",
+    "\n",
+    "We analyze the obtained solutions and visualize the optimized delivery route.\n",
+    "\n",
+    "- Compare classical vs quantum solutions.\n",
+    "- Visualize the best found route.\n",
+    "- Discuss scalability challenges and possible improvements.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a42a8f00",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## 5. Conclusions and Next Steps\n",
+    "\n",
+    "- QAOA can provide approximate solutions to complex routing problems such as drone delivery.\n",
+    "- Hybrid quantum-classical workflows allow experimentation today, even with small quantum devices.\n",
+    "- Future work may involve:\n",
+    "  - Scaling to larger drone fleets and delivery networks.\n",
+    "  - Exploring alternative quantum optimization algorithms (e.g., VQE, QAOA with warm-starts).\n",
+    "  - Integration with real-world logistics datasets.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8be9f8d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "import matplotlib.pyplot as plt\n",
+    "from qiskit_optimization.applications import Tsp\n",
+    "from qiskit_optimization.converters import QuadraticProgramToQubo\n",
+    "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
+    "from qiskit_algorithms import QAOA\n",
+    "from qiskit_algorithms.optimizers import COBYLA, SPSA, ADAM\n",
+    "from qiskit.primitives import Sampler\n",
+    "from qiskit_optimization import QuadraticProgram\n",
+    "import random\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f61923fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw_graph(G, path=None):\n",
+    "    pos = nx.spring_layout(G)\n",
+    "    edge_labels = nx.get_edge_attributes(G, 'weight')\n",
+    "    colors = ['red' if node in path else 'lightblue' for node in G.nodes()] if path else 'lightblue'\n",
+    "\n",
+    "    nx.draw_networkx(G, pos, node_color=colors, with_labels=True)\n",
+    "    nx.draw_networkx_edge_labels(G, pos, edge_labels)\n",
+    "\n",
+    "    if path:\n",
+    "        edge_path = [(path[i], path[(i+1)%len(path)]) for i in range(len(path))]\n",
+    "        nx.draw_networkx_edges(G, pos, edgelist=edge_path, edge_color='blue', width=2)\n",
+    "        \n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41975c15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tsp_solver(tsp_custom, qp ):\n",
+    "    sampler = Sampler()\n",
+    "    qaoa = QAOA(sampler=sampler, optimizer=COBYLA(), reps=1)\n",
+    "\n",
+    "    qaoa_optimizer = MinimumEigenOptimizer(qaoa)\n",
+    "\n",
+    "    quantum_result = qaoa_optimizer.solve(qp)\n",
+    "    quantum_path = tsp_custom.interpret(quantum_result.x)\n",
+    "\n",
+    "    return quantum_result.fval, quantum_path      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f5380cce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tsp_solver_qubo(tsp_custom, qp):\n",
+    "    sampler = Sampler()\n",
+    "    qaoa_cost_history = []\n",
+    "\n",
+    "    def callback_fn(eval_count, params, mean, norm, accepted):\n",
+    "        qaoa_cost_history.append(mean)\n",
+    "        print(f\"Step {eval_count}, Cost: {mean}, Norm: {norm}, Accepted: {accepted}\")\n",
+    "\n",
+    "    qubo = QuadraticProgramToQubo()\n",
+    "    qubo_converter = qubo.convert(qp)\n",
+    "\n",
+    "    qaoa = QAOA(sampler=sampler, optimizer=SPSA(maxiter = 100, callback = callback_fn))\n",
+    "\n",
+    "    qaoa_optimizer = MinimumEigenOptimizer(qaoa)\n",
+    "\n",
+    "    quantum_result = qaoa_optimizer.solve(qubo_converter)    \n",
+    "    quantum_path = tsp_custom.interpret(quantum_result.x)\n",
+    "\n",
+    "    return quantum_result.fval, quantum_path, qaoa_cost_history        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3e0ca0a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def route_cost(path, dist_matrix):\n",
+    "    return sum(dist_matrix[path[i]][path[(i+1)%len(path)]] for i in range(len(path)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6f284eb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulated_annealing(dist_matrix, temp=10000.0, cooling_rate=0.995, stopping_temp=1e-8, initial_solution=None):\n",
+    "    num_nodes = len(dist_matrix)\n",
+    "    if initial_solution is None:\n",
+    "        curr_solution = list(range(num_nodes))\n",
+    "    else:\n",
+    "        curr_solution = initial_solution[:]\n",
+    "    best_solution = curr_solution[:]\n",
+    "    best_cost = route_cost(curr_solution, dist_matrix)\n",
+    "    cost_history = [best_cost]\n",
+    "\n",
+    "    while temp > stopping_temp:\n",
+    "        new_solution = curr_solution[:]\n",
+    "        i, j = random.sample(range(num_nodes), 2)\n",
+    "        new_solution[i], new_solution[j] = new_solution[j], new_solution[i]\n",
+    "        new_cost = route_cost(new_solution, dist_matrix)\n",
+    "        \n",
+    "        if new_cost < best_cost or random.random() < np.exp((best_cost - new_cost) / temp):\n",
+    "            curr_solution = new_solution[:]\n",
+    "            if new_cost < best_cost:\n",
+    "                best_solution = new_solution[:]\n",
+    "                best_cost = new_cost\n",
+    "                cost_history.append(best_cost)\n",
+    "                \n",
+    "        temp *= cooling_rate\n",
+    "\n",
+    "    return best_solution, best_cost, cost_history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cbbbc6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hybrid_solver(tsp_custom, qp, dist_matrix, iterations=3):\n",
+    "    \n",
+    "    best_path, best_cost, sa_history = simulated_annealing(dist_matrix)\n",
+    "    print(\"Initial candidate from Simulated Annealing:\")\n",
+    "    print(\"Path:\", best_path, \"Cost:\", best_cost)\n",
+    "    hybrid_history = [best_cost]\n",
+    "    \n",
+    "    for it in range(iterations):\n",
+    "        print(f\"\\nHybrid Iteration {it+1}:\")\n",
+    "    \n",
+    "        obj_quantum, q_path = tsp_solver(tsp_custom, qp)\n",
+    "        print(\"Quantum candidate (QAOA):\")\n",
+    "        print(\"Path:\", q_path, \"Cost:\", obj_quantum)\n",
+    "    \n",
+    "        if obj_quantum < best_cost:\n",
+    "            best_cost = obj_quantum\n",
+    "            best_path = q_path[:]\n",
+    "        \n",
+    "        new_path, new_cost, _ = simulated_annealing(dist_matrix, initial_solution=best_path)\n",
+    "        print(\"Refined candidate (Simulated Annealing):\")\n",
+    "        print(\"Path:\", new_path, \"Cost:\", new_cost)\n",
+    "        if new_cost < best_cost:\n",
+    "            best_cost = new_cost\n",
+    "            best_path = new_path[:]\n",
+    "        hybrid_history.append(best_cost)\n",
+    "    \n",
+    "    return best_path, best_cost, hybrid_history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d3f866a",
+   "metadata": {},
+   "source": [
+    "## 4 nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ea8976cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "custom_matrix = np.array([\n",
+    "    [0, 2, 9, 10],\n",
+    "    [2, 0, 7, 4],\n",
+    "    [9, 7, 0, 8],\n",
+    "    [10, 4, 8, 0]\n",
+    "])\n",
+    "\n",
+    "n_nodes = custom_matrix.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "597703b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_custom = nx.complete_graph(n_nodes)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "38ec7c4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i, j in G_custom.edges():\n",
+    "    G_custom[i][j]['weight'] = custom_matrix[i][j]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1b10d272",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tsp_custom = Tsp(G_custom)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2d3032f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph = tsp_custom.graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "55740a21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dVq1a1aHtBkAWDNthw4YNk1KlSvGUArmNPTdeySUePnyoKak0adLE+bmoAUB7YOyxon1ww4YNZdmyZVqVTdHbsmWL3kEyM+B5wsPD9WcB/QsWLVqkp2aAGTFyFwYDlGhovoJGP0iHYl8UDVqaNGmihVFPw50OLv7oEYA9LAyJ6dmzJ9OhDm4R4N8UPQbIs+BYImoMevXqJW+++SYzAuR23CagRNm7d692wtu5c6cGBGgJPHXqVJk7d260n487ncyZM8uAAQO0yhovgAwEHG82hKwA7xY9y3fffafHFD/44APp3r27vo/BALkbMwOUKDhjnTx5clmzZo2e+ceYYEx9i+2ChRc6NAoix4WFhcnWrVt1L5k8x08//aRBNAYioU9BFIynfhK2h9BjwtETCUTxxcwAJRgu6sePH5csWbI8brCCFyw0XkHxU0z4YhZ/yLwgIGC9gOfAzwq2B/B/Onr06Md1NzEVGCLQxvbamDFj5OrVq25eLXk6BgOUYLioozbg0KFD2g2wWbNmGghgTgAKAjFO+OLFi0Yv02PqBdCMpmjRokYvhZwExweR7Zk+ffrj+pqnMwJPBt79+/fXAltM5cTPGXoRrFixwuFOh0SxYTBAiVKlShVNdWLfHwOEfv75Z72LxXx4/Pnzzz83eokeUy+AdrV27bzoyRxpqY3AG423UIuDXgTIDpw6dUpef/117XSIwkNk6YgSisEAJQpepIoVK6azAtCCtVatWtrtClmCV155Rdus4nw8JRz2izdt2sQtAnqcUUC74j179mjA/dZbb2mvjvz58+vPHAoScXSXKD4YDFC8PV3pjDQlggFMb3sSMgR58+aVVKlSsTo6Ef7880/t38BmQ/R0IF68eHGZMGGCZgvQ+hjFvO+995528MTpBGxD8GePHMFggOI9QfD+/fv/ej9S16iGRjEU7lCOHj2qdy7nz5+XBg0a6OewaDBx9QI+Pj6xFmWSvaHTIQZ4/frrr7p90KlTJz3pU65cOS06RIEiiw4pNgwGKE6ocEZXtJIlS+q+JWoEnr7bQFET+q+PHz9eZwkcOHBAh6xEdVKjxNULIOuCgIAoLrly5dI+HijkRVtvFB2ipiCq6HD58uUsOqRncGohxQgvGLjwDxkyRE8MVK5cWQsC8TamO31kBXCXkiNHDrev1xPhxzNTpkya8h00aJDRyyGLunnzpsyePVubfGEMOLYRWrRooaeA2PTLs3FqISUYzrNPmTJFChQoIE2bNpWcOXNqAdu6dev09EBsKX98DQMB5zl27Jhcu3aN9QKUKOnSpdOtO3QMRdEhMgTffvutFh3ie4tFh8RggP41YwBpfhT9ffjhh1qchCl5OMtcvnx5o5dn23oBnD3H3i+RM0QVHWK8MooOsf3EokNiMEBy9+5dGT58uO41du7cWbcBDh48KPPnz5eXX37Z6OWJ3esF8OKNhkNEzpQiRQotOkRRMOoL8LOPGgMWHdoTgwGb7yOiqxmalvTp00fbnSItjTGqL7zwgtHLo38yA+wvQK6GrUC8FuAkAuaMsOjQfhgM2BBGCGMoCoKAESNG6BhhvAhMmjRJcufObfTy6B/nzp2Ts2fPsl6A3AYzEKpXr66dDrGNgOPCyBq88cYbWguEU0O4YSDPw2DARnDuH/MCcBeAfgAdOnSQM2fOyNixYyVbtmxGL4+i2SIAjIgmMqLoEK8R6HSI2qGookMUCSNAnTlzJruLehAGAzaAlsBt2rSRPHny6PEiRPe44xw6dKj2BiDzBgOFChV6PMSGyCioHYoqOvzxxx+16LBly5ZadIjXli1btrDo0OIYDHgwFAHiaCCODy1btkwGDx6smQDMT0+bNq3Ry6M4sF6AzFh02KhRo8dFh126dNEaA5w2evHFF2XUqFG6DUnWw2DAAyGlV69ePa0IxgVl3Lhx+oPbrVs3VqVbBHoLYN4D6wXI7EWHeG1BQIDx2mhKhi1HDE9i0aG1MBjwIGgMhPa/JUqU0OE2U6dOlRMnTui+H7oCknVs3LhR3zIzQGaHPhgoOsT2AQYmoegQGUgUHWbPnl169uzJokMLYDBgcdinw3CSSpUqaaEZigTRSAR3le+//75Ds9LJfJDRwZ0XXkyJrFp0iAFmkydP1qJDBLYsOjQvBgMWDgJQB1C2bFmNyvEDtnjxYs0IoJEIJgmStYsHmRUgqxcdfvXVV5otQNYA9QaYhYCiw9atW7Po0GQYDFhMZGSkDg8qVqyY1KlTR+/8Map0x44d2jQIKTuy/mAR3FmxXoA8regQ/Uy6du2q2UwWHZoLrxwWERERoSk2HDXDD1bmzJllw4YNegeJkcGxDQ8ia8EdE8ZGMxggT4Otr379+mlQgODg6aJDZDtZdGgMBgMmFxoaqg2CMGYU53rRJnj79u3aQ5wXC8+tF0D/B46WJU+FDGa1atUeFx2i8Rl6nyDbGVV0iHHo5D4MBkwK40THjBmj7YHbt2+vw0NQD7BkyRIpVaqU0csjF0K2B4Eesz1kl6JDvMah4PDJosOCBQtq3cyMGTNYdOgGDAZM5s6dO9ocCHMDMD/gtddekyNHjmgEjeEh5PmZoG3btrF4kGzp6aJDHInGqaioosPNmzez6NBFGAyYxPXr13XvDEHAF198Ie+88472CJg+fbp2ECR7QCFoeHg4t4DI1qKKDtHMCE2NoooOK1SooHVTI0eOZNGhkzEYMBgiYLT0RBCAfTNEv/jm//rrr/V9ZL96AX9/f2aBiP6B18Eniw6RPcDIdYxXxgmqpUuXurXo8MHDh3I7NEJu3g/Xt/izJ0jyyIGcC446BQQEaAobL1SUeOjQhfHB06ZN01QYpgl27NhR0qdPb/TSyEDYFsIY2RUrVhi9FCLTunnzpm4jIHOKOgOcrsIodvQxQIMjZwsKi5DTt0Pkr+AwCY6IfObjfsmTSWY/H8mVxlf8fZKLmTh6/WYw4GaokB02bJjMmjVL0qRJI507d5Z27drpvy/ZG+5uMECqd+/eWk1NRHHbu3evBgV4Tb1165ZuJaDOoEGDBpIqVapEPXZw+APZc+WOXA0JF5TzxnaxjPp4oK+3vJwpQPy8zdH4zdHrN7cJ3AQnAVAHgKOB2AfDnheyAxgnzECAYN++fVo1zXoBIsehAdv48eN1y3Xu3Lni5+fnlKLD07dDZO2Za3ItJFz/HNcjRH0cn4+vw9dbCYMBF0NlOM7OorkG+gOgZwD2vj799FP9piV6sl4AhVMlS5Y0eilEloOfHdxwoQdL1JTWp4sO//rrL4ce68iNu5oRePgo7iDgafh8fB2+Ho9jFQwGXABRKLoDYmYAZgdgYtd3332nbz/88EPx8fExeolk0v4C+H7hcCmixBcd9u3bV2+8EBBEFR2i02FU0SG6ukbn9O0QOXQ9+r4G94ODZe74kfJF6ybSokwhqVcwi6xb9FOM68DjnLFIhoDBgJODgFWrVukZcUwRxEz6efPmycGDB7W4JXlycxWWkLm+dziciMj5nQ6rVq2qk1wvX74s48aN08mub775puTIkUN7uaCPy5M1Avuu3pGY3L11U+ZP/FIunDouzxcoJI7Ye/WOPq7ZMRhwAvSRX7RokaZ3a9WqpYVg6LGNYTMoYkF1OFFs8IKEXhOsFyByDRTnotPhrl27Hr82T5kyReu4KlasqEWIOy/dlNjKC9IGBsrU/+6VSet2SPNufRx63kf/bBmYHYOBRMBFf/bs2XomvF69eloIiJQUBs28/vrrbCdL8aoXQNCIbQIicn/RYf+hw+VGWGSsNQLJvX0kbcbAeD0XHg+nEXA80cwYDCQAOsRNnTpVe2c3bdpUJ3Ft2rRJ1q1bpykpBgEUX9giKFGiRKKPQhFRwooO56z6TR65qIEQrghmP13gUcGAqztD3b9/X/tm58mTRz744AONLpFyQoMYzOYmSkxmgPUCRMYJTuotSZK65pKI7AAaFpmZOboimLwz1N27d/VI4OjRo+XGjRvSuHFj7Q+A4ypEiYXRrShqMrJeAFMy0ZQEjbB8fX0NWweRESIePoz2+uFMeHzcoHq5KOCwbTDgaGco/Aecuh0iJ2+HxLszFFpeIhOAClQ0g3nvvfe0+hSZASJnZgUA56GNgFMvzZo102ZYmInRtm1bPd3A7S4yGr4PcQQwLCxMt2ef/hWf98f2uSnSpJfKrTu6/O9zLzxS0qRgMOA0yATg+EdU1Wd8O0MVDQzQTEFMMA3ryy+/1BfGyMhI3RLA1CycUSVyRTBQuHBhQ+ZS4CTM4sWL5fDhw/LKK6/on8ke8NrmigtrYj736ffH1AsgPry8vLR3x5O/0OvlyT/ncPCYYGI9NPH4ZcsFA+joFFNDiLg8euKYR1hkpBRMn/pfH79w4YJ2qZo8ebL2BOjQoYN06tRJAgPjVz1KFN/iwSpVqhjy3Bs3btTjVaioxrAsd05/8/S7WWdeFF3xGM4I/J6+qMZ0sY36hYI99Md35HPj8/6YPhe/0GsgLrdDI2Td2eviaklNnG2zVDAQU2eoc8ePyrwJo+XkwT/l9vWr4pMipWTLm1/ebNVWSlWpEe1j4XFSJEsmOdP4ysmTJ3V4ELoEopob9QAff/yxnkslciVkoTC8qn///m5/bjRhGTJkiAa9mPSGQMDswQAuYEZeQB19f2LhmGl8L4qpU6d26YX16ffjjttTtpJSeSfzqOfx6GAgts5Q1y5dkPvB96Ry3QaSNjCzhIXel61rVsiwdu/JhwNGSI13mkb7dXv+ui1DPu8h076ZKBkzZpTBgwfLRx99pD9URO66Mwd3nyTAnSuCX1x0WrRooXUDyIZF3UVFVzOATpqoozHyIozUdmIl5OKHmwRnXEAdeQz8P7BRmXt5JU2qxeaOFBGunDVdQu4Gyc2rV/TPO9evlZtXLuvvazZtJX6po58MiMc3a/GgpUYYbzx/Q/f8Hd1xwYtG93qvSnhYmHy16r/Rf86DB3J8707xv3VRp1ylTJnSqWsmevJuNroL3cCBA3WOxfz5812yR4vuamiu8jQ0xnr77bf1LWoVzp07J++++65UrlxZOnbsqL0znoYCR0yAiwkCCVfcgTrzMXCh9ZS7WXKufVfuaLF5XNeYj6qU1hvQ6Hzz6zYJzJb9mffjOy53Gl8pmsn9E2odvX57WeX4IE4NxAci6/SZs8jJA/ti/hwvLylYsqxUy5lBUibw2CEZB3FsVBGUWdLD0b3fkdR7XH0qcBGL78UPXdViKkpEwSC2KLA9EJUFwL8lRm3//PPPOuDlad9//71mFGJ6Pt7NkpXlwpaxA42Bvl23Pd6PjQAjtqJ1M/CySq1AbMcHo4SGhEh42H0JuXtXdqxbI3v+u14q1KzjUGcoIyI2s9/NPlkEZdY92oTMKX8SLoJPX9jiuuDiHD7O4yf2Lhbrr127tk5Xa9iwYaxpY2ffzaIvO/qxRz1PSEiINGrUSDMDyFZEh0dqyZP5+yQXP3kgQZG4mXTepRE/uRl9vRPc58ZdLBEMoKGQIy/53w0fIGt++uFxyrJM9VrSus9ghzpDFRX3wZ2iWe9io97njEIyFBjFN7WLC607U8xG3s1iwiXgjH/evHnd+tyoi3myNgb/7wg48AsV30R2M2/ePOnRp58Mm79KkiVNhjsFpzwuHgb9bczOy5M6Q9Vu0VrKvlpbbl29IptXLZOHDyPlgQPnVO+FP5AfZs+R8ND7brkIO/NIj6MXP9RDRN3NumOP9sliNIq5v0DmzJlNcceNoAh9NNKlS2f0UojcCq/J3bp109oaZMeKBPrL4TvOax1cLNDxRndGMn0BYWLOfw5s1UiC7wbJsHkr4kyzdqlbXc4cOfhMgwojipziegy8cLMIyvqQps+aNav89NNPRi+FyJZQOIstut27d2ujuXbt2ulra2L62TypUIbUUjC9scPHPKaAMDEdm8q++rpM6tddLp0+KVlzx56G3bJ1qwSm9uXdLLkFhl5t375dxowZY/RSiGwJkwpxggaFths3bpTSpUs//hga0vkkS/a40218rkK4TcO9GjIC6GNjFaa/8iWmY1N4WKi+Dbl3N87PTeHjw0CA3AaBAAo0jRxORGRHODXTr18/qVmzpgYAyAqUfiIQiILq/+o5M2rxH8R1JYr6OD4fX2elQMASmQFHOjbduXFdAtJn+Nf7UCuwYcl88U6RQrLlye+U5yFyZr0Aajgwk4CI3APNtZo0aSLr1q2TL774QrvNxnYT6OftJRWzp3fLdFyjeXlCZ6hv+3WX+/fuSaGSZSRdpsxy+/o1+WPZIrl46oS06NFPUvr5xfocZu8MRZ45jwA1A8xGEbkHGmahPgAFg5jQWbVqVYe/1t8nuR4/x6kzjCHG9EFsYSNzjRtJT7h+WOJvgIgrthQNegkkSZpEfpn7vUwe8JksmzlZ0md+TnpOnCF1Wn4Y62Mn+efxidwFxzbxwuTuFsREdoQaeRQHYionOmvu2bMnXoHA03DhT5MiuaRL6a1vPSEQsERmwJHOUBVr19VfCWGFzlDkWfBiFBwczHoBIhdDBT1azS9cuFC6dOkiQ4cO1WPPZNFgACmaQF/veM0m8KTOUOR59QLo+1C8eHGjl0Lksfbt2yf169eXq1evajCAWRwUM8vkN9DBydlH663SGYo8r16gXLly2jOCiJxv5syZUrZsWT02uGvXLgYCnhQM+Hl7SdFA5164rdIZijwHuk8iGGC9AJFr+ne0bt1aWrZsqT0EMJXT3a2+rcpSV0Ls7YdFRjqtM5TVzoGS9R06dEhu3rzJegEiJztx4oRuCxw9elSmT5+uAQF5YGbgyc5QSO0nTRJ3E4inRT54oH/h4pkCDG8RSfaErABaXiOFSUTOsXjxYilRooQW5m7dupWBgB2CgcR0hjq0Y4vsW/QdMwJkaPFgyZIldTojESUOunh27dpVawKqV68uO3fulKJF3TmD1nNYMhgAv386Q1XLmUFyp/HVxkHRfl7yZPpxfF7kqQMydEB/OXv2rLuXS6TnnVkvQOQcFy9elMqVK8u4ceO0j8D8+fN1IA956NTC+IirM9S9e/ckf/78UqlSJZkzZ46hayX7OXXqlI4rXrZsmbz++utGL4fIsn777Tdp3LixnsiZN2+elC9f3uglmZaj12/LZgYS0hkqVapUMnjwYPnxxx91X4nInZAVwHjUChUqGL0UIsuexhk0aJBuCRQrVkwbeDEQcA6PCgYc0aJFC3n55ZelU6dOmrYlcme9wEsvvSRp06Y1eilElnPjxg3NqPXt21d/rVq1SjJmzGj0sjyG7YIBDIbBDHlkBn766Sejl0M2ywzwSCFRwkZ+o2Mn3q5cuVL69+8vyZJx0qwz2S4YANQM1K1bV3r06KFNKohc7a+//pLjx4+zeJAoHpC9nTBhgk74fO6552T37t3y2muvGb0sj2TLYABGjBghly9flrFjxxq9FLJJVgAYDBA55u7du9KkSRP5+OOPpW3btrrNliNHDqOX5bFsGwzky5dPOnToIEOGDNG7NiJXwgsZ2qLi7oaIYnfw4EEpXbq0LF++XLdzcXyQszxcy7bBAPTp00e/wVCMQuRKrBcgcszs2bM1EEBNAJoINWzY0Ogl2YKtgwFUdaMQZdq0afLnn38avRzyULdv39bvL24REMUsNDRUtwOaNm0q9erVk23btkmBAgWMXpZt2DoYgI8++ki3DDp37syjhuQSmzZt0u8tZgaIonf69GktEpwxY4ZMnjxZvvvuOx0/TO5j+2AgefLkMmrUKO1otWLFCqOXQx5aL5AlSxbJlSuX0UshMh105MSxQUzz3Lx5s7Rp00abc5F72T4YgNq1a0u1atV04AUGXxC5ol6AL3BE//PgwQP57LPPpE6dOvrzsWvXLg0KyBgMBjCgIUkSGT16tJ4D//bbb41eDnmQkJAQ2bFjB+sFiJ6AE1y4ARs5cqQMHz5clixZws6cBmMw8I8iRYrI+++/rwWFt27dMno55CFQBIU7INYLEP1tw4YN2hL+6NGjsm7dOunevTuzZibAYOAJAwcOlPDwcPniiy+MXgp5UL0A7ngKFSpk9FKIDB8yhCxAlSpVpGDBgjpkiEGyeTAYeELmzJmlV69e2v4SWwZEzqgXwBYBZmIQ2RWyrW+99Zb07NlT28CvXbtWX2/JPPgK9ZRPP/1Uu8QhdUWUGMgyoTqa9QJkZygMLFGihGbJcHIAXV+9vLyMXhY9hcHAU1KmTPm4oGX9+vVGL4csDENVMAiLqVCyI/TWmDRpkpQvX17SpUunPw8YQUzmxGAgGu+8846ULVtWGxFFRkYavRyy8BaBr6+vFksR2UlwcLC0aNFCm7qhMHvjxo3ss2FyDAaigcrWL7/8Uvbu3Svff/+90cshi0JaFHdFaGxFZBc4JVCmTBlZuHChzJo1SyZOnCgpUqQwelkUBwYDMUBmoHHjxtK7d2+5d++e0cshC1ZO426IWwRkJ5gwWLJkSc2oor/Gu+++a/SSyEEMBmIxdOhQrYIdMWKE0Ushizlw4IAOKGLxINmlWPaTTz6RRo0aaV0AAgEep7UWBgOxeP7557VuALMLzp8/b/RyyGL1AtgeQLqUyJOdO3dOM2Do3opj2XPmzJFUqVIZvSyKJwYDccC5WH9/f+0/QBSfeoFSpUrp6RQiT7V69WotkL18+bJui7Vv357dBC2KwUAcUqdOLYMGDdJCmO3btxu9HLLIkaqo4UREngg1AX379pVatWpp9gvHBkuXLm30sigRGAw4oGXLljq7AFsGeKEnis3Jkyf1Ton1AuSJrl69Kq+++qoMHjxYW7cvX75c0qdPb/SyKJEYDDggWbJkOtVw06ZNsmDBAqOXQyaHrABSpRUqVDB6KUROhddAjBnev3+/rFmzRk9bsdW2Z+D/ooMwbhNVsuirHRoaavRyyOT1AkWLFpWAgACjl0LkFMiIovdKpUqVtHkQtgWqVq1q9LLIiRgMxEPUqYLx48cbvRQyMdYLkCe5c+eO1K9fX7dJMbsFY4ezZs1q9LLIyRgMxEOBAgWkbdu2uleGfTOip126dElrBlgvQJ5g37592kTo119/lUWLFsnIkSPZUdNDMRiIp379+mkNAd4SRZcVAAYDZHUzZszQTqzoGYDJgxhBTJ6LwUA8oWoWR2omT54sBw8eNHo5ZMJ6gfz580umTJmMXgpRgmDSJoYLtWrVSpo2bapjuPPmzWv0ssjFGAwkQLt27SRPnjzSpUsXo5dCJsN6AbKy48ePS7ly5bSLIDIDU6ZMYeMsm2AwkADe3t66d/bLL7/IqlWrjF4OmcTNmzf1yBW3CMiKUBOA+gCMH962bZu89957Ri+J3IjBQALVqVNHj9kgO/DgwQOjl0MmOYMNzAyQlUREROjrWL169aR69eqyc+dObbJG9sJgIIHQVGbMmDFy5MgRrR8gQr1AtmzZdMAVkRVcvHhRKleurMel0Udg/vz57I9hUwwGEgEDOpBKw8kCjKsle4uqF+CgFrICHBfEa9iZM2dkw4YN2kOA37v2xWAgkTDECNW36D1A9rZixQoZOHCg0csgitXDhw91pkCNGjWkWLFismfPHilfvrzRyyKDMRhIpCxZsmiLYqTZ0GyG7H3sNHfu3EYvgyhGN27ckNq1a2s2E0ekUQCdMWNGo5dFJpDkkQNj+IKCgnQfCW0p/f393bMyCwkJCdHuhBjlyUFGRGRGOCHQoEEDfb2aPXu2Th4kzxfk4PWbmQEn8PX1lWHDhsnChQsfd6AjIjID3O9NmDBBj7xipgC2BRgI0NMYDDhJ48aNpVSpUtKpUyfdkyMiMtrdu3f1tenjjz/WZmkoFMyePbvRyyITYjDgJJjpjaM56OE9a9Yso5dDRDaHdum4QUFh67x582Ts2LHaMI0oOgwGnKhChQq6J9erVy/t4kWeJzIyUs9mI+CbNm2a7sPy/5rMBt+fpUuX1gmDaCKE1yWi2DAYcLLhw4fLtWvXZNSoUUYvhZwM/684koUTA/h/Rtc2HMlCC9eOHTvyNAkZLjQ0VD766CNp1qyZ1K9fX4NVFDcTxYXBgJPlypVLm3eMGDFC7yDJcyDN+vvvv2vaFUeysA/74osvagtXNHBp3bq1nD592uhlkk3hew/ZyZkzZ2pXVLxFcTORIxgMuAC2Cfz8/KR3795GL4WcaOnSpXrHVa1aNW07jCxB5syZdeY7TpHgDDf6TYADJ3aJnGbZsmVSvHhxuXXrlo4cbtOmDbsJUrwwGHABnOlEJ7rvvvtOCwrJMyDAS5Uq1b8u9tu3b5fAwEBJly6dbhUgKMAdGl+IyR0wJK1nz546OO2VV16R3bt3a1BAFF8MBlwEKWOkkDt37sy7RA8pHMTZbPSTQG3ApUuXZNy4cfpiXKVKFf2cqlWr6scyZcpk9HLJBi5fvqxZKtQnYVty8eLFkiZNGqOXRRblZfQCPJWXl5eMHj1aXnvtNVmyZIm89dZbRi+JEiFZsmTSpEkTWb58uRZkYXsAGaD+/fvrsVIEC2vWrNH3cZ+WXA21K40aNdIM1Lp16zg2mxKN7YhdrFatWnLs2DE98+vj42P0csgJsCd74MABnfiGc9yArQH0mciQIYP2fCdyBTQ0GzlypNYlYVtgzpw5GpgSJfb6zWDAxQ4dOiRFihR5fBSNPBMKt86dO6fd3VA/QOSK77EWLVposSCCgQEDBmgGkig2DAZMpH379joY5MSJE3rnSEQUHyhERt8AvAb/8MMPOnmQyBEcVGQi2Fd+8i0RkSNwrzZp0iRtboUbCZwWYCBArsBgwA0wL/zzzz+Xb7/9Vg4fPmz0csgJohJqHEpFroI2182bN9eOgjidtHHjRsmZM6fRyyIPxWDATdCt7vnnn5euXbsavRRygvnz5+uRwvv37xu9FPJAR44ckTJlysiiRYt0i/Hrr79mATK5FIMBN8EPMs4Cr1y5Uo+gkbWtX79ez3mjERGRM82dO1dPqeC46o4dO/RIK5GrMRhwo7ffflv+85//6KkCNKsh60KnQZ7tJmcKCwvTDGLjxo3l9ddf10CgUKFCRi+LbILBgBuhQciYMWP0jPr06dONXg4l0PXr17VvBAI7Imc4e/asBpcoFsSWAPoHRLW+JnIHBgNuhnG3KArq06ePHvkg60EhFzAzQM6wevVqnSfw119/yaZNm6Rdu3acbUFux2DAAIMHD5a7d+/K0KFDjV4KJXCLAMWgOXLkMHopZGGoCcBNAbqUYvIljg1GdbQkcjcGAwbA+Nvu3btr+9ozZ84YvRyKpz/++INbBJQoV69e1cFXQ4YMkUGDBmlXwfTp0xu9LLIxBgMG6datm/7w9+jRw+ilUDwgo7Nnzx5uEVCCYSsAcy32798va9eu1dbCGHZFZCR+BxoER9JwVzBv3jwdfEPWsGXLFk3vMjNACWlUhQJiDBjKnTu3BpVR46+JjMZgwEDNmjXTwqFOnTqxk52F6gXQURJjjIkchb7w9erV02PFnTt31rHDWbJkMXpZRI8xGDAQUoOoG9i+fbs2GiHr1Auw2psctXfvXilRooT89ttvsnjxYm0+ljx5cqOXRfQvDAYMhr1nNCPq2bOnhISEGL0ciqMpzLZt21gvQA5DP5Fy5cpJ6tSp9bRA3bp1jV4SUbQYDJjA8OHD9YwxsgRkXugIh4CA9QIUFwT2rVq1kvfff1+3A1EXlCdPHqOXRRQjBgMmkDdvXvnkk0+07wD63ZN56wVwh1e0aFGjl0Imdvz4cc0GYOtv5syZMnnyZEmZMqXRyyKKFYMBk8CI4xQpUmgTEjJvvUCFChUkWbJkRi+FTGrhwoVaH4Bpllu3bpUWLVoYvSQihzAYMIk0adLIgAEDdI8RBUdkLjhOiPPhrBeg6EREROgpgfr162szoZ07d0qRIkWMXhaRwxgMmMgHH3ygR9bwooIzyWQe+/bt04ZDrBegp124cEEqVaokX331lYwdO1Z7h/j7+xu9LKJ4YTBgIjhuNHr0aFm/fr22JyVz1Qv4+Piwdzz9CzoIopvguXPndBupY8eOPHZKlsRgwGRq1qwpNWrUkK5du0p4eLjRy6F/4IW+TJkyGhAQoUnYwIEDdUsAwQCODaJokMiqGAyYDO4qkB04efKkfPPNN0Yvh/5pI4vMAOsFCK5fv66TBvv37y99+/aVVatWaVdKIitjMGBChQsXljZt2mhB4c2bN41eju0dPXpUrl27xnoB0hMCaCGOAsHVq1drQMDTJeQJGAyYFAKBBw8eaCqSjIWsAF7wmQa2d3YIBYLIDmXNmlWHDGE7j8hTMBgwqUyZMknv3r3l66+/1jtTMrZeAPvCaDhE9oNTJI0aNdLGYO3atZMNGzZI9uzZjV4WkVMxGDAxVCZny5ZNunfvbvRSbI31AvZ14MABPUGycuVKPTKIo4Pe3t5GL4vI6RgMmBg6EmJuwdKlS3XkKbnf2bNn9RfrBeznhx9+kNKlS+uRX9QINGjQwOglEbkMgwGTwwtQ+fLltc85GxEZkxWAihUrGr0UcpPQ0FD58MMPpXnz5vrzh0mVaAZG5Mm8jF4AxX3UcPny5ZI2bVo928yGJu4PBl588UXJkCGD0UshNwgODtYtoYMHD8qUKVN06iB/5sgOGAxYAAIBSJqUiRwjigfRapY8C7Js0V3k/fz8pGHDhnpSAEWjRHbBYMDDIHvAoME5rl69KkeOHNHGMmRtuNM/duyYBgB169aN9W4fBbvMBpDd8KrhQQ4dOiRz5syR27dvG70Uj7Bx40Z9y+JBaxs2bJhUq1ZNhgwZIu+88462EEYTqZgwECA7YjDgQdDC+Pvvv5eePXsavRSPqRfIlSuXHu8ka/r8889l0qRJMmvWLO0YePjwYR1FjYJcYFEu0d8YDHiAqIFGb7zxhrRv314mT56sbVMp8fUCzApYe4YAGgRh1kfVqlUlICBAcufOLU2aNJHffvvN6OURmQqDAQuLjIzUO5uoJihTp06V1q1by3PPPSeXL182enmWFhQUJHv37mWzIQvDCRB0DIw6Furl5fU4ePb399ffc0uA6G8sILQYzE1HkWDOnDkfD0jBXQ4yAhcuXJC2bdtKy5Yt9Q6IEm7z5s3678zMgLU1btxY3yJoRvCMgCAiIkIKFiz4+P0MCIiYGbCcadOmyVtvvaW/P336tB6Bql69uhQrVkzWrFmjA44KFSqk3QspcfUCmA+RL18+o5dCTvDkBR8/N6lSpXr8ftQQ4KQBkZ0xGLCYLl26aBEU7ljz588v9+/flyVLlsjEiRO1U6Gvr+8zX8MiqYTXC/Cu0XMgK4BJoJcuXXrcQwDbalWqVNGug0R2xm0Ci8Fe54gRI+TTTz+VMWPG6FGpzJkzP3PRwsUsLCxMswT4OGeuOw4Xhu3bt8uoUaOMXgo5GWppUEuAeQNly5aVGzdu6DAiZoDI7pgZsCCMUsUL2s2bN7VYMCoQQJMczFzPkyeP1KlTR2sHsIWArQNyHAIBFJmxXsC6UB+Amo+n4Wdm9+7d+n+bNWtWOX78OAMBIgYD1jV+/HhZu3at3LlzR/+8a9cuqVmzpvTo0UPefvttbZiDaYeYejhy5Ej9ODleL4BjaC+99JLRS6F4QjasQ4cOmv6PrhMnCgd9fHxk5syZsnDhQkPWSGRG3CawqEaNGkn27Nn1ovXzzz/r2WmcpV68eLHkyJHj8ecVL15c90TRjKhEiRKGrtkqsMVSoUIFbq1YDEZNY64AjoSOHTs22pMCCAQQQEcdxyWivzEzYGG4YEWdMEA2AK2IEQggRYpfTx5HxGkDihsKzHCskP0FrGXVqlUa+F65ckWzYjhiG1PxJwMBomcxGLC4o0eP6r4nggEcl0IQgDta/Lp48aI0a9ZMzpw5w6yAg3BXee/ePdYLWAS+39FyuFatWlKuXDmtByhVqpTRyyKyHG4TWBxOCqDtapo0afTPCAJw3BDbBfPmzdOiwunTp0uRIkWMXqpl6gXQo6FkyZJGL4XigO9tNBX6/fffdQgR6mU4sZMoYRgMWBxqBlq1aiVt2rTROgLMY1+/fr3uiwYGBkrHjh0fNykix+oFcOSMqWRzw1YAjtViWweFtKiLIaKEYzDgAXBiABf+RYsW6WjWSpUqaY0A5rZnyZLF6OVZBgrOkBlAa2cy7/8R+msgC4AmW5g+yO9xosRjMOBBnQlRNIUOhNgmSJkypb6fvdcdh86OaELD4kFzun37tvbOQMfNbt266dZA1PAhIkoc/iR5kKhWxFGBADAQiN8WAS4u2CYg8xV21q9fX+tjUA+DrBcROQ+rbYj+gS0CnLpA3QWZB47OIkBLnTq1Ns9iIEDkfAwGPNiTvQYodthOiRpOROYQEhKi2wLoJti8eXPt/4BW20TkfNwm8GD79+/XwSxoU0xxd6+7cOEC6wVMAiOFsS1w4sQJbR3cokULo5dE5NGYGfDwUwbvv/++BAcHG70U00NW4MmujmScBQsWaJ8HTI/ctm0bAwEiN2Aw4MGGDh2qU9ow8pjirhfAYKJ06dIZvRTbioiIkM6dO0uDBg3k1VdflZ07d3JYFJGbMBjwYDlz5pROnTrp1EKkwClmrBcwFr4/0R8DI7jHjRun3TP9/f2NXhaRbTAY8HCfffaZVmH36tXL6KWYFobbYI+a9QLGQAfBl19+WQdqISj75JNPeCSWyM0YDHg43F198cUX8sMPP2jalaLfIgBmBtzr4cOHMnDgQN0SwMTBPXv26LAhInI/BgM2gCJC7L1iywBH6OjZYABH1tjW1n3QPAiTBvv37y/9+vWTlStXSoYMGYxeFpFtMRiwAUwyHD16tA53WbhwodHLMR3WC7jX1q1bdVsADYRWr16twQC+R4nIOAwGbKJ69epSu3Zt6d69u4SFhRm9HNPAdMd9+/axXsANkJUaP368Bl7Zs2eX3bt3S40aNYxeFhExGLAXnCpAkRZekOlvmzZt0osUMwOuFRQUpCOHMVK7Q4cO8vvvv2tAQETmwGDARl544QWdbDho0CAddUx/1ws899xzbHPr4k6YpUqV0i2B+fPny5dffine3t5GL4uInsBgwGZQsJU0aVLdp6X/1QvwKJtrfP/991KmTBnx8fHR0yxoMUxE5sNgwGbSp08vffr0kUmTJsnBgwfFzu7fvy87duxgvYALoJXwBx98oK2EGzZsqEWD+fPnN3pZRBQDBgM2hD3b3LlzS9euXcXO0PceLXBZL+Bcp06dkvLly2tWYOrUqTJjxgzx9fU1ellEFAsGAzaE/VrMK8AeLn7ZuV4gTZo0UrhwYaOX4jGWLl2qDYRwSmPLli3a44JbMETmx2DApurWrSuvvPKKdOnSRR48eCB2rReoWLGi1lBQ4uB7qEePHvLmm29K5cqVtYcAegkQkTXwVdCmcLc2ZswYOXz4sKZy7QbbA5s3b2a9gBNcvnxZqlSpoo2tRo0aJYsWLdKMCxFZB4MBG0M6FwVeffv21bSunaAPfkhICIOBRFq/fr1mAE6cOKG/R6aJ2wJE1sNgwOYGDx4swcHB+tZuWwQoakNARAkbMjR06FCpVq2aFCpUSIMrFmISWReDAZvDcB7s9WKGPKrA7VQ8iAl5yZMnN3oplnPz5k2pU6eOjsXGiGyMIM6UKZPRyyKiRGAwQJrazZgxowYFdrmrRTDAO9n4Q+MgZFNQb7FixQrtZskhQ0TWx2CAxM/PT1O+CxYs0MmGnu7QoUNy69Yt1gvEA+Y3fPPNN1KhQgUJDAzUbQGMICYiz8BggNS7774rJUuWlE6dOumds6fXC2B7AG1yKW737t2TZs2aSbt27aRNmzaaVXn++eeNXhYRORGDAVI4a4+jhkgDz5kzRzwZLmYIfNgVL244elq6dGlZsmSJzJ49WyZMmKBzBojIszAYoMewh45BMigKw7E7T013Rw0notj9+OOPOm0QMMOhSZMmRi+JiFyEwQD9y/Dhw+Xq1avaQMYTnT59Wi5dusR6gViEhYVJ+/bt9eKPjoLbt2/X8ddE5LkYDNC/YIBRx44dZdiwYXrR9DTICqApDgrh6Flnz57VrAm6Uk6cOFFmzZolqVKlMnpZRORiDAboGb1799b9dLz1xHqBIkWKsF1uNFauXKndBJEZwqmStm3bspsgkU0wGKBnBAQEyMCBA+W7776T3bt3iydhvcCzIiMj5fPPP5fatWvr6GH8n0fVChCRPTAYoGjhCBn2iTt37qxFd54yUAc99Fkv8D9XrlyRGjVqaJ+JIUOG6AjidOnSGb0sInIzBgMULS8vLy0i3LBhg/z888/iKVsEwMzA//49sC1w4MAB+fXXX/UUCcc5E9kTf/IpRq+99pr+6tatm4SHh4snXPzy5csnmTNnFjtDpgejhitXrix58+bVboL4PRHZF4MBihUuGjiO9/XXX4vVsV5A5Pbt2/L2229rgIeZFOvWrdNhVURkbwwGKFYvvviifPDBB1pQeOPGDbEqzCLYv3+/resFkAEoUaKErF+/XjsKoqcEtoOIiBgMUJwGDBig8wrw1qo2bdqk6XE7Zgbw9542bZqObMZJEZwWQDMhIqIoDAYoThhvjKNnaEJz5MgRsWq9QNasWSVXrlxiJ2gr3bJlS2ndurU0b95cRw+jsRQR0ZMYDJBDPvnkE8mRI4fuNVu1XgBbBHZqonPs2DEpW7aszJs3T3tGTJ48WVKkSGH0sojIhBgMkEMwqW7EiBGyfPlyPYZmJcHBwTqN0U5bBAsWLNDJjJgzsG3bNs0KEBHFhMEAOaxevXpSsWJFbUSErnVWgYvhgwcPbFE8iCOgnTp1kgYNGuixUEwbfOmll4xeFhGZHIMBchhS7GPGjNGq/OnTp4uVtgjQVc/TJ++dP39eKlWqJBMmTJBx48bJTz/9JP7+/kYvi4gsgMEAxQt61jdt2lQLCoOCgsQqxYPYIvDk7npr1qyR4sWLa0CA4Ac1HnaqjyCixPHcV0dyGfSxv3v3ro45tkLafMuWLR5bL4DtGhz5xJYAggH0EsARQiKi+GAwQPGWLVs2PVWALYMzZ86ImeFM/f379z2yXuD69etSq1YtDQb69++vI4gzZMhg9LKIyIIYDFCCIBjAPjyG25gZUuZ+fn46kMeTINuBvxOCnV9++UX69u0ryZIlM3pZRGRRDAYoQVKlSqUjb+fOnasXJjPXC5QvX95j2u6imyCKA5HpyJ49u24LVK9e3ehlEZHFMRigBMPZddyd4igbLlJmgxbKGzdu9Jh6ARRsNmzYUD799FP5+OOPdbw0tmyIiBKLwQAlGKrzUTeAc/zIEJjNgQMHdEqfJ9QL4DgnTnJgSwANhfDvnjx5cqOXRUQegsEAJQrOtdetW1d69OihhXpmqxfABbN06dJiZWglXKZMGe0CiU6KaP5ERORMDAYo0dCm+K+//pIvv/xSzFYvgEAgZcqUYkWhoaHSpk0bee+99+Sdd96RrVu3Sv78+Y1eFhF5IAYDlGj58uWTDh06aP8BBAVmgBoGZAasWi9w8uRJLXycNWuWjh+eMWOG+Pr6Gr0sIvJQDAbIKfr06SPe3t761iwXUwQmVqwX+Pnnn6VEiRJaMIiTGq1atTJ6SUTk4RgMkFOkTZtWG9/gLnbfvn1GL0ezAmjHi7trq4iIiJDu3btrDUblypW1PqBYsWJGL4uIbIDBADnNRx99JAUKFNCphkYfNUS9AC6kAQEBYgWXLl2SqlWr6imBUaNGyaJFiyRNmjRGL4uIbILBADkNKvdxIVu3bp0sX77c0LVYqV5g/fr12q/hxIkT8vvvv0uXLl04ZIiI3IrBADkVeuVXq1ZNunbtqmlvI1y8eFFOnTpl+noBNEVCF0f8exUuXFi7CVasWNHoZRGRDTEYIKfCHS1S3bjL/eabbwzbIgAzX1hv3rwpb7zxhvTu3Vt69eqlI4gzZcpk9LKIyKYYDJDTvfTSS/L+++9rQSEuekYEA6hdMOvFdceOHTpuGH0DVqxYIV988QWHDBGRoRgMkEvgAodtArw1ol7AjFsEKKqcOHGiZiwCAwN14iC2VYiIjMZggFwCd+VIf0+YMEGOHTvmtue9ceOGziQwW/HgvXv3pGnTptK+fXvtKojsxfPPP2/0soiIFIMBchlMM8yaNauenXeXTZs26VszZQYOHTqkbZHRTGjOnDkaIGHOABGRWTAYIJdJkSKFDBs2TC+COD7nri2C7Nmzm+auGxd/TBtEYSVqBRo3bmz0koiInsFggFwKA3bKli2rjYgiIyNd/nxIv5shKxAWFqZbAu+++6689dZbsn37dnnhhReMXhYRUbQYDJBL4Y4Y0wz37t2ro3hdvS+/a9cuw+sFzpw5o0WCU6dO1eOVP/zwg/j5+Rm6JiKi2DAYIJdDZgDpcZypxwXbVXBUD9kHIzMDOCqIY4PXrl3T+gW0aGY3QSIyOwYD5BaoHbh9+7YMHz7cpfUCGTJkkIIFC4q7PXjwQIOd119/XSpUqKDHBkuWLOn2dRARJQSDAXKLHDlyaN0AZhecP3/eZfUC2CJw9534lStXpEaNGhrwDB06VAsm06VL59Y1EBElBoMBcpuePXvqFMHPPvvMJQV72CZwd70AAhAMGcLxwd9++03/jkmT8seKiKyFr1rkNqlTp5ZBgwbJ7NmztbremVA4GBoa6rZ6AXQTHDlypFSuXFny5cunQ4YqVarklucmInI2BgPkVi1btpQiRYpoQyJcUJ1ZL5AqVSopWrSouBpqH3BcEM2UMJ0RGYHnnnvO5c9LROQqDAbIrTCQB1MNN2/eLPPnz3dquh6Fe15eXuJKyACUKFFCfv/9d60NQJ2Aq5+TiMjVGAyQ21WtWlXH9/bo0UNT+4mF44QbN250ab0AshjoG1CuXDlJkyaNnhaoU6eOy56PiMidGAyQIbDffuHCBRk3blyiH2v//v0SFBTksnqBkJAQ3d7AgKEWLVpo/4DcuXO75LmIiIzAYIAMUaBAAWnXrp0MHjxYrl69muh6AW9vb50B4GyYuFimTBmZN2+edlCcNGmSzlwgIvIkDAbIMH379tX9drxNbL0ALtjOvkijpgGNg8LDw/X0Q/PmzZ36+EREZsFggAyTPn16DQSmTJkiBw4cSPBePjIDzqwXwMX/008/lYYNG0rNmjVl586dUrhwYac9PhGR2TAYIENhqyBPnjzSpUuXBB01PH78uG4zOKteAN0RX3nlFZk4caKMHz9e5s6dq/0RiIg8GYMBMhT2+lFMuGbNGlm9enW8vx5ZAXT8K1++fKLX8ssvv2g3wYsXL+rjfvzxxxwyRES2wGCADIcjeujkh+xAREREgtoBJ+buHUcT+/fvr1sCqBHAsUFMWiQisgsGA2Q43H2jEdGRI0dk8uTJ8fpa3MEnZosAo4Zr1aolAwcOlAEDBsjKlSt18iERkZ0wGCBTKFasmJ7l79evn7b7dXR//8yZMwkuHkQXRGQVkAnAFkGfPn04ZIiIbImvfGQaGGKEjoR46+gWAVSsWDFez4NCxbFjx2qhIEYro8Vw9erVE7RmIiJPwGCATAPDfjACGFX8J0+edGiL4IUXXpCMGTM6/BzoVIgjgxiUhALBDRs2SLZs2RK5ciIia2MwQKbSuXNnyZQpk04EfNqDhw/ldmiE3Lwfrm+37dgRr3oBtC1GgSC2BBYsWKB1CsmTJ3fy34CIyHo4bo1MxdfXVycBNm3aVO/8i5UpJ6dvh8hfwWESHBH5r8/9fNZSeRQeKvuu3JFcaXzF3yfmCztaCbdt21by5csnu3bt0rdERPQ3ZgbIdBo3bizVa70u26/ck1/PXJdTt0OeCQQgSdKkkjSFr34cn7fx/A0JDn/wr8+5f/++Dhh677335J133pEtW7YwECAiegozA2Q6Z4NC5aPR30pk5EP9c1x9CaM+fi0kXNaeuSZFAwM0U4C6g/r16+uRxWnTpkmrVq1cvnYiIitiMECmcuTGXTl0/R5u+yWZV/wSVwgK0NF4z5U7snf/AWn5Rg3tGYBsAI4uEhFR9LhNQKaB2gANBJzgUWB2adO9t9YHMBAgIoodgwEyBez177t6J8aPR4SHyQ+jBknr/7wsjYvmlp4Na8u+TRti7SVQoX5T8Urp56IVExF5DgYDZApI7cc2tPCrnp/KspmT5T9vvCUtew3UToGDP2wmh3dti7HFcdSWARERxY7BABkuKCxCroaEx1goePzPPbJp5c/ybqfPpEX3vlLjnabS/7v5kjFLNvlhZMzdCvF4eFw8PhERxYzBAJmiViC2QcFbflkuSZMlk+rvNH38Pm+fFFK1XmM5uneXXL98McavTfLP4xMRUcwYDJDh0FAotuODpw8fkCw5c4tvqn+PKc5b5O/CwNOHD8b4tY/+eXwiIooZgwEyVMTDh9E2FHrSrWtXJW3GTM+8P+p9t65eifXr8fhoZUxERNFjMECGCg6PPRCA8NBQ8fL2fub9yX18/v54WGicj3HPgechIrIrBgNkqIexHSH4h3eKFPIgPPyZ90eEhT2uH3DG8xAR2RWDATJU0iSxlQ7+LW3GQLl17dmtgKj3pQ3M5JTnISKyKwYDZKhU3sni/JycBV+US2dOSci9u/96//F9e/RtrhdedMrzEBHZFYMBMpRX0qTilzz2C3W5V1+Xh5GRsvanWf/qSLhu8U+Sr2hxyfBc1li/Ho+P5yEiouhxUBEZLrOfj44hjmlXP3/R4lLutTdk9pdD5c7N65I5Ry75fck8uXbxvLQbNDrWx07yz+MTEVHMGAyQ4XTccByNgT4ZPk5+HJdVNixdKMF37sjzBV6Qz775Xl4sVTbWr3v0z+MTEVHMkjzCRJc4BAUFSUBAgNy5c0f8/f3j+nSieNt4/oZci6UlcUIgK5DR11sqZk/vxEclIrIOR6/f3EglU3g5U4A4u+Afj4fHJSKi2DEYIFPw8/aSooHOvXAXCwzQxyUiotgxGCDTwN5+oQypnPJYhTKklpysFSAicghvm8hUCqZPLT7Jksm+q3cE1SzxqSFI8s/WADICDASIiBzHYIBMmSEI9PWWPVfuyNWQcL3IxxYURH0cxYKoEfDj1gARUbzwVZNMyc/bS08BBIVFyOnbITqGOLrphmgohD4CCCD8fZIbsVQiIstjMECmhgt80UwBUlRExxBj+iCGDmHWAFoMs7MgEVHiMRggy8CFP00KXvyJiJyNr6xEREQ2x2CAiIjI5hgMEBER2RyDASIiIptjMEBERGRzDAaIiIhsjsEAERGRzTEYICIisjkGA0RERDbHYICIiMjmGAwQERHZHIMBIiIim2MwQEREZHMMBoiIiGyOwQAREZHNMRggIiKyOS9HPunRo0f6NigoyNXrISIiIieJum5HXccTFQzcvXtX32bPnt0ZayMiIiI3wnU8ICAgxo8neRRXuCAiDx8+lEuXLknq1KklSZIkzl4jERERuQAu8QgEsmTJIkmTJk1cMEBERESeiwWERERENsdggIiIyOYYDBAREdkcgwEiIiKbYzBARERkcwwGiIiIbI7BABERkdjb/wOHgbl1rbd4RgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "draw_graph(graph)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d8035537",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qp = tsp_custom.to_quadratic_program()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "a11e3c85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qaoa = QAOA(sampler=Sampler(), optimizer=COBYLA(), reps=1)\n",
+    "\n",
+    "qp_solver = MinimumEigenOptimizer(qaoa)\n",
+    "qp_result = qp_solver.solve(qp)\n",
+    "\n",
+    "qc = qaoa.ansatz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34632c42",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "QAOA Time: 226.9048 seconds\n",
+      "QAOA Solution: [3, 2, 0, 1]\n",
+      " Cost: 23.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "qaoa_start = time.time()\n",
+    "obj, path = tsp_solver(tsp_custom, qp)\n",
+    "qaoa_end = time.time()\n",
+    "\n",
+    "print(f\"QAOA Time: {qaoa_end - qaoa_start:.4f} seconds\")\n",
+    "print(f\"QAOA Solution: {path}\\n Cost: {obj}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "1dc57d98",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 3, Cost: 233.69319871599944, Norm: 0.40615579259358464, Accepted: True\n",
+      "Step 6, Cost: 387.8722134435071, Norm: 0.25750379926371914, Accepted: True\n",
+      "Step 9, Cost: 21.371028175799054, Norm: 1.0138010246048812, Accepted: True\n",
+      "Step 12, Cost: 709.845351607552, Norm: 18.85753800622198, Accepted: True\n",
+      "Step 15, Cost: 41.7982874295244, Norm: 0.7821040400491325, Accepted: True\n",
+      "Step 18, Cost: -9.749997408160622, Norm: 0.2848910221102469, Accepted: True\n",
+      "Step 21, Cost: -128.08642759101963, Norm: 0.11961816211336343, Accepted: True\n",
+      "Step 24, Cost: 621.927217708531, Norm: 0.5152547384498085, Accepted: True\n",
+      "Step 27, Cost: -3.9079156974980656, Norm: 2.694389514511236, Accepted: True\n",
+      "Step 30, Cost: -1.1336349880223875, Norm: 0.15001479634639184, Accepted: True\n",
+      "Step 33, Cost: 0.012608197061227233, Norm: 0.25672104833015114, Accepted: True\n",
+      "Step 36, Cost: 78.16879371537249, Norm: 0.25953612944818466, Accepted: True\n",
+      "Step 39, Cost: -1.1251173537809747, Norm: 0.01528202206791592, Accepted: True\n",
+      "Step 42, Cost: 209.93478415128484, Norm: 0.05951022131897985, Accepted: True\n",
+      "Step 45, Cost: 0.01780299930314469, Norm: 0.26424462892362494, Accepted: True\n",
+      "Step 48, Cost: 0.04193150691601255, Norm: 0.0006186993383182129, Accepted: True\n",
+      "Step 51, Cost: 10.914912129850354, Norm: 0.005152313941098678, Accepted: True\n",
+      "Step 54, Cost: 78.11487952873289, Norm: 0.3659697263952424, Accepted: True\n",
+      "Step 57, Cost: 2.043685577963022, Norm: 0.011565368914683264, Accepted: True\n",
+      "Step 60, Cost: 256.00467445080784, Norm: 0.06291994626106623, Accepted: True\n",
+      "Step 63, Cost: 1.5367585797660501, Norm: 0.006061626326570402, Accepted: True\n",
+      "Step 66, Cost: 9.46243526002259, Norm: 0.5438017542600376, Accepted: True\n",
+      "Step 69, Cost: 30.017577951979998, Norm: 0.07532199619912813, Accepted: True\n",
+      "Step 72, Cost: 0.9512468339211178, Norm: 0.0076299659718021, Accepted: True\n",
+      "Step 75, Cost: -67.37781875343664, Norm: 0.8271106667423247, Accepted: True\n",
+      "Step 78, Cost: 8.201974702615189e-05, Norm: 0.3302037014471913, Accepted: True\n",
+      "Step 81, Cost: 7.994680464694244, Norm: 0.8844378859141903, Accepted: True\n",
+      "Step 84, Cost: 60.580101257925925, Norm: 0.23980523159498815, Accepted: True\n",
+      "Step 87, Cost: -8.376091734190998, Norm: 0.8997176642215411, Accepted: True\n",
+      "Step 90, Cost: 50.88192619520457, Norm: 1.8188608121356964, Accepted: True\n",
+      "Step 93, Cost: -16.442730442539553, Norm: 0.9312208142189425, Accepted: True\n",
+      "Step 96, Cost: -45.92398964756555, Norm: 0.402838964978178, Accepted: True\n",
+      "Step 99, Cost: -3.374138161937462, Norm: 3.258032029851555, Accepted: True\n",
+      "Step 102, Cost: 7.968320804392639, Norm: 0.011285838972892202, Accepted: True\n",
+      "Step 105, Cost: 469.3744509353956, Norm: 0.035094149742087, Accepted: True\n",
+      "Step 108, Cost: -4.864824666277115, Norm: 1.3340678215671071, Accepted: True\n",
+      "Step 111, Cost: 18.107232580662856, Norm: 0.14373946300286117, Accepted: True\n",
+      "Step 114, Cost: 57.40539937211546, Norm: 0.03140639837017743, Accepted: True\n",
+      "Step 117, Cost: 3.5802510836396806, Norm: 0.01033649098465144, Accepted: True\n",
+      "Step 120, Cost: 22.09404742940137, Norm: 0.8313679357433079, Accepted: True\n",
+      "Step 123, Cost: 14.752792772302554, Norm: 0.1196670371505251, Accepted: True\n",
+      "Step 126, Cost: -13.305531698726528, Norm: 0.8122557588425205, Accepted: True\n",
+      "Step 129, Cost: 20.407854799025056, Norm: 0.004017024768701041, Accepted: True\n",
+      "Step 132, Cost: 2.1416925925160637, Norm: 0.16848294981660036, Accepted: True\n",
+      "Step 135, Cost: 404.0815151251158, Norm: 0.21820807957130628, Accepted: True\n",
+      "Step 138, Cost: -180.8873535629944, Norm: 0.00276229782626665, Accepted: True\n",
+      "Step 141, Cost: -42.26738560327548, Norm: 0.0014430801225984486, Accepted: True\n",
+      "Step 144, Cost: 41.71835448289351, Norm: 0.002991513140393665, Accepted: True\n",
+      "Step 147, Cost: -98.67390973913116, Norm: 0.0625832864238691, Accepted: True\n",
+      "Step 150, Cost: 0.12105937291821761, Norm: 0.06031475282182599, Accepted: True\n",
+      "Step 153, Cost: 78.87404918634653, Norm: 0.1313334216591973, Accepted: True\n",
+      "Step 156, Cost: 85.86764440803188, Norm: 2.3003099704964144e-05, Accepted: True\n",
+      "Step 159, Cost: -225.00075505179942, Norm: 0.0037735412149959902, Accepted: True\n",
+      "Step 162, Cost: 2.653496399649172, Norm: 0.29160136916867363, Accepted: True\n",
+      "Step 165, Cost: -13.90778706967613, Norm: 0.06361596912414849, Accepted: True\n",
+      "Step 168, Cost: 1.6239041377979468, Norm: 0.007518078425322028, Accepted: True\n",
+      "Step 171, Cost: 237.68892892443986, Norm: 0.45061275360017694, Accepted: True\n",
+      "Step 174, Cost: -0.029659435146826882, Norm: 0.5334308458223315, Accepted: True\n",
+      "Step 177, Cost: 11.502858868739875, Norm: 0.12251439019122912, Accepted: True\n",
+      "Step 180, Cost: 22.52822075963174, Norm: 0.14354468314964694, Accepted: True\n",
+      "Step 183, Cost: 0.06192021473216766, Norm: 0.20351925029292475, Accepted: True\n",
+      "Step 186, Cost: 23.393916422670202, Norm: 0.005524680094553102, Accepted: True\n",
+      "Step 189, Cost: -4.716712623098619, Norm: 0.08566375832825432, Accepted: True\n",
+      "Step 192, Cost: 0.030588391045079044, Norm: 0.07958773527831442, Accepted: True\n",
+      "Step 195, Cost: 0.027052624850025464, Norm: 0.0003542484664312115, Accepted: True\n",
+      "Step 198, Cost: 0.028962863276306594, Norm: 0.0001123897381454877, Accepted: True\n",
+      "Step 201, Cost: 0.026284187917882063, Norm: 0.00020923805183204047, Accepted: True\n",
+      "Step 204, Cost: 0.03024361536036596, Norm: 0.00028212216495899226, Accepted: True\n",
+      "Step 207, Cost: 0.029931836413094937, Norm: 1.4453317445073766e-05, Accepted: True\n",
+      "Step 210, Cost: 0.03280775675167899, Norm: 0.00037992780736606057, Accepted: True\n",
+      "Step 213, Cost: 0.03276152963963366, Norm: 7.093677931128568e-06, Accepted: True\n",
+      "Step 216, Cost: 0.02977848139864786, Norm: 0.00023048925682596735, Accepted: True\n",
+      "Step 219, Cost: 0.032437570542099003, Norm: 0.00025221735065136255, Accepted: True\n",
+      "Step 222, Cost: -0.006036400838554945, Norm: 0.0007869981180397422, Accepted: True\n",
+      "Step 225, Cost: 0.031068941378711218, Norm: 0.0008578645855124748, Accepted: True\n",
+      "Step 228, Cost: -0.14537172040372087, Norm: 0.0015835743179828342, Accepted: True\n",
+      "Step 231, Cost: 2.6846637583479414, Norm: 0.018122741537135816, Accepted: True\n",
+      "Step 234, Cost: -0.14467148979660827, Norm: 0.09617900746044028, Accepted: True\n",
+      "Step 237, Cost: 5.115016441279459, Norm: 0.10467385180938112, Accepted: True\n",
+      "Step 240, Cost: -9.83777149158254, Norm: 0.028823110149498735, Accepted: True\n",
+      "Step 243, Cost: 9.33784124748451, Norm: 0.04593490383933028, Accepted: True\n",
+      "Step 246, Cost: 2.2455954854147513, Norm: 0.03957405398745721, Accepted: True\n",
+      "Step 249, Cost: -0.4909542029862577, Norm: 0.02005008614064561, Accepted: True\n",
+      "Step 252, Cost: 0.02193881825371194, Norm: 0.015406471094908848, Accepted: True\n",
+      "Step 255, Cost: 3.1424496993314146, Norm: 0.0038727711064189254, Accepted: True\n",
+      "Step 258, Cost: 41.76755339476825, Norm: 0.3838036528590321, Accepted: True\n",
+      "Step 261, Cost: 67.42560913027678, Norm: 0.008604053193963747, Accepted: True\n",
+      "Step 264, Cost: 0.18103556103984028, Norm: 0.28313967333802254, Accepted: True\n",
+      "Step 267, Cost: -0.5589268796163138, Norm: 0.003309751618428316, Accepted: True\n",
+      "Step 270, Cost: -0.015453531228539374, Norm: 0.003694191569451786, Accepted: True\n",
+      "Step 273, Cost: -13.61548768103511, Norm: 0.0670957133691036, Accepted: True\n",
+      "Step 276, Cost: -8.942460759520714, Norm: 0.000419270642821592, Accepted: True\n",
+      "Step 279, Cost: 5.606671699441234, Norm: 0.0007172345980425384, Accepted: True\n",
+      "Step 282, Cost: 11.495563149748135, Norm: 0.0035488815818110592, Accepted: True\n",
+      "Step 285, Cost: -6.479887317182093, Norm: 0.01566249964472185, Accepted: True\n",
+      "Step 288, Cost: -0.5300546686268985, Norm: 0.005847237756064618, Accepted: True\n",
+      "Step 291, Cost: -0.03611962685039993, Norm: 0.015020029047759157, Accepted: True\n",
+      "Step 294, Cost: -0.7610781158173345, Norm: 0.012907978133873546, Accepted: True\n",
+      "Step 297, Cost: 8.40946568789817, Norm: 0.04833086425978036, Accepted: True\n",
+      "Step 300, Cost: 21.029072473743806, Norm: 0.0004889635859917653, Accepted: True\n",
+      "QAOA QUBO Time: 1043.8153 seconds\n",
+      "QAOA Solution: [3, 2, 0, 1]\n",
+      " Cost: 23.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "qaoa_qubo_start = time.time()\n",
+    "obj_qubo, path_qubo, qaoa_cost_history = tsp_solver_qubo(tsp_custom, qp)\n",
+    "qaoa_qubo_end = time.time()\n",
+    "\n",
+    "print(f\"QAOA QUBO Time: {qaoa_qubo_end - qaoa_qubo_start:.4f} seconds\")\n",
+    "print(f\"QAOA Solution: {path_qubo}\\n Cost: {obj_qubo}\")\n",
+    "\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "plt.plot(qaoa_cost_history, label='QAOA Cost')\n",
+    "plt.xlabel(\"Iteration\")\n",
+    "plt.ylabel(\"Cost\")\n",
+    "plt.title(\"QAOA: Cost vs. Iterations\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4f66db6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simulated Annealing Time: 0.0487 seconds\n",
+      "Simulated Annealing Solution: [2, 0, 1, 3]\n",
+      " Cost: 23\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sa_start = time.time()\n",
+    "classic_path, classic_cost, sa_cost_history = simulated_annealing(custom_matrix)\n",
+    "sa_end = time.time()\n",
+    "\n",
+    "print(f\"Simulated Annealing Time: {sa_end - sa_start:.4f} seconds\")\n",
+    "print(f\"Simulated Annealing Solution: {classic_path}\\n Cost: {classic_cost}\")\n",
+    "\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "plt.plot(sa_cost_history, label='Simulated Annealing Cost')\n",
+    "plt.xlabel(\"Iteration (recorded updates)\")\n",
+    "plt.ylabel(\"Route Cost\")\n",
+    "plt.title(\"Simulated Annealing: Cost vs. Iterations\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e366b835",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial candidate from Simulated Annealing:\n",
+      "Path: [3, 2, 0, 1] Cost: 23\n",
+      "\n",
+      "Hybrid Iteration 1:\n",
+      "Quantum candidate (QAOA):\n",
+      "Path: [3, 2, 0, 1] Cost: 23.0\n",
+      "Refined candidate (Simulated Annealing):\n",
+      "Path: [3, 2, 0, 1] Cost: 23\n",
+      "\n",
+      "Hybrid Iteration 2:\n",
+      "Quantum candidate (QAOA):\n",
+      "Path: [3, 2, 0, 1] Cost: 23.0\n",
+      "Refined candidate (Simulated Annealing):\n",
+      "Path: [3, 2, 0, 1] Cost: 23\n",
+      "\n",
+      "Hybrid Iteration 3:\n",
+      "Quantum candidate (QAOA):\n",
+      "Path: [3, 2, 0, 1] Cost: 23.0\n",
+      "Refined candidate (Simulated Annealing):\n",
+      "Path: [3, 2, 0, 1] Cost: 23\n",
+      "Hybrid Solver Time: 819.0434 seconds\n",
+      "Hybrid Path: [3, 2, 0, 1]\n",
+      "Hybrid Cost: 23\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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j6Trpy+9xTklahx5T4PeDgU5i0lBfOiFIh+R1CN4PCuqV1TbQcnUSn4KDerVV8qH6Zh0eV0+mho7SGKvqAVZgUehTQFI9q09j4Koe+uWXX3bjFavHWCcqRqKeSL2n+sKhnlN9KdC20XTVOGsYKtVsKgxrmt5TDT2mgKQTnvyhx3T04LHHHgsdTdB20rK0z6jeVuuhef0hwvx26ouU1l37rgLj+YaoO9/73L59e1fmo3VQ+Y9C+D/+8Q8XxP0T8fQlUOPTahnhY+SmhkoPtE4KjnoPtB/rd1M31U/r90/t0tEX9fZqnfUlQmUmOrJwPqrn1nuqkxH1Onqu3kv9HodTL6red70f+n1UaZK2XaTxq7X/KIRrqDH9Puq9Ug+yapn1GZHaExR9qX1/gaiK6lgQQB7mDzn01VdfRXxcwwUlN2yShv/Sc//6178mecwfMkhDAb300ktelSpVvEKFCrkhwr755psE82pIKg1NFUniYb3k4MGD3j333OOVKFHCDVGl//tDYJ1v6DEN59WnTx83VJWeW6BAAa9q1apez549vS1btiSZX0ON1a9f381Xvnx5r3fv3m6Yo/O10R/iSeutdj3//PMR26PhloYPH+62sbZPqVKlvKZNm3pDhw71jh49GppPy1C7I8nI0GMaXk3rp6HE1JbEPvjgAzccld4f3TSv2rF582b3+NatW7377rvPq1WrlhuSrnTp0t61117rzZs3L8FyNm3a5LVp0yY07FtqhiHTdtbQYRo6q0iRIm75et8GDRrk7d69O8G8U6dOdUOWaRuqDXfddVeCIe8OHDjg2q32az303rdo0cJ77733Eixnz549bliv4sWLu3amZhiy873Pb7zxhlt3DZ+l9mlbPfHEEwneX/+9iTR82PmGHpMvvvjC7Td6PxMvR/t19+7dvQoVKrj9+KKLLvJ++9vfetOmTUvV54Deh3vvvdcrU6aMGxasQ4cO7v1UOxK/j2+++aYbdk3D1oUPQ5Z46DHZu3dvaLlqt97nxL+/4Z8jiYWvZ2rfXyCaYvRPdOM2gLRS75l6ztTzl/hsaQAA8H8Iu0Auo19Z1XfqUGbi8WABAEBC1OwCuYROmlKNpwKuLm2rMUYBAEDK6NkFcgmVLOjkIl09SePA6iQhAACQMsIuAAAAAotxdgEAABBYhF0AAAAEFieoRaDLKO7atcsNrh3tS4kCAAAgKVXi6sqOumBO+OWvEyPsRqCgW6VKlWg3AwAAAOfx448/WuXKlZN9nLAbgX+5RG28EiVKRLs5AAAASOTYsWOuc/J8l7km7Ebgly4o6BJ2AQAAcq7zlZxyghoAAAACi7ALAACAwCLsAgAAILCo2QUAACHx8fF29uzZaDcDsPz581tsbGyGh4El7AIAAOfEiRO2c+dON34pkBMUKVLEKlasaAULFkz3Mgi7AADA9egq6CpclC1blosqIar0heuXX36x/fv327Zt26xOnTopXjgiJYRdAADgShcUMBR0CxcuHO3mAKb9sECBAvbDDz+44BsXF5eu5XCCGgAACKFHFzlJentzEywjU1oCAAAA5ECEXQAAAAQWYRcAAGSa+HOeLdty0D5a85P7qfu5wfbt210Jx5o1a9L83CFDhliTJk1SnKdnz57WtWvXDLQQ6UXYBQAAmWL2+t121fAFdueby63flDXup+5relZJLkQuWrTIhdcjR45YVhs4cKDNnz8/Q8vw2+vfdHLWJZdcYv/85z+jFug/+OADa9u2rZUsWdKKFStmjRo1smeffdYOHTqUKe2pXr26jR492rIaYRcAAGSYAm3vSats99HTCabvOXraTc/KwBstGr3i119/dUHwwgsvzJRlbt682Xbv3m0bNmywBx980Hr37p3hIJ0ef/7zn61bt27WrFkzmzVrlq1fv95eeukl++abb2zixImWmxB2AQBAxCD38y+/pup2/PRZG/zxtxapYMGfNuTjDW6+1Cwvsy9qcfLkSStRooRNmzYtwfQZM2ZY0aJF7fjx46FpmzZtslatWrlhrho2bGiLFy9O0vuq8Ne0aVMrVKiQLVmyJEkZg8YsHjBggF1wwQUuBD/55JOpXqdy5cpZhQoVrEaNGvboo4+6n6tWrQo9fu7cORs2bJibrt7fxo0bJ1ivw4cP21133RUaQq5OnTo2btw495ieI5dddplbD/XaRvLll1/aX//6Vxdu//a3v7ntoV7YG264wfX29ujRIzTv2LFjrVatWu6iD/Xq1UsQhLXO2jZVq1Z126pSpUpunUSvrSHFHnvssVBvdlZhnF0AAJDEqbPx1uCZOZmyLMW8PcdO26VD/puq+Tc828GKFMy8iKJAe8cdd7jQd+utt4am+/eLFy9uBw8edNOeeOIJd2i9QYMG9vLLL9uNN97oLmoQ3nP71FNP2ciRI61mzZpWqlQpF4LDKSSOHz/e3nnnHbv44ovd/enTp9t1112X6jYrKM6ZM8d27NhhLVq0CE1X0J00aZK9/vrrLsh+9tlndvfdd7twe80119jTTz/teoUVyMuUKWPff/+9nTp1KhRimzdvbvPmzXMlEsldlexf//qX661++OGHIz6uEC9ap379+rntdf3119vMmTPt3nvvtcqVK9u1117rgvGoUaNsypQp7vX27Nnjeoblww8/dEH9gQcesD/84Q+WlQi7AAAgV1PIUjgLp97VcPfff7/roVSJgC4/u2/fPvv0009d8AvXt29fu+WWW0K9lrNnz7a3337b9c76VLeqXs7kKPwNGjTIfve737n7CqYKrqmhoChnzpxxvbh6rTZt2oSmqcdVbW7ZsqWbpsCt3uU33njDhV2FY/XcXnHFFe7x6tWrh5atQCwK7uo9Ts53333nlqsLOqREgV81034oVm/28uXL3XSFXbVFr6MgrGWph1dhW0qXLm358+d3XzRSaktmIOwCAIAkChfI73pYU+PLbYes57ivzjvf+HubWfMapVP12mmhYKVgGm7FihWux9OnkKXexXfffdf1zKp3tFq1aqEg6fNDpMTGxrrQuHHjxgTz+EEykqNHj7pAHd4b6y8nNaUMn3/+uQuACrbqiVX4VjBU7a56aX/++eckQVtXF1PAFc2nsK7Sh/bt27uT9xTy0yK1JRfaLuqZDde6dWt75ZVX3P9vu+02F/wVnDt27GidOnVyPeXaHtmJsAsAAJJQDWVqSwmurlPWKpaMcyejRYpJqsasUDLOzZc/X+bXZqpMoXbt2gmm7dy5M8l86t199dVXXdhVCYMOuaenVlSvl1VUV+uXCSicK7S/8MILLsSeOHHCTf/Pf/5jF110UYLnqSZWfvOb37haWPVaz50719q1a2d9+vRxva2pVbduXddbrEtIn693NyVVqlRxJ9ypJ1ptUQ+waoBVB52R5aYVJ6gBAIAMUYAdfGMD9//E0dG/r8ezIuimhXp6FQTHjBnj6lrDT7Ty6TC8TyMtrFy50tXdppaG6VKZhEJq4uWkhw71+zW3qiNWqFV5gMJ9+E3BMrxcQeum3uvRo0eHhi/za3QTl3gk9vvf/94F69deey3i4/5wbtouS5cuTfCY7qudPp0kp95cbXPVNi9btszWrVsXas/52pIZ6NkFAAAZ1rFhRRt79+U29JMNCYYfU4+ugq4ejzadTKY6Wp2EpkP8fn1sOPX86sQvBTmdXKXRDe677740vY5O2nrxxRfdcurXr+9OdEvteL+qJT59+nSojEGjG/gn1am8QWP6agQD1fNeddVVrmxCAVOjTSjgPvPMM26kCPUKaxkzZ84MhXWN9KDwqTpkrbtGnFA4T0wlGKpRfvzxx+2nn36ym2++2Y2koDIK1R/rdbWO2o633367K6FQXe4nn3ziTjzz66B1kp7CrJZXpEgRF771+iof8euJdYKdTh5UiNcJdVnCQxJHjx7VURj3EwCAvODUqVPehg0b3M+M+DX+nPfF9we8Gat3up+6n5V69OjhdenSJcn0hQsXur/lhw8fTjB9/vz5bvp7772XYPq2bdvc9MmTJ3vNmzf3ChYs6DVo0MBbsGDBeZc5ePBgr3HjxqH7Z8+e9fr16+eVKFHCu+CCC7wBAwZ43bt3j9jOxMv2b7GxsV6NGjW8gQMHeidOnAjNd+7cOW/06NFevXr1vAIFCnhly5b1OnTo4C1evNg9/txzz3kXX3yxV7hwYa906dLuNbdu3Rp6/ptvvulVqVLFy5cvn3fNNdekuG2nTp3qtWnTxitevLhXtGhRr1GjRt6zzz6bYP1fe+01r2bNmq4tdevW9SZMmBB6bPr06V6LFi3cdtDzr7zySm/evHmhx5ctW+aWWahQIbfOad0vU5vXYvRP1sTo3OvYsWPum46+LembEgAAQafeRA2xpZpR9fgFlXpK1TO6a9euZIfeQu7YL1Ob1yhjAAAAgadRDDRKgsoLdGUygm7ewQlqAAAg8EaMGOHqZzWmq8bARd5B2AUAAIGny9ZqKK358+cnuQAFgi2qYVeXvGvWrJk7u1BnCGrgY43HFolKizV2nMbD07WsU6J5dTaihv7QWX86Q1BXAwEAAEDeEtWwq0GFNdCxxrTTYMP6xqWhQE6ePJlkXo0Tl9qBn3WoQuO5aXgMjXOnwZ87dOjgipwBAEDyOG8dQdsfo3qCmsZ5C6fx2NTDq4GXwy/ft2bNGnvppZfs66+/dr2159soCsZ/+ctfrEuXLm7ahAkTrHz58q5HWGO5AQCApBcv8C89q6OiQE45sVAycsW1HDUag4aOEF0DOnwldSUPDfKsovLz0fAUe/bscaULPg1LoQGNddWOSGFXgy7rFj6UBQAAeUlsbKwb+H///v0uWOTLx2k9iB51XioD6iIbunyy/2UsV4ddXQmkf//+1rp1a2vYsGFousbCa9WqVaiX9nwUdEU9ueF0338sUu3w0KFDM9R+AAByM5UK6uipOo10SV0gJ1DQTU1nZ64Iu6rdXb9+vS1ZsiQ07eOPP7YFCxbY6tWrs/S1NQTJgAEDEvTshl9jGgCAvEBjz+oStyplAKJNRxgy0qObo8Ju37593bWbdX3k8OtUK+hu2bLFpfpwt9xyi1199dW2aNGiJMvy0//evXsT1PfqfpMmTSK+vq7HrBsAAHmdyheCfAU15D35ol2PoaA7ffp0F2x1KbhwTz31lK1du9adoObfZNSoUTZu3LiIy9QyFHg1jl54T61GZWjZsmUWrxEAAAByktholy5MnjzZPvroIzfWrl9TqxPKdCaoQmukOo2qVasmCMa6Iorqbm+++WZXc6Ta3+eff94ditF8Tz/9tFWqVMmN4wsAAIC8I6phd+zYse5n27ZtE0xXr23Pnj1TvRxdiMIfyUGefPJJN1bvAw88YEeOHLGrrrrKDXPGYRkAAIC8JcZj9OgkVPag3mUF6BIlSkS7OQAAAEhnXmMQPQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBRdgFAABAYBF2AQAAEFiEXQAAAAQWYRcAAACBFdWwO2zYMGvWrJkVL17cypUrZ127drXNmzcnmOfBBx+0WrVqWeHCha1s2bLWpUsX27RpU4rL7dmzp8XExCS4dezYMYvXBgAAADlNVMPu4sWLrU+fPrZ8+XKbO3eunT171tq3b28nT54MzdO0aVMbN26cbdy40ebMmWOe57l54uPjU1y2wu3u3btDt3//+9/ZsEYAAADISWI8pcccYv/+/a6HVyG4TZs2EedZu3atNW7c2L7//nvX45tcz+6RI0dsxowZ6WrHsWPHrGTJknb06FErUaJEupYBAACArJPavJajanbVWCldunTEx9Xjq17eGjVqWJUqVVJc1qJFi1xwrlevnvXu3dsOHjyY7LxnzpxxGyz8BgAAgNwvx4Tdc+fOWf/+/a1169bWsGHDBI+99tprVqxYMXebNWuWK3koWLBgiiUMEyZMsPnz59vw4cNdT/FvfvObZEsfVDusbwb+7XxBGgAAALlDjiljUO+rguySJUuscuXKSXp89+3b52pvR44caT/99JMtXbrU4uLiUrXsrVu3upKHefPmWbt27SL27OrmU8+uAi9lDAAAADlTripj6Nu3r82cOdMWLlyYJOiKVqROnTqujnfatGluNIbp06enevk1a9a0MmXKuDrfSAoVKuQ2UvgNAAAAuV9sNF9cncqPPPKIC66qsVUtbmqeo1t4T+z57Ny509XsVqxYMYMtBgAAQG4S1Z5dDTs2adIkmzx5shtrd8+ePe526tSpUPmB6mlXrlxpO3bssC+++MJuu+02N+Zup06dQsupX79+qKf3xIkT9sQTT7jhzLZv3+7qdjU2b+3ata1Dhw5RW1cAAADksbA7duxYV2fRtm1b1+vq36ZOneoeV03u559/7oKtwmq3bt1cKFbo1UgLPl2Iwh/JIX/+/G54sptuusnq1q1rvXr1cmP1ajkqVwAAAEDekWNOUMtJGGcXAAAgZ8tVJ6gBAAAAWYGwCwAAgMAi7AIAACCwCLsAAAAILMIuAAAAAouwCwAAgMAi7AIAACCwCLsAAAAILMIuAAAAAouwCwAAgMAi7AIAACCwCLsAAAAILMIuAAAAAouwCwAAgMAi7AIAACCwCLsAAAAILMIuAAAAAivNYXfHjh3meV6S6ZqmxwAAAIBcG3Zr1Khh+/fvTzL90KFD7jEAAAAg14Zd9eDGxMQkmX7ixAmLi4vLrHYBAAAAGRab2hkHDBjgfiroPv3001akSJHQY/Hx8bZixQpr0qRJxlsEAAAAZHfYXb16dahnd926dVawYMHQY/p/48aNbeDAgZnVLgAAACD7wu7ChQvdz3vvvddeeeUVK1GiRMZfHQAAAMhJNbvjxo1LEHSPHTtmM2bMsE2bNmV22wAAAIDsDbu33367/eMf/3D/P3XqlF1xxRVu2qWXXmoffPBBxloDAAAARDPsfvbZZ3b11Ve7/0+fPt3V8B45csTGjBljzz//fGa2DQAAAMjesHv06FErXbq0+//s2bPtlltucSMzdO7c2b777ruMtQYAAACIZtitUqWKLVu2zE6ePOnCbvv27d30w4cPM84uAAAAcudoDL7+/fvbXXfdZcWKFbNq1apZ27ZtQ+UNqtsFAAAAcm3Yffjhh6158+b2448/2g033GD58v3/zuGaNWtSswsAAIAcJcbTGWbp5D810uWDczMNp1ayZElXn8x4wgAAALk3r6W5ZlcmTJjgShYKFy7sbo0aNbKJEydmpL0AAABA9MsYXn75ZXv66aetb9++1rp1azdtyZIl9tBDD9mBAwfssccey4p2AgAAAFlfxlCjRg0bOnSode/ePcH0d99914YMGWLbtm2z3I4yBgAAgDxaxrB7925r1apVkumapscAAACAnCLNYbd27dr23nvvJZk+depUq1OnTma1CwAAAMj+ml2VMHTr1s2Nq+vX7C5dutTmz58fMQQDAAAAuaZnV5cHXrFihZUpU8ZmzJjhbvr/l19+aTfffHPWtBIAAADI7nF2g4oT1AAAAPLYCWq7du2ygQMHugUnphd54oknbO/evelvMQAAAJDJ8qVlfF0F3UjJWan6+PHjbh4AAAAg14Xd2bNnJxlbN5wemzlzZma1CwAAAMi+sKuLRVStWjXZxytXrmzbt2/PeIsAAACA7A67hQsXTjHM6jHNAwAAAOS6sNuiRQubOHFiso9PmDDBmjdvnlntAgAAALLvohIaieGGG25wJ6Np5IXy5cu76RqBYcSIETZ+/Hj773//m/EWAQAAANEYZ/eNN96wfv362dmzZ92oDDExMW7YsQIFCtioUaOsd+/eFgSMswsAABCMvJbmi0r89NNP7rLA33//vempdevWtVtvvdWdoBYUhF0AAIA8GnbzAsIuAABAMPJaqmt2kTXiz3n25bZDtu/4aStXPM6a1yht+fPFRLtZQJZgf0dewv6OvCQ+B+/vUQ27w4YNsw8//NA2bdrkhi1r1aqVDR8+3OrVqxea58EHH7R58+a5yxUXK1YsNE/9+vWTXa46qwcPHmxvvvmmHTlyxFq3bm1jx461OnXqWE4ye/1uG/rJBtt99HRoWsWScTb4xgbWsWHFqLYNyGzs78hL2N+Rl8zO4ft7qoceywqLFy+2Pn362PLly23u3LnuxLf27dvbyZMnQ/M0bdrUxo0bZxs3brQ5c+a4IKt54uPjk12uRocYM2aMvf7667ZixQorWrSodejQwU6f/r83ISfsGL0nrUqwY8ieo6fddD0OBAX7O/IS9nfkJbNzwf6eo2p29+/fb+XKlXMhuE2bNhHnWbt2rTVu3NidIFerVq0kj2t1KlWqZI8//rgbLk1Uy6Gh0jQ82h133BH1ml119V81fEGSHcOnTv/yJeJs7oA2OeYQAJCR/f36lxfb3mNnIj7O/o4gYX9HXhKfiv29Qsk4W/LH67Jkf8/Sml2VBkybNs22bNnixtwtXbq0rVq1ygXKiy66KN2NVmNFy4tEPb7q5a1Ro4ZVqVIl2csa79mzx66//vrQNG0IXRRj2bJlEcPumTNn3C1842Ul1bQkF3RF3z72HDttlw5h3GIEH/s78hL2d+S1/X330dMu97SsdWHuKWNQz6qGG1Pd7MiRI13wFdXeDho0KN0NOXfunPXv39/V1zZs2DDBY6+99pqr19Vt1qxZruShYMGCEZejoCv+RS98uu8/Fql2WIHYvyUXpDOLircBAADygn1Rzj1p7tkdMGCA9ezZ09XFFi9ePDS9U6dO9vvf/z7dDVHt7vr1623JkiVJHrvrrrvc1dt2797tAvbtt99uS5cutbi4OMsMCular/Ce3awMvDpLMTXG39vMnc0I5Gb6Rt9z3FfnnY/9HUHA/o685MtU7u+pzT05Jux+9dVX7kpqial8Ibme0/Pp27evzZw50z777LOIF6fwe1w1msKVV15ppUqVsunTp9udd96ZZN4KFSqELmNcseL/nQGo+02aNIn4+oUKFXK37KIPOJ2lqOJtL4Ual6vrlKWmC7me9mP2d+QV7O/IS65O5f4e7S92aS5jUCiMVNP6v//9z8qWLZumZelkMgVdBdcFCxa4WtzUPEe38BrbcFqGAu/8+fND09RejcrQsmVLywn0AafhOCTxR51/X4/zQYggYH9HXsL+jrwkfy7Z39Mcdm+66SZ79tln3TBhEhMTYzt27LA//vGPdsstt6S5dGHSpEk2efJkVxKhnmHdTp065R7funWrq6dduXKle40vvvjCbrvtNjcmr8omfBpzV4HZb49qf59//nn7+OOPbd26dda9e3c3QkPXrl0tp9C4c2Pvvtx94wmn+5qeE8alAzIL+zvyEvZ35CUdc8H+nuahxzRiwq233mpff/21HT9+3IVIBVT1mn766aduTNtUv3hM5KSvERdUF6wLSdx///0u7B4+fNidZKYhyZ555pkEF57QcvznhF9U4p///Kc7ge6qq65yJ7npxLqcdrngnHzFESCzsb8jL2F/R14SH4X9PbV5Ld3j7OoEsW+++cZOnDhhl19+eYKhvnK77Ay7AAAAyEHj7E6YMMG6devmhgjTzffLL7/YlClTXMkAAAAAkBOkuWc3f/78bggwXeks3MGDB920lC7jm1vQswsAABCMvJbmE9SUjSPV2u7cudO9IAAAAJBTpLqM4bLLLnMhV7d27dpZbOz/PVW9ubpMb8eOHbOqnQAAAEDWhV1/2K41a9ZYhw4d3KV7fbp0b/Xq1dM89BgAAACQI8KuhvIShVqdoJZZl+oFAAAAskqaR2Po0aNH1rQEAAAAiHbYzZcvX7IXg5AgjMYAAACAPBp2P/zwwwRhV5cNXr16tb377rs2dOjQzG4fAAAAkG7pvoJaYpMnT7apU6faRx99ZLkd4+wCAADk0XF2k3PllVfa/PnzM2txAAAAQIZlStg9deqUjRkzxi666KLMWBwAAAAQnZrdUqVKJajZVRXE8ePHrUiRIjZp0qTMaRUAAAAQjbA7evToJKMzlC1b1lq0aOGCMAAAAJBTMM4uAAAAAivNYVeOHDlib7/9tm3cuNHdv+SSS+y+++5zZ8QBAAAAufYEta+//tpq1aplo0aNskOHDrnbyy+/7KatWrUqa1oJAAAAZMc4u1dffbXVrl3b3nzzTYuN/f8dw7/++qvdf//9tnXrVvvss88st2OcXQAAgGDktTSH3cKFC7srptWvXz/B9A0bNtgVV1xhP//8s+V2hF0AAIA8elEJLWzHjh1Jpv/4449WvHjxtLcUAAAAyCJpDrvdunWzXr16uUsDK+DqNmXKFFfGcOedd2ZNKwEAAIDsGI1h5MiR7qIS3bt3d7W6UqBAAevdu7e9+OKL6WkDAAAAkCXSXLPrU23uli1b3P81EoOuoKbLBqumN7ejZhcAACCP1uz6FG4vvfRSd8ufP78bfqxGjRrpXRwAAACQ6VIdds+cOWODBg1yIy60atXKZsyY4aaPGzfOhVyNu/vYY49lfgsBAACArK7ZfeaZZ+yNN96w66+/3r744gu77bbb7N5777Xly5e7Xl3dVw8vAAAAkOvC7vvvv28TJkywm266ydavX2+NGjVyJ6h988037oQ1AAAAINeWMezcudOaNm3q/t+wYUMrVKiQK1sg6AIAACDXh934+HgrWLBg6L4uFVysWLGsahcAAACQfWUMGqGsZ8+erkdXTp8+bQ899JAVLVo0wXwffvhhxlsFAAAAZGfY7dGjR4L7d999d2a8PgAAABD9sKshxgAAAIDcJN0XlQAAAAByOsIuAAAAAouwCwAAgMAi7AIAACCwCLsAAAAILMIuAAAAAouwCwAAgMAi7AIAACCwCLsAAAAILMIuAAAAAouwCwAAgMAi7AIAACCwCLsAAAAILMIuAAAAAouwCwAAgMAi7AIAACCwCLsAAAAILMIuAAAAAiuqYXfYsGHWrFkzK168uJUrV866du1qmzdvDj1+6NAhe+SRR6xevXpWuHBhq1q1qj366KN29OjRFJfbs2dPi4mJSXDr2LFjNqwRAAAAcpKoht3Fixdbnz59bPny5TZ37lw7e/astW/f3k6ePOke37Vrl7uNHDnS1q9fb+PHj7fZs2dbr169zrtshdvdu3eHbv/+97+zYY0AAACQk8R4nudZDrF//37Xw6sQ3KZNm4jzvP/++3b33Xe7QBwbG5tsz+6RI0dsxowZ6WrHsWPHrGTJkq4HuUSJEulaBgAAALJOavNajqrZ9csTSpcuneI8WqHkgq5v0aJFLjirBKJ379528ODBZOc9c+aM22DhNwAAAOR+OaZn99y5c3bTTTe5HtklS5ZEnOfAgQPWtGlT17P7wgsvJLusKVOmWJEiRaxGjRq2ZcsW+9Of/mTFihWzZcuWWf78+ZPMP2TIEBs6dGiS6fTsAgAA5O6e3RwTdtX7OmvWLBd0K1euHHGFbrjhBtfr+/HHH1uBAgVSveytW7darVq1bN68edauXbuIPbu6hb9WlSpVCLsAAAA5VK4qY+jbt6/NnDnTFi5cGDHoHj9+3J1wplEbpk+fnqagKzVr1rQyZcrY999/H/HxQoUKuY0UfgMAAEDuF9Wwq05lBV0F2AULFriyg0ipXSM0FCxY0PXoxsXFpfl1du7c6Wp2K1asmEktBwAAQG4Q1bCrYccmTZpkkydPdr22e/bscbdTp04lCLoaeeHtt9929/154uPjQ8upX7++C8xy4sQJe+KJJ9xwZtu3b7f58+dbly5drHbt2tahQ4eorSsAAACyX8pDGmSxsWPHup9t27ZNMH3cuHFu+LBVq1bZihUr3DSF1XDbtm2z6tWru//rQhT+SA46AW3t2rX27rvvupPdKlWq5ALzc88958oVAAAAkHfkmBPUchLG2QUAAMjZctUJagAAAEBWIOwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsAi7AAAACCzCLgAAAAKLsAsAAIDAIuwCAAAgsKIadocNG2bNmjWz4sWLW7ly5axr1662efPm0OOHDh2yRx55xOrVq2eFCxe2qlWr2qOPPmpHjx5Ncbme59kzzzxjFStWdM+7/vrr7bvvvsuGNQIAAEBOEtWwu3jxYuvTp48tX77c5s6da2fPnrX27dvbyZMn3eO7du1yt5EjR9r69ett/PjxNnv2bOvVq1eKyx0xYoSNGTPGXn/9dVuxYoUVLVrUOnToYKdPn86mNQMAAEBOEOOpGzSH2L9/v+vhVQhu06ZNxHnef/99u/vuu10gjo2NTfK4VqdSpUr2+OOP28CBA9009QSXL1/eheU77rgjyXPOnDnjbr5jx45ZlSpV3PNKlCiRqesIAACAjFNeK1my5HnzWo6q2fXLE0qXLp3iPFqhSEFXtm3bZnv27HGlCz5tiBYtWtiyZcuSLafQPP5NQRcAAAC5X44Ju+fOnbP+/ftb69atrWHDhhHnOXDggD333HP2wAMPJLscBV1RT2443fcfS2zQoEEuRPu3H3/8MUPrAgAAgJwhcvdoFKh2V3W5S5YsSbarunPnztagQQMbMmRIpr52oUKF3A0AAADBkiN6dvv27WszZ860hQsXWuXKlZM8fvz4cevYsaMbtWH69OlWoECBZJdVoUIF93Pv3r0Jpuu+/xgAAADyhqiGXZ1MpqCrALtgwQKrUaNGxB5djdBQsGBB+/jjjy0uLi7FZWoZCrXz589PsAyNytCyZcssWQ8AAADkTPmiXbowadIkmzx5suu1VU2tbqdOnUoQdDXywttvv+3u+/PEx8eHllO/fn0XmCUmJsbV/j7//PMuHK9bt866d+/uRmjQOL4AAADIO6Jaszt27Fj3s23btgmmjxs3znr27GmrVq1yPbJSu3btJKMuVK9e3f1fF6IIv9DEk08+6QKyTmQ7cuSIXXXVVW583vP1CgMAACBYctQ4u7lt3DYAAABER64cZxcAAADITIRdAAAABBZhFwAAAIFF2AUAAEBgEXYBAAAQWIRdAAAABBZhFwAAAIFF2AUAAEBgEXYBAAAQWIRdAAAABBZhFwAAAIFF2AUAAEBgEXYBAAAQWIRdAAAABBZhFwAAAIFF2AUAAEBgEXYBAAAQWIRdAAAABBZhFwAAAIFF2AUAAEBgEXYBAAAQWIRdAAAABBZhFwAAAIFF2AUAAEBgxUa7ATmR53nu57Fjx6LdFAAAAETg5zQ/tyWHsBvB8ePH3c8qVapEuykAAAA4T24rWbJkso/HeOeLw3nQuXPnbNeuXVa8eHGLiYnJlm8mCtY//vijlShRIstfD/8f2z062O7RwXaPDrZ7dLDd88Z29zzPBd1KlSpZvnzJV+bSsxuBNljlypWz/XW1Y/BLmf3Y7tHBdo8Otnt0sN2jg+0eHdm53VPq0fVxghoAAAACi7ALAACAwCLs5gCFChWywYMHu5/IPmz36GC7RwfbPTrY7tHBdo+OQjl0u3OCGgAAAAKLnl0AAAAEFmEXAAAAgUXYBQAAQGARdgEAABBYhN1s8uqrr1r16tUtLi7OWrRoYV9++WWK87///vtWv359N/+ll15qn376aba1Na9u9/Hjx7sr5oXf9DykzWeffWY33niju6KNtuGMGTPO+5xFixbZ5Zdf7s7grV27tnsvkHXbXNs78b6u2549e7KtzUEwbNgwa9asmbvaZrly5axr1662efPm8z6Pz/fs3+58vmfc2LFjrVGjRqELRrRs2dJmzZqVK/Z1wm42mDp1qg0YMMANx7Fq1Spr3LixdejQwfbt2xdx/i+++MLuvPNO69Wrl61evdr9Iuu2fv36bG97Xtruol/g3bt3h24//PBDtrY5CE6ePOm2tb5opMa2bdusc+fOdu2119qaNWusf//+dv/999ucOXOyvK15dZv7FBDC93cFB6Te4sWLrU+fPrZ8+XKbO3eunT171tq3b+/ej+Tw+R6d7S58vmeMriz74osv2sqVK+3rr7+26667zrp06WLffvttzt/XNfQYslbz5s29Pn36hO7Hx8d7lSpV8oYNGxZx/ttvv93r3LlzgmktWrTwHnzwwSxva17e7uPGjfNKliyZjS0MPn3ETJ8+PcV5nnzySe+SSy5JMK1bt25ehw4dsrh1eXebL1y40M13+PDhbGtXXrBv3z63XRcvXpzsPHy+R2e78/meNUqVKuW99dZbOX5fp2c3i/3yyy/uW9D1118fmpYvXz53f9myZRGfo+nh84t6JJObH5mz3eXEiRNWrVo1q1KlSorfWJF52N+jp0mTJlaxYkW74YYbbOnSpdFuTq539OhR97N06dLJzsP+Hp3tLny+Z574+HibMmWK601XOUNO39cJu1nswIEDbqcoX758gum6n1x9nKanZX5kznavV6+evfPOO/bRRx/ZpEmT7Ny5c9aqVSvbuXNnNrU6b0pufz927JidOnUqau0KMgXc119/3T744AN30x//tm3bunIfpI8+L1SC07p1a2vYsGGy8/H5Hp3tzud75li3bp0VK1bMnV/x0EMP2fTp061BgwY5fl+PzfZXBHIofTsN/4aqD8KLL77Y3njjDXvuueei2jYgM+kPv27h+/qWLVts1KhRNnHixKi2LbdSDalqEZcsWRLtpuQpqd3ufL5nDn1u6NwK9aZPmzbNevTo4Wqokwu8OQU9u1msTJkylj9/ftu7d2+C6bpfoUKFiM/R9LTMj8zZ7okVKFDALrvsMvv++++zqJVIaX/XySSFCxeOWrvymubNm7Ovp1Pfvn1t5syZtnDhQncST0r4fI/Odk+Mz/f0KViwoBsxp2nTpm5UDJ0Y+8orr+T4fZ2wmw07hnaK+fPnh6bp8InuJ1fnounh84vOOE1ufmTOdk9MZRA6ZKNDvsg67O85g3pr2NfTRucDKnDpUO6CBQusRo0a530O+3t0tntifL5nDv1dPXPmTM7f17P9lLg8aMqUKV6hQoW88ePHexs2bPAeeOAB74ILLvD27NnjHr/nnnu8p556KjT/0qVLvdjYWG/kyJHexo0bvcGDB3sFChTw1q1bF8W1CP52Hzp0qDdnzhxvy5Yt3sqVK7077rjDi4uL87799tsorkXuc/z4cW/16tXupo+Yl19+2f3/hx9+cI9rm2vb+7Zu3eoVKVLEe+KJJ9z+/uqrr3r58+f3Zs+eHcW1CPY2HzVqlDdjxgzvu+++c58r/fr18/Lly+fNmzcvimuR+/Tu3dud4b9o0SJv9+7dodvPP/8cmofP95yx3fl8zzhtT414sW3bNm/t2rXufkxMjPff//43x+/rhN1s8ve//92rWrWqV7BgQTck1vLly0OPXXPNNV6PHj0SzP/ee+95devWdfNrWKb//Oc/UWh13tru/fv3D81bvnx5r1OnTt6qVaui1PLcyx/WKvHN39b6qW2f+DlNmjRx275mzZpumCBk3TYfPny4V6tWLffHvnTp0l7btm29BQsWRHENcqdI21y38P2Xz/ecsd35fM+4++67z6tWrZrbhmXLlvXatWsXCro5fV+P0T/Z358MAAAAZD1qdgEAABBYhF0AAAAEFmEXAAAAgUXYBQAAQGARdgEAABBYhF0AAAAEFmEXAAAAgUXYBQAAQGARdgEgi2zfvt1iYmJszZo1aX7ukCFDrEmTJinO07NnT+vatavlNqlZNwDILIRdAEhliFy0aJELr0eOHMnyNgwcONDmz5+foWUkbu/48ePtggsusOyk158xY0amrxsApFZsqucEAGQ5XcE9Pj7eihUr5m45kdqnEJsvX/r6S3LyugEIHnp2ASAdTp48aSVKlLBp06YlmK5ezKJFi9rx48dD0zZt2mStWrWyuLg4a9iwoS1evDhJ7+usWbOsadOmVqhQIVuyZEmSQ/0KmAMGDHA9sxdeeKE9+eSTLhinll7n3nvvtaNHj7rX002vIWfOnHG9rRdddJFre4sWLdz8Pr9H+OOPP7YGDRq4Nu7YscO++uoru+GGG6xMmTJWsmRJu+aaa2zVqlWh51WvXt39vPnmm93r+fcTr9u5c+fs2WeftcqVK7tl67HZs2cnKQf58MMP7dprr7UiRYpY48aNbdmyZalefwB5F2EXANJBofCOO+6wcePGJZiu+7feeqsVL148NO2JJ56wxx9/3FavXm0tW7a0G2+80Q4ePJjgeU899ZS9+OKLtnHjRmvUqFGS13vppZdc6HznnXdcGD506JBNnz491e1V2B49erQL6Lt373Y3BVzp27evC45TpkyxtWvX2m233WYdO3a07777LvT8n3/+2YYPH25vvfWWffvtt1auXDkX6Hv06OHas3z5cqtTp4516tQpFPQVhv1totfz7yf2yiuvuPUbOXKke/0OHTrYTTfdlOD15c9//rNrs2qg69ata3feeaf9+uuvqd4GAPIoDwCQQI8ePbz8+fN7RYsWTXCLi4tTV6p3+PBhN9+KFSvcfLt27XL39+7d68XGxnqLFi1y97dt2+bmf/HFF0PLPnv2rFe5cmVv+PDh7v7ChQvdPDNmzEjQhsGDB3uNGzcO3a9YsaI3YsSIJMvp0qVLsuvhL9tv77hx47ySJUsmmOeHH35w6/DTTz8lmN6uXTtv0KBBoedpOWvWrElxu8XHx3vFixf3Pvnkk9A0PW/69OkprlulSpW8F154IcE8zZo18x5++OEE2/Gtt94KPf7tt9+6aRs3bkyxTQBAzy4ARKDD5epBDL+pVzNc8+bN7ZJLLrF3333X3Z80aZJVq1bN2rRpk2A+9eb6YmNj7YorrnA9uOE0LTkqPVDPqMoLEi8no9atW+dKJNRT6tfS6qZSiy1btoTmK1iwYJIe571799of/vAH16OrMgb1Gp84ccKVOKTWsWPHbNeuXda6desE03U/8TYKf/2KFSu6n/v27UvzOgPIWzhBDQCSKVOoXbt2gmk7d+5MMt/9999vr776qitD0OF61cWqvjQ9rxcNCqf58+e3lStXup/hwk8iK1y4cJL1UgmDyjFUhqCQr3pbBftffvklS9paoECB0P/9tqjeFwBSQs8uAGTA3XffbT/88IONGTPGNmzY4AJgYqpn9anGVMHy4osvTvVrqNdUPZkrVqxIspy0UO+senHDXXbZZW6aekgV7sNvFSpUSHF5S5cutUcffdTV6aqHW2H3wIEDSQJq4tcMp97gSpUquWUlXrZOhgOAjKJnFwAyoFSpUva73/3OnYTWvn17N6JAYur51aF+BdxRo0bZ4cOH7b777kvT6/Tr18+dwKbl1K9f315++eU0j/er0RDUk6sxbjWagUY1UPnCXXfdZd27d3cniSn87t+/382jsoHOnTsnuzy1ZeLEia6cQuUI2gbqAU78mlqWyhIUhrW9EtPzBg8ebLVq1XIjMaiHXGUj//rXv9K0fgAQCT27AJBBvXr1cofukwuwCqm6KWBq5AIN4aXhutJCozncc889rudYpQIa7UFDeqWFRmR46KGHrFu3bla2bFkbMWKEm65wqbCr16hXr567oIZGTqhatWqKy3v77bddcL/88std29TLq1EawilAz50716pUqeKCdCR6noZV0+tfeumlbtgxbSOFaQDIqBidpZbhpQBAHqbezccee8ydaKVSAQBAzkEZAwCkk8ae1SgJ6rV98MEHCboAkANRxgAA6aQyANXP6kSuQYMGRbs5AIAIKGMAAABAYNGzCwAAgMAi7AIAACCwCLsAAAAILMIuAAAAAouwCwAAgMAi7AIAACCwCLsAAAAILMIuAAAALKj+H897etXgW8w7AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start = time.time()\n",
+    "hybrid_path, hybrid_cost, hybrid_history = hybrid_solver(tsp_custom, qp, custom_matrix, iterations=3)\n",
+    "end = time.time()\n",
+    "\n",
+    "print(\"--------------------------------------------------------\")\n",
+    "print(f\"Hybrid Solver Time: {end - start:.4f} seconds\")\n",
+    "print(\"Hybrid Path:\", hybrid_path)\n",
+    "print(\"Hybrid Cost:\", hybrid_cost)\n",
+    "\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "plt.plot(hybrid_history, marker='o', label='Hybrid Best Cost')\n",
+    "plt.xlabel(\"Hybrid Iteration\")\n",
+    "plt.ylabel(\"Route Cost\")\n",
+    "plt.title(\"Hybrid Solver: Best Cost vs. Iterations\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0be4ccee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Visualizing Quantum Route:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Visualizing Classical Route:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Visualizing Hybrid Route:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"\\nVisualizing Quantum Route:\")\n",
+    "draw_graph(graph, path=path)\n",
+    "\n",
+    "print(\"\\nVisualizing Classical Route:\")\n",
+    "draw_graph(graph, path=classic_path)\n",
+    "\n",
+    "print(\"\\nVisualizing Hybrid Route:\")\n",
+    "draw_graph(graph, path=hybrid_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "229fa45c",
+   "metadata": {},
+   "source": [
+    "## For 4 complex Nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "cd3c269a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_cities = 4\n",
+    "random_seed = 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "0a42f244",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adjacency Matrix:\n",
+      " [[ 0. 77. 72. 93.]\n",
+      " [77.  0. 17. 49.]\n",
+      " [72. 17.  0. 34.]\n",
+      " [93. 49. 34.  0.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tsp = Tsp.create_random_instance(n_cities, seed=random_seed)\n",
+    "tsp_graph = tsp.graph\n",
+    "matrix = nx.to_numpy_array(tsp_graph)\n",
+    "print(\"Adjacency Matrix:\\n\", matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "99c72081",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "draw_graph(tsp_graph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "cb2ec2af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tsp_quadratic_program = tsp.to_quadratic_program()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "95be3d7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "QAOA Solution for 4 cities: [3, 2, 1, 0]\n",
+      " Cost: 221.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "obj_4, path_4 = tsp_solver(tsp, tsp_quadratic_program)\n",
+    "print(f\"QAOA Solution for 4 cities: {path_4}\\n Cost: {obj_4}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "7922c11f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classical (Simulated Annealing) Solution: [0, 1, 2, 3]\n",
+      "Classical Cost: 221.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "classic_path_, classic_cost_ = simulated_annealing(matrix)\n",
+    "\n",
+    "print(\"Classical (Simulated Annealing) Solution:\", classic_path_)\n",
+    "print(\"Classical Cost:\", classic_cost_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "6fd4237c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Visualizing Quantum Route:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Visualizing Classical Route:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"\\nVisualizing Quantum Route:\")\n",
+    "draw_graph(tsp_graph, path=path_4)\n",
+    "\n",
+    "print(\"\\nVisualizing Classical Route:\")\n",
+    "draw_graph(tsp_graph, path=classic_path_)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d822cdb3",
+   "metadata": {},
+   "source": [
+    "## For another 4 nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "7781eb47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matrix_ = np.array([[ 0, 48, 91, 33],\n",
+    " [48. , 0. ,63. ,71.],\n",
+    " [91. ,63. , 0., 92.],\n",
+    " [33., 71., 92.,  0.]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "7d30b1e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_nodes_ = matrix_.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "51b83224",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_custom_ = nx.complete_graph(n_nodes_)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "256f74df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i, j in G_custom_.edges():\n",
+    "    G_custom_[i][j]['weight'] = matrix_[i][j]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "19879fb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tsp_custom_ = Tsp(G_custom_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "f022f493",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph_ = tsp_custom_.graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "91343f9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "draw_graph(graph_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "02d09f88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qp_ = tsp_custom_.to_quadratic_program()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "32ab7603",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "QAOA Solution: [3, 2, 1, 0]\n",
+      " Cost: 236.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "obj_1, path_1 = tsp_solver(tsp_custom_, qp_)\n",
+    "print(f\"QAOA Solution: {path_1}\\n Cost: {obj_1}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "6594d285",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classical (Simulated Annealing) Solution: [0, 1, 2, 3]\n",
+      "Classical Cost: 236.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "classic_path_1, classic_cost_1 = simulated_annealing(matrix_)\n",
+    "\n",
+    "print(\"Classical (Simulated Annealing) Solution:\", classic_path_1)\n",
+    "print(\"Classical Cost:\", classic_cost_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "bda4a3c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Visualizing Quantum Route:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Visualizing Classical Route:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"\\nVisualizing Quantum Route:\")\n",
+    "draw_graph(graph_, path=path_1)\n",
+    "\n",
+    "print(\"\\nVisualizing Classical Route:\")\n",
+    "draw_graph(graph_, path=classic_path_1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8487b7f1",
+   "metadata": {},
+   "source": [
+    "## QUBO Optimisation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "96152f38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Problem name: TSP\n",
+      "\n",
+      "Minimize\n",
+      "  2*x_0_0*x_1_1 + 2*x_0_0*x_1_3 + 9*x_0_0*x_2_1 + 9*x_0_0*x_2_3 + 10*x_0_0*x_3_1\n",
+      "  + 10*x_0_0*x_3_3 + 2*x_0_1*x_1_0 + 2*x_0_1*x_1_2 + 9*x_0_1*x_2_0\n",
+      "  + 9*x_0_1*x_2_2 + 10*x_0_1*x_3_0 + 10*x_0_1*x_3_2 + 2*x_0_2*x_1_1\n",
+      "  + 2*x_0_2*x_1_3 + 9*x_0_2*x_2_1 + 9*x_0_2*x_2_3 + 10*x_0_2*x_3_1\n",
+      "  + 10*x_0_2*x_3_3 + 2*x_0_3*x_1_0 + 2*x_0_3*x_1_2 + 9*x_0_3*x_2_0\n",
+      "  + 9*x_0_3*x_2_2 + 10*x_0_3*x_3_0 + 10*x_0_3*x_3_2 + 6*x_1_0*x_2_1\n",
+      "  + 6*x_1_0*x_2_3 + 4*x_1_0*x_3_1 + 4*x_1_0*x_3_3 + 6*x_1_1*x_2_0\n",
+      "  + 6*x_1_1*x_2_2 + 4*x_1_1*x_3_0 + 4*x_1_1*x_3_2 + 6*x_1_2*x_2_1\n",
+      "  + 6*x_1_2*x_2_3 + 4*x_1_2*x_3_1 + 4*x_1_2*x_3_3 + 6*x_1_3*x_2_0\n",
+      "  + 6*x_1_3*x_2_2 + 4*x_1_3*x_3_0 + 4*x_1_3*x_3_2 + 8*x_2_0*x_3_1\n",
+      "  + 8*x_2_0*x_3_3 + 8*x_2_1*x_3_0 + 8*x_2_1*x_3_2 + 8*x_2_2*x_3_1\n",
+      "  + 8*x_2_2*x_3_3 + 8*x_2_3*x_3_0 + 8*x_2_3*x_3_2\n",
+      "\n",
+      "Subject to\n",
+      "  Linear constraints (8)\n",
+      "    x_0_0 + x_0_1 + x_0_2 + x_0_3 == 1  'c0'\n",
+      "    x_1_0 + x_1_1 + x_1_2 + x_1_3 == 1  'c1'\n",
+      "    x_2_0 + x_2_1 + x_2_2 + x_2_3 == 1  'c2'\n",
+      "    x_3_0 + x_3_1 + x_3_2 + x_3_3 == 1  'c3'\n",
+      "    x_0_0 + x_1_0 + x_2_0 + x_3_0 == 1  'c4'\n",
+      "    x_0_1 + x_1_1 + x_2_1 + x_3_1 == 1  'c5'\n",
+      "    x_0_2 + x_1_2 + x_2_2 + x_3_2 == 1  'c6'\n",
+      "    x_0_3 + x_1_3 + x_2_3 + x_3_3 == 1  'c7'\n",
+      "\n",
+      "  Binary variables (16)\n",
+      "    x_0_0 x_0_1 x_0_2 x_0_3 x_1_0 x_1_1 x_1_2 x_1_3 x_2_0 x_2_1 x_2_2 x_2_3\n",
+      "    x_3_0 x_3_1 x_3_2 x_3_3\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(qp.prettyprint())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bdc7c757",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qubo = QuadraticProgramToQubo()\n",
+    "qubo_version = qubo.convert(qp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0efa74b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Problem name: TSP\n",
+      "\n",
+      "Minimize\n",
+      "  626*x_0_0^2 + 626*x_0_0*x_0_1 + 626*x_0_0*x_0_2 + 626*x_0_0*x_0_3\n",
+      "  + 626*x_0_0*x_1_0 + 2*x_0_0*x_1_1 + 2*x_0_0*x_1_3 + 626*x_0_0*x_2_0\n",
+      "  + 9*x_0_0*x_2_1 + 9*x_0_0*x_2_3 + 626*x_0_0*x_3_0 + 10*x_0_0*x_3_1\n",
+      "  + 10*x_0_0*x_3_3 + 626*x_0_1^2 + 626*x_0_1*x_0_2 + 626*x_0_1*x_0_3\n",
+      "  + 2*x_0_1*x_1_0 + 626*x_0_1*x_1_1 + 2*x_0_1*x_1_2 + 9*x_0_1*x_2_0\n",
+      "  + 626*x_0_1*x_2_1 + 9*x_0_1*x_2_2 + 10*x_0_1*x_3_0 + 626*x_0_1*x_3_1\n",
+      "  + 10*x_0_1*x_3_2 + 626*x_0_2^2 + 626*x_0_2*x_0_3 + 2*x_0_2*x_1_1\n",
+      "  + 626*x_0_2*x_1_2 + 2*x_0_2*x_1_3 + 9*x_0_2*x_2_1 + 626*x_0_2*x_2_2\n",
+      "  + 9*x_0_2*x_2_3 + 10*x_0_2*x_3_1 + 626*x_0_2*x_3_2 + 10*x_0_2*x_3_3\n",
+      "  + 626*x_0_3^2 + 2*x_0_3*x_1_0 + 2*x_0_3*x_1_2 + 626*x_0_3*x_1_3\n",
+      "  + 9*x_0_3*x_2_0 + 9*x_0_3*x_2_2 + 626*x_0_3*x_2_3 + 10*x_0_3*x_3_0\n",
+      "  + 10*x_0_3*x_3_2 + 626*x_0_3*x_3_3 + 626*x_1_0^2 + 626*x_1_0*x_1_1\n",
+      "  + 626*x_1_0*x_1_2 + 626*x_1_0*x_1_3 + 626*x_1_0*x_2_0 + 6*x_1_0*x_2_1\n",
+      "  + 6*x_1_0*x_2_3 + 626*x_1_0*x_3_0 + 4*x_1_0*x_3_1 + 4*x_1_0*x_3_3\n",
+      "  + 626*x_1_1^2 + 626*x_1_1*x_1_2 + 626*x_1_1*x_1_3 + 6*x_1_1*x_2_0\n",
+      "  + 626*x_1_1*x_2_1 + 6*x_1_1*x_2_2 + 4*x_1_1*x_3_0 + 626*x_1_1*x_3_1\n",
+      "  + 4*x_1_1*x_3_2 + 626*x_1_2^2 + 626*x_1_2*x_1_3 + 6*x_1_2*x_2_1\n",
+      "  + 626*x_1_2*x_2_2 + 6*x_1_2*x_2_3 + 4*x_1_2*x_3_1 + 626*x_1_2*x_3_2\n",
+      "  + 4*x_1_2*x_3_3 + 626*x_1_3^2 + 6*x_1_3*x_2_0 + 6*x_1_3*x_2_2\n",
+      "  + 626*x_1_3*x_2_3 + 4*x_1_3*x_3_0 + 4*x_1_3*x_3_2 + 626*x_1_3*x_3_3\n",
+      "  + 626*x_2_0^2 + 626*x_2_0*x_2_1 + 626*x_2_0*x_2_2 + 626*x_2_0*x_2_3\n",
+      "  + 626*x_2_0*x_3_0 + 8*x_2_0*x_3_1 + 8*x_2_0*x_3_3 + 626*x_2_1^2\n",
+      "  + 626*x_2_1*x_2_2 + 626*x_2_1*x_2_3 + 8*x_2_1*x_3_0 + 626*x_2_1*x_3_1\n",
+      "  + 8*x_2_1*x_3_2 + 626*x_2_2^2 + 626*x_2_2*x_2_3 + 8*x_2_2*x_3_1\n",
+      "  + 626*x_2_2*x_3_2 + 8*x_2_2*x_3_3 + 626*x_2_3^2 + 8*x_2_3*x_3_0\n",
+      "  + 8*x_2_3*x_3_2 + 626*x_2_3*x_3_3 + 626*x_3_0^2 + 626*x_3_0*x_3_1\n",
+      "  + 626*x_3_0*x_3_2 + 626*x_3_0*x_3_3 + 626*x_3_1^2 + 626*x_3_1*x_3_2\n",
+      "  + 626*x_3_1*x_3_3 + 626*x_3_2^2 + 626*x_3_2*x_3_3 + 626*x_3_3^2 - 1252*x_0_0\n",
+      "  - 1252*x_0_1 - 1252*x_0_2 - 1252*x_0_3 - 1252*x_1_0 - 1252*x_1_1 - 1252*x_1_2\n",
+      "  - 1252*x_1_3 - 1252*x_2_0 - 1252*x_2_1 - 1252*x_2_2 - 1252*x_2_3 - 1252*x_3_0\n",
+      "  - 1252*x_3_1 - 1252*x_3_2 - 1252*x_3_3 + 2504\n",
+      "\n",
+      "Subject to\n",
+      "  No constraints\n",
+      "\n",
+      "  Binary variables (16)\n",
+      "    x_0_0 x_0_1 x_0_2 x_0_3 x_1_0 x_1_1 x_1_2 x_1_3 x_2_0 x_2_1 x_2_2 x_2_3\n",
+      "    x_3_0 x_3_1 x_3_2 x_3_3\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(qubo_version.prettyprint())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ed5ef295",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Offest :  5086.0\n",
+      "Ising Hamiltonian  : \n",
+      "SparsePauliOp(['IIIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIZI', 'IIIIIIIIIIIIIZII', 'IIIIIIIIIIIIZIII', 'IIIIIIIIIIIZIIII', 'IIIIIIIIIIZIIIII', 'IIIIIIIIIZIIIIII', 'IIIIIIIIZIIIIIII', 'IIIIIIIZIIIIIIII', 'IIIIIIZIIIIIIIII', 'IIIIIZIIIIIIIIII', 'IIIIZIIIIIIIIIII', 'IIIZIIIIIIIIIIII', 'IIZIIIIIIIIIIIII', 'IZIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIII', 'IIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIZIZ', 'IIIIIIIIIIIIZIIZ', 'IIIIIIIIIIIZIIIZ', 'IIIIIIIIIIZIIIIZ', 'IIIIIIIIZIIIIIIZ', 'IIIIIIIZIIIIIIIZ', 'IIIIIIZIIIIIIIIZ', 'IIIIZIIIIIIIIIIZ', 'IIIZIIIIIIIIIIIZ', 'IIZIIIIIIIIIIIIZ', 'ZIIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIZIZI', 'IIIIIIIIIIIZIIZI', 'IIIIIIIIIIZIIIZI', 'IIIIIIIIIZIIIIZI', 'IIIIIIIZIIIIIIZI', 'IIIIIIZIIIIIIIZI', 'IIIIIZIIIIIIIIZI', 'IIIZIIIIIIIIIIZI', 'IIZIIIIIIIIIIIZI', 'IZIIIIIIIIIIIIZI', 'IIIIIIIIIIIIZZII', 'IIIIIIIIIIZIIZII', 'IIIIIIIIIZIIIZII', 'IIIIIIIIZIIIIZII', 'IIIIIIZIIIIIIZII', 'IIIIIZIIIIIIIZII', 'IIIIZIIIIIIIIZII', 'IIZIIIIIIIIIIZII', 'IZIIIIIIIIIIIZII', 'ZIIIIIIIIIIIIZII', 'IIIIIIIIIIIZZIII', 'IIIIIIIIIZIIZIII', 'IIIIIIIIZIIIZIII', 'IIIIIIIZIIIIZIII', 'IIIIIZIIIIIIZIII', 'IIIIZIIIIIIIZIII', 'IIIZIIIIIIIIZIII', 'IZIIIIIIIIIIZIII', 'ZIIIIIIIIIIIZIII', 'IIIIIIIIIIZZIIII', 'IIIIIIIIIZIZIIII', 'IIIIIIIIZIIZIIII', 'IIIIIIIZIIIZIIII', 'IIIIIIZIIIIZIIII', 'IIIIZIIIIIIZIIII', 'IIIZIIIIIIIZIIII', 'IIZIIIIIIIIZIIII', 'ZIIIIIIIIIIZIIII', 'IIIIIIIIIZZIIIII', 'IIIIIIIIZIZIIIII', 'IIIIIIIZIIZIIIII', 'IIIIIIZIIIZIIIII', 'IIIIIZIIIIZIIIII', 'IIIZIIIIIIZIIIII', 'IIZIIIIIIIZIIIII', 'IZIIIIIIIIZIIIII', 'IIIIIIIIZZIIIIII', 'IIIIIIZIIZIIIIII', 'IIIIIZIIIZIIIIII', 'IIIIZIIIIZIIIIII', 'IIZIIIIIIZIIIIII', 'IZIIIIIIIZIIIIII', 'ZIIIIIIIIZIIIIII', 'IIIIIIIZZIIIIIII', 'IIIIIZIIZIIIIIII', 'IIIIZIIIZIIIIIII', 'IIIZIIIIZIIIIIII', 'IZIIIIIIZIIIIIII', 'ZIIIIIIIZIIIIIII', 'IIIIIIZZIIIIIIII', 'IIIIIZIZIIIIIIII', 'IIIIZIIZIIIIIIII', 'IIIZIIIZIIIIIIII', 'IIZIIIIZIIIIIIII', 'ZIIIIIIZIIIIIIII', 'IIIIIZZIIIIIIIII', 'IIIIZIZIIIIIIIII', 'IIIZIIZIIIIIIIII', 'IIZIIIZIIIIIIIII', 'IZIIIIZIIIIIIIII', 'IIIIZZIIIIIIIIII', 'IIZIIZIIIIIIIIII', 'IZIIIZIIIIIIIIII', 'ZIIIIZIIIIIIIIII', 'IIIZZIIIIIIIIIII', 'IZIIZIIIIIIIIIII', 'ZIIIZIIIIIIIIIII', 'IIZZIIIIIIIIIIII', 'IZIZIIIIIIIIIIII', 'ZIIZIIIIIIIIIIII', 'IZZIIIIIIIIIIIII', 'ZIZIIIIIIIIIIIII', 'ZZIIIIIIIIIIIIII'],\n",
+      "              coeffs=[-6.365e+02+0.j, -6.365e+02+0.j, -6.365e+02+0.j, -6.365e+02+0.j,\n",
+      " -6.320e+02+0.j, -6.320e+02+0.j, -6.320e+02+0.j, -6.320e+02+0.j,\n",
+      " -6.375e+02+0.j, -6.375e+02+0.j, -6.375e+02+0.j, -6.375e+02+0.j,\n",
+      " -6.370e+02+0.j, -6.370e+02+0.j, -6.370e+02+0.j, -6.370e+02+0.j,\n",
+      "  1.565e+02+0.j,  1.565e+02+0.j,  1.565e+02+0.j,  1.565e+02+0.j,\n",
+      "  5.000e-01+0.j,  5.000e-01+0.j,  1.565e+02+0.j,  2.250e+00+0.j,\n",
+      "  2.250e+00+0.j,  1.565e+02+0.j,  2.500e+00+0.j,  2.500e+00+0.j,\n",
+      "  1.565e+02+0.j,  1.565e+02+0.j,  5.000e-01+0.j,  1.565e+02+0.j,\n",
+      "  5.000e-01+0.j,  2.250e+00+0.j,  1.565e+02+0.j,  2.250e+00+0.j,\n",
+      "  2.500e+00+0.j,  1.565e+02+0.j,  2.500e+00+0.j,  1.565e+02+0.j,\n",
+      "  5.000e-01+0.j,  1.565e+02+0.j,  5.000e-01+0.j,  2.250e+00+0.j,\n",
+      "  1.565e+02+0.j,  2.250e+00+0.j,  2.500e+00+0.j,  1.565e+02+0.j,\n",
+      "  2.500e+00+0.j,  5.000e-01+0.j,  5.000e-01+0.j,  1.565e+02+0.j,\n",
+      "  2.250e+00+0.j,  2.250e+00+0.j,  1.565e+02+0.j,  2.500e+00+0.j,\n",
+      "  2.500e+00+0.j,  1.565e+02+0.j,  1.565e+02+0.j,  1.565e+02+0.j,\n",
+      "  1.565e+02+0.j,  1.565e+02+0.j,  1.500e+00+0.j,  1.500e+00+0.j,\n",
+      "  1.565e+02+0.j,  1.000e+00+0.j,  1.000e+00+0.j,  1.565e+02+0.j,\n",
+      "  1.565e+02+0.j,  1.500e+00+0.j,  1.565e+02+0.j,  1.500e+00+0.j,\n",
+      "  1.000e+00+0.j,  1.565e+02+0.j,  1.000e+00+0.j,  1.565e+02+0.j,\n",
+      "  1.500e+00+0.j,  1.565e+02+0.j,  1.500e+00+0.j,  1.000e+00+0.j,\n",
+      "  1.565e+02+0.j,  1.000e+00+0.j,  1.500e+00+0.j,  1.500e+00+0.j,\n",
+      "  1.565e+02+0.j,  1.000e+00+0.j,  1.000e+00+0.j,  1.565e+02+0.j,\n",
+      "  1.565e+02+0.j,  1.565e+02+0.j,  1.565e+02+0.j,  1.565e+02+0.j,\n",
+      "  2.000e+00+0.j,  2.000e+00+0.j,  1.565e+02+0.j,  1.565e+02+0.j,\n",
+      "  2.000e+00+0.j,  1.565e+02+0.j,  2.000e+00+0.j,  1.565e+02+0.j,\n",
+      "  2.000e+00+0.j,  1.565e+02+0.j,  2.000e+00+0.j,  2.000e+00+0.j,\n",
+      "  2.000e+00+0.j,  1.565e+02+0.j,  1.565e+02+0.j,  1.565e+02+0.j,\n",
+      "  1.565e+02+0.j,  1.565e+02+0.j,  1.565e+02+0.j,  1.565e+02+0.j])\n"
+     ]
+    }
+   ],
+   "source": [
+    "op,offset=qubo_version.to_ising()\n",
+    "print(f\"Offest : \",offset)\n",
+    "print(\"Ising Hamiltonian  : \")\n",
+    "print(op)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "467b17e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "objective function value: 23.0\n",
+      "variable values: x_0_0=0.0, x_0_1=0.0, x_0_2=1.0, x_0_3=0.0, x_1_0=0.0, x_1_1=0.0, x_1_2=0.0, x_1_3=1.0, x_2_0=0.0, x_2_1=1.0, x_2_2=0.0, x_2_3=0.0, x_3_0=1.0, x_3_1=0.0, x_3_2=0.0, x_3_3=0.0\n",
+      "status: SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "qa=MinimumEigenOptimizer(QAOA(sampler=Sampler(),optimizer=COBYLA()))\n",
+    "rslt=qa.solve(qubo_version)\n",
+    "print(rslt.prettyprint())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c5d244ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "QAOA Solution:  [3, 2, 0, 1]\n",
+      "Cost:  5109.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "qubo_path = tsp_custom.interpret(rslt.x)\n",
+    "print(\"QAOA Solution: \",qubo_path)\n",
+    "print(\"Cost: \",rslt.fval+offset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86c1ce89",
+   "metadata": {},
+   "source": [
+    "## SPSA Optimiser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "7df20038",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Problem name: TSP\n",
+      "\n",
+      "Minimize\n",
+      "  2*x_0_0*x_1_1 + 2*x_0_0*x_1_3 + 9*x_0_0*x_2_1 + 9*x_0_0*x_2_3 + 10*x_0_0*x_3_1\n",
+      "  + 10*x_0_0*x_3_3 + 2*x_0_1*x_1_0 + 2*x_0_1*x_1_2 + 9*x_0_1*x_2_0\n",
+      "  + 9*x_0_1*x_2_2 + 10*x_0_1*x_3_0 + 10*x_0_1*x_3_2 + 2*x_0_2*x_1_1\n",
+      "  + 2*x_0_2*x_1_3 + 9*x_0_2*x_2_1 + 9*x_0_2*x_2_3 + 10*x_0_2*x_3_1\n",
+      "  + 10*x_0_2*x_3_3 + 2*x_0_3*x_1_0 + 2*x_0_3*x_1_2 + 9*x_0_3*x_2_0\n",
+      "  + 9*x_0_3*x_2_2 + 10*x_0_3*x_3_0 + 10*x_0_3*x_3_2 + 6*x_1_0*x_2_1\n",
+      "  + 6*x_1_0*x_2_3 + 4*x_1_0*x_3_1 + 4*x_1_0*x_3_3 + 6*x_1_1*x_2_0\n",
+      "  + 6*x_1_1*x_2_2 + 4*x_1_1*x_3_0 + 4*x_1_1*x_3_2 + 6*x_1_2*x_2_1\n",
+      "  + 6*x_1_2*x_2_3 + 4*x_1_2*x_3_1 + 4*x_1_2*x_3_3 + 6*x_1_3*x_2_0\n",
+      "  + 6*x_1_3*x_2_2 + 4*x_1_3*x_3_0 + 4*x_1_3*x_3_2 + 8*x_2_0*x_3_1\n",
+      "  + 8*x_2_0*x_3_3 + 8*x_2_1*x_3_0 + 8*x_2_1*x_3_2 + 8*x_2_2*x_3_1\n",
+      "  + 8*x_2_2*x_3_3 + 8*x_2_3*x_3_0 + 8*x_2_3*x_3_2\n",
+      "\n",
+      "Subject to\n",
+      "  Linear constraints (8)\n",
+      "    x_0_0 + x_0_1 + x_0_2 + x_0_3 == 1  'c0'\n",
+      "    x_1_0 + x_1_1 + x_1_2 + x_1_3 == 1  'c1'\n",
+      "    x_2_0 + x_2_1 + x_2_2 + x_2_3 == 1  'c2'\n",
+      "    x_3_0 + x_3_1 + x_3_2 + x_3_3 == 1  'c3'\n",
+      "    x_0_0 + x_1_0 + x_2_0 + x_3_0 == 1  'c4'\n",
+      "    x_0_1 + x_1_1 + x_2_1 + x_3_1 == 1  'c5'\n",
+      "    x_0_2 + x_1_2 + x_2_2 + x_3_2 == 1  'c6'\n",
+      "    x_0_3 + x_1_3 + x_2_3 + x_3_3 == 1  'c7'\n",
+      "\n",
+      "  Binary variables (16)\n",
+      "    x_0_0 x_0_1 x_0_2 x_0_3 x_1_0 x_1_1 x_1_2 x_1_3 x_2_0 x_2_1 x_2_2 x_2_3\n",
+      "    x_3_0 x_3_1 x_3_2 x_3_3\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(qp.prettyprint())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45b735e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qaoa_spsa = MinimumEigenOptimizer(QAOA(sampler=Sampler(), optimizer=SPSA(), reps=3))    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "72ca4ce0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "QAOA Solution with COBYLA:  [3, 2, 0, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "qubo_path_spsa = tsp_custom.interpret(qaoa_spsa.solve(qp).x)\n",
+    "print(\"QAOA Solution with SPSA: \", qubo_path_spsa)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27fd0197",
+   "metadata": {},
+   "source": [
+    "### QAOA Circuit Diagram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "48d7414c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2252.61x31554.8 with 1 Axes>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc.decompose().draw('mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "5ab958fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2252.61x19514.8 with 1 Axes>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc.decompose().decompose().draw('mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9c57b09",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}