data.ts

import { BlogPost, Project, Experience, Education, SkillCategory } from './types';

export const ABOUT_TEXT = `The world of quantum mechanics is often depicted as a bizarre and counterintuitive place. Particles existing in multiple states simultaneously, spooky action at a distance – it all sounds like science fiction. But here’s the thing: quantum mechanics isn’t some fringe theory. It’s the undeniably successful framework that underpins the behavior of the universe at its most fundamental level.

My passion lies in demystifying this "weirdness" and translating it into powerful, real-world applications. I believe that by embracing concepts like superposition, wave-particle duality, and entanglement, we can unlock a new era of technological innovation. My work focuses on the intersection of quantum computing and artificial intelligence, exploring how quantum algorithms can solve problems currently intractable for even the most powerful classical supercomputers.`;

export const SKILLS: SkillCategory[] = [
  {
    category: "Quantum Computing",
    items: ["Quantum Algorithms (Shor’s, Grover’s)", "Quantum Machine Learning", "Quantum Error Correction", "Quantum Information Theory", "Entanglement & Superposition"]
  },
  {
    category: "AI & Machine Learning",
    items: ["Deep Learning", "Neural Networks", "Reinforcement Learning", "NLP", "Computer Vision"]
  },
  {
    category: "Programming & Software",
    items: ["Python", "C++", "Qiskit", "PennyLane", "Cirq", "TensorFlow", "PyTorch", "Scikit-learn"]
  },
  {
    category: "Mathematics",
    items: ["Linear Algebra", "Probability & Statistics", "Complex Analysis", "Differential Equations"]
  }
];

export const TECH_STACK = [
  { category: "Quantum Frameworks", technologies: ["Qiskit", "PennyLane", "Cirq", "D-Wave Ocean", "Microsoft QDK"] },
  { category: "AI/ML Libraries", technologies: ["TensorFlow", "PyTorch", "Keras", "Scikit-learn", "Hugging Face", "NLTK"] },
  { category: "Programming Languages", technologies: ["Python", "C++", "MATLAB"] },
  { category: "Cloud & Platforms", technologies: ["IBM Quantum", "AWS Braket", "Google Cloud", "Azure Quantum", "Docker"] },
];

export const EXPERIENCE: Experience[] = [
  {
    role: "Quantum Research Scientist",
    company: "IBM Quantum",
    period: "June 2024 - Present",
    responsibilities: [
      "Developing and implementing novel quantum machine learning algorithms on IBM’s fleet of superconducting quantum processors.",
      "Collaborating with industry partners to identify and solve high-impact business problems in finance, materials science, and healthcare.",
      "Contributing to the open-source Qiskit framework, focusing on the qiskit-machine-learning module."
    ]
  }
];

export const EDUCATION: Education[] = [
  {
    institution: "Massachusetts Institute of Technology (MIT)",
    degree: "Ph.D. in Quantum Information Science",
    period: "September 2019 - May 2024",
    details: "Thesis: Developing Noise-Resilient Quantum Machine Learning Algorithms for Near-Term Devices."
  },
  {
    institution: "University of Oxford",
    degree: "M.Sc. in Physics and Theoretical Physics",
    period: "October 2017 - September 2018",
    details: "Focus on Quantum Field Theory and Condensed Matter Physics."
  },
  {
    institution: "Stanford University",
    degree: "B.S. in Computer Science",
    period: "September 2013 - June 2017",
    details: "Specialization in Artificial Intelligence."
  }
];

export const PROJECTS: Project[] = [
  {
    title: "Quantum Variational Classifier for Drug Discovery",
    description: "Developed a hybrid quantum-classical model using a Variational Quantum Eigensolver (VQE) to predict molecular binding affinities.",
    technologies: ["VQE", "QML", "Drug Discovery", "Chemoinformatics"]
  },
  {
    title: "Noise-Resistant Entanglement Distribution Protocol",
    description: "Simulated a novel entanglement swapping protocol incorporating error-mitigation techniques. Showcased improved fidelity on noisy simulated quantum hardware.",
    technologies: ["Quantum Networking", "Entanglement", "Error Mitigation", "Qiskit Aer"]
  },
  {
    title: "Quantum Natural Language Processing (QNLP) Model",
    description: "An exploratory project implementing a sentence-meaning model on a quantum circuit, based on the DisCoCat framework, to classify text sentiment.",
    technologies: ["QNLP", "DisCoCat", "PennyLane", "NLP"]
  },
  {
    title: "Financial Portfolio Optimization using Quantum Annealing",
    description: "Formulated a Quadratic Unconstrained Binary Optimization (QUBO) problem for portfolio optimization and solved it using D-Wave’s quantum annealer.",
    technologies: ["Quantum Annealing", "QUBO", "Finance", "D-Wave Ocean"]
  },
  {
    title: "Educational Quantum Circuit Simulator in Python",
    description: "Built a lightweight quantum circuit simulator from scratch using NumPy to teach the fundamental principles of quantum gates, superposition, and entanglement.",
    technologies: ["Education", "Simulator", "Quantum Gates", "NumPy"]
  }
];

// Content Generation
const CATEGORIES = ['Quantum Mechanics', 'Calculus', 'Linear Algebra', 'AI & ML'] as const;

const POST_CONTENTS: Record<string, string> = {
  "Understanding Eigenvalues in Quantum States": `
    <p>In quantum mechanics, an observable is represented by a Hermitian operator. The possible values you can measure are the <strong>eigenvalues</strong> of this operator.</p>
    <p>If $\\hat{A}$ is an operator and $\\psi$ is a state vector, the eigenvalue equation is:</p>
    $$ \\hat{A} |\\psi\\rangle = \\lambda |\\psi\\rangle $$
    <p>Here, $\\lambda$ is the eigenvalue. For example, the Hamiltonian operator $\\hat{H}$ represents total energy:</p>
    $$ \\hat{H} |\\psi\\rangle = E |\\psi\\rangle $$
    <p>When we measure the energy of a system in state $|\\psi\\rangle$, we get one of the eigenvalues $E_n$ of $\\hat{H}$. The state then "collapses" to the corresponding eigenvector $|n\\rangle$.</p>
    <h3 class="text-xl font-bold text-white mt-4 mb-2">Matrix Representation</h3>
    <p>For a qubit, operators are $2 \\times 2$ matrices. Consider the Pauli-Z operator:</p>
    $$ \\sigma_z = \\begin{pmatrix} 1 & 0 \\\\ 0 & -1 \\end{pmatrix} $$
    <p>The eigenvalues are $+1$ and $-1$, corresponding to the states $|0\\rangle$ and $|1\\rangle$.</p>
  `,
  "The Derivative of a Wave Function": `
    <p>The Schrödinger equation describes how the quantum state of a physical system changes over time. It is a linear partial differential equation.</p>
    $$ i\\hbar \\frac{\\partial}{\\partial t} \\Psi(x,t) = \\left[ -\\frac{\\hbar^2}{2m} \\frac{\\partial^2}{\\partial x^2} + V(x,t) \\right] \\Psi(x,t) $$
    <p>Let's look at the momentum operator in position space. It involves a spatial derivative:</p>
    $$ \\hat{p} = -i\\hbar \\frac{\\partial}{\\partial x} $$
    <p>This means that momentum is intimately linked to the gradient of the wave function. A wave function with a shorter wavelength (higher frequency) changes more rapidly in space, resulting in a larger derivative and thus higher momentum.</p>
  `,
  "Matrix Multiplication from Scratch": `
    <p>Linear algebra is the language of quantum computing. Quantum gates are unitary matrices that multiply state vectors.</p>
    <p>If we have a state $|\\psi\\rangle = \\alpha|0\\rangle + \\beta|1\\rangle$ represented as:</p>
    $$ \\begin{pmatrix} \\alpha \\\\ \\beta \\end{pmatrix} $$
    <p>And we apply a Hadamard gate $H$:</p>
    $$ H = \\frac{1}{\\sqrt{2}} \\begin{pmatrix} 1 & 1 \\\\ 1 & -1 \\end{pmatrix} $$
    <p>The new state is $H|\\psi\\rangle$:</p>
    $$ \\frac{1}{\\sqrt{2}} \\begin{pmatrix} 1 & 1 \\\\ 1 & -1 \\end{pmatrix} \\begin{pmatrix} \\alpha \\\\ \\beta \\end{pmatrix} = \\frac{1}{\\sqrt{2}} \\begin{pmatrix} \\alpha + \\beta \\\\ \\alpha - \\beta \\end{pmatrix} $$
    <p>This simple matrix multiplication creates superposition from basis states.</p>
  `,
  "Superposition vs. Classical Probability": `
    <p>In classical probability, a coin is either heads or tails with probability 0.5. We describe this as a probability distribution.</p>
    <p>In quantum mechanics, a qubit can be in a state:</p>
    $$ |\\psi\\rangle = \\alpha|0\\rangle + \\beta|1\\rangle $$
    <p>Where $\\alpha$ and $\\beta$ are complex numbers called amplitudes. The probability of measuring 0 is $|\\alpha|^2$.</p>
    <p>The key difference is interference. Probabilities must be non-negative real numbers, but amplitudes can be negative or complex. When we add amplitudes:</p>
    $$ P_{total} = |\\psi_1 + \\psi_2|^2 = |\\psi_1|^2 + |\\psi_2|^2 + 2Re(\\psi_1^* \\psi_2) $$
    <p>That last term is the interference term, which does not exist in classical probability ($P_{total} \\neq P_1 + P_2$).</p>
  `,
  "Grover's Search Algorithm Explained": `
    <p>Grover's algorithm provides a quadratic speedup for unstructured search problems. If you have $N$ items, classical search takes $O(N)$ steps. Grover takes $O(\\sqrt{N})$.</p>
    <p>It works by amplitude amplification. We start with a uniform superposition of all states:</p>
    $$ |s\\rangle = \\frac{1}{\\sqrt{N}} \\sum_{x=0}^{N-1} |x\\rangle $$
    <p>We then iteratively apply two operators:</p>
    <ol class="list-decimal pl-6 my-4 space-y-2">
      <li>The Oracle $U_\\omega$: Flips the phase of the target state $|\\omega\\rangle$.</li>
      <li>The Diffuser $U_s$: Inverts amplitudes about the mean.</li>
    </ol>
    $$ U_{grover} = U_s U_\\omega $$
    <p>Geometrically, this rotates the state vector closer to the target state $|\\omega\\rangle$ in the Hilbert space.</p>
  `,
  "Visualizing High-Dimensional Vector Spaces": `
    <p>A single qubit lives in a 2D complex vector space (Hilbert space). Two qubits live in a 4D space. $n$ qubits live in a $2^n$ dimensional space.</p>
    <p>Visualizing this is hard. For a single qubit, we use the <strong>Bloch Sphere</strong>.</p>
    <p>Any pure qubit state can be written as:</p>
    $$ |\\psi\\rangle = \\cos(\\theta/2)|0\\rangle + e^{i\\phi}\\sin(\\theta/2)|1\\rangle $$
    <p>Here, $\\theta$ and $\\phi$ are spherical coordinates. The north pole is $|0\\rangle$, south pole is $|1\\rangle$.</p>
    <p>Operations like the X-gate rotate the vector by $\\pi$ around the X-axis.</p>
  `,
  "The Chain Rule in Backpropagation": `
    <p>Backpropagation is the backbone of training Neural Networks. It's essentially the chain rule of calculus applied over and over again.</p>
    <p>If we have a loss function $L$ and weights $w$, we want the gradient $\\frac{\\partial L}{\\partial w}$.</p>
    <p>If $L$ depends on output $y$, which depends on activation $a$, which depends on sum $z$, which depends on $w$:</p>
    $$ \\frac{\\partial L}{\\partial w} = \\frac{\\partial L}{\\partial y} \\cdot \\frac{\\partial y}{\\partial a} \\cdot \\frac{\\partial a}{\\partial z} \\cdot \\frac{\\partial z}{\\partial w} $$
    <p>In Quantum Machine Learning (QML), we use the <strong>Parameter Shift Rule</strong> to evaluate gradients of quantum circuits analytically, which looks very similar to finite differences but is exact.</p>
  `,
  "Entanglement Entropy for Beginners": `
    <p>Entanglement is the correlation between parts of a quantum system that cannot be described classically. How do we measure it?</p>
    <p>One measure is the <strong>Von Neumann Entropy</strong> of the reduced density matrix.</p>
    <p>If we have a system $AB$ in a pure state $|\\psi_{AB}\\rangle$, we trace out system $B$ to get the reduced density matrix for $A$:</p>
    $$ \\rho_A = \\text{Tr}_B(|\\psi_{AB}\\rangle\\langle\\psi_{AB}|) $$
    <p>The entanglement entropy is:</p>
    $$ S(\\rho_A) = -\\text{Tr}(\\rho_A \\ln \\rho_A) $$
    <p>If $S=0$, the state is separable (not entangled). If $S > 0$, there is entanglement.</p>
  `,
  "Fourier Transforms in Quantum Computing": `
    <p>The Quantum Fourier Transform (QFT) is the quantum analogue of the Discrete Fourier Transform (DFT). It is exponentially faster, taking $O(n^2)$ gates for $n$ qubits compared to $O(n 2^n)$ for classical FFT.</p>
    <p>The QFT maps a state $|x\\rangle$ to:</p>
    $$ \\text{QFT}|x\\rangle = \\frac{1}{\\sqrt{N}} \\sum_{k=0}^{N-1} \\omega_N^{xk} |k\\rangle $$
    <p>Where $\\omega_N = e^{2\\pi i / N}$.</p>
    <p>The QFT doesn't give us the Fourier coefficients directly upon measurement (amplitudes collapse), but it is a crucial subroutine in Phase Estimation and Shor's Algorithm.</p>
  `,
  "Optimization Landscapes in VQE": `
    <p>Variational Quantum Eigensolvers (VQE) are hybrid algorithms used to find ground state energies. We parameterize a quantum circuit with angles $\\vec{\\theta}$ to create a state $|\\psi(\\vec{\\theta})\\rangle$.</p>
    <p>We minimize the cost function:</p>
    $$ C(\\vec{\\theta}) = \\langle \\psi(\\vec{\\theta}) | \\hat{H} | \\psi(\\vec{\\theta}) \\rangle $$
    <p>The optimization landscape can be tricky. We often face <strong>Barren Plateaus</strong>, where the gradient vanishes exponentially with the number of qubits.</p>
    $$ \\text{Var}(\\partial_k C) \\approx e^{-\\alpha n} $$
    <p>This makes training large quantum neural networks difficult and is an active area of research.</p>
  `
};

export const generateBlogPosts = (): BlogPost[] => {
  const posts: BlogPost[] = [];
  const baseTitles = Object.keys(POST_CONTENTS);

  for (let i = 0; i < 100; i++) {
    const titleBase = baseTitles[i % baseTitles.length];
    const category = CATEGORIES[i % CATEGORIES.length];
    posts.push({
      id: `post-${i}`,
      title: `${titleBase}${i >= 10 ? ` - Vol. ${Math.floor(i/10)+1}` : ''}`,
      excerpt: `An in-depth exploration of ${category.toLowerCase()} concepts using Python visualizations. We delve into the mathematical rigor required to understand the universe.`,
      category: category,
      date: new Date(2024, 0, 1 + i).toISOString().split('T')[0],
      readTime: `${5 + (i % 10)} min read`,
      content: POST_CONTENTS[titleBase] // Inject specific content
    });
  }
  return posts;
};

export const BLOG_POSTS = generateBlogPosts();