Sentinel Hypervisor: Hybrid Quantum-Classical Orchestrator

Lead Architect: Sandeep Kumar Sahoo (MrDecryptDecipher) Domain: Quantitative Finance & Quantum Reliability Engineering Compliance: FIPS 204 (Post-Quantum Cryptography)

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🚀 Project Overview

The Sentinel Hypervisor represents a paradigm shift in financial technology, establishing a "Zero-Point" control plane that unifies High-Frequency Trading (HFT) with Quantum Computing.

In the current NISQ (Noisy Intermediate-Scale Quantum) era, relying solely on classical heuristics is insufficient for complex derivative pricing. Similarly, raw quantum hardware is too noisy for direct production use. Sentinel bridges this chasm by acting as an intelligent Hypervisor:

• For the Quant: It provides an interface to execute Heston Volatility Models using Quantum Amplitude Estimation, delivering quadratic speedups ($\mathcal{O}(1/\epsilon)$).
• For the Engineer: It enforces Reliability (SRE) via runtime formal verification and physics-based "pre-flight" checks, preventing execution on unstable hardware.
• For the Future: It integrates Azure Quantum (Q#) and Holographic Error Correction, preparing the system for the Fault-Tolerant era.

This is not a simulation. This is a production-grade orchestration engine written in Rust, capable of dispatching jobs to IBM Quantum Heron processors and Neutral Atom arrays.

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🏛️ Architecture

1. The Actor Model (Orchestration)

The system utilizes a Rust-based Actor model to manage strict concurrency between market data ingestion and quantum job dispatch.

graph TD
    subgraph "Market Ecology"
        Market[Alpaca WebSocket] -->|JSON Stream| Feed[Heston Feed Engine]
    end

    subgraph "Rust Control Plane"
        Feed -->|Tick Data| Mgr[Quantum Manager]
        Mgr -->|Validate| SRE[Coherence Verifier]
        SRE -->|Dispatch| Nexus[Interop Nexus]
    end

    subgraph "Quantum Execution"
        Nexus -->|PyO3 Bridge| Primitives[Qiskit Primitives]
        Primitives -->|Pulse Schedule| QPU[IBM Quantum Heron]
    end
    
    QPU -->|Results| Ledger[Post-Quantum Ledger]

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2. Semantic Inference Engine

Strategy parameters are dynamically tuned by querying a localized Knowledge Graph ($G = (V, E)$) containing hardware specifications.

graph LR
    KG[(Knowledge Graph)] -->|Query Specs| Engine[Inference Engine]
    Hardware[Target: IBM Heron] -.->|EPLG & T1| KG
    
    Engine -->|High Fidelity| Deep[Strategy: Deep QAOA p=4]
    Engine -->|Low Coherence| Shallow[Strategy: Shallow QAOA p=1]

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⚛️ Quantum Modules

Module A: Quantum Quantitative Finance (Option Pricing)

We utilize Iterative Quantum Amplitude Estimation (IQAE) to estimate the expectation value of European Call Options. This approach offers a theoretical quadratic speedup over classical Monte Carlo methods ($\mathcal{O}(1/\epsilon)$ vs $\mathcal{O}(1/\epsilon^2)$) .

Mathematical Foundation: The algorithm estimates $a$ such that: $$ \hat{a} = \sin^2(y \pi / M) $$ Where the Grover Operator $\mathcal{Q}$ amplifies the amplitude of the desired state $|\psi_1\rangle$ (in-the-money paths).

sequenceDiagram
    participant Rust as Orchestrator
    participant Py as Qiskit Engine
    participant QPU as Quantum Backend
    
    Rust->>Py: Invoke IQAE(Spot, Strike, Vol)
    Py->>Py: Encode Heston Volatility -> Uncertainty Circuit
    Py->>Py: Append Payoff Operator (Linear Amplitude)
    Py->>QPU: Execute Oracle Queries
    QPU-->>Py: Return Measurement Counts
    Py-->>Rust: Estimated Option Premium

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Module B: Dynamical Decoupling (Error Mitigation)

To mitigate decoherence during circuit execution times exceeding $T_2^*$, the system automatically injects distinct pulse sequences (e.g., $X-X$ or $XY4$) on idle qubits.

gantt
    title Pulse Schedule (X-X Sequence)
    dateFormat s
    axisFormat %S
    
    section Qubit 0 (Busy)
    Rzz Gate       :active, 0, 2
    Rx Gate        :active, 4, 5
    
    section Qubit 1 (Idle)
    Identity       : 0, 1
    X-Pulse (Pi)   :crit, 1, 1.2
    Identity       : 1.2, 2.8
    X-Pulse (Pi)   :crit, 2.8, 3.0
    Identity       : 3, 4

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Module C: Azure Quantum & Q# (Fault Tolerance)

We provide Q# specifications for the Resource Estimator to calculate the physical qubit overhead necessary for Fault-Tolerant Quantum Computing (FTQC).

• Pricing Oracle: PricingOracle.qs implements the Amplitude Amplification operator.
• Resource Estimation: ResourceEstimator.qs acts as the entry point for logical-to-physical mapping.

graph TD
    Algo[Q# Pricing Algorithm] -->|Submit| Azure[Azure Quantum]
    Azure -->|Compile| RE[Resource Estimator]
    RE -->|Output| Metrics[FTQC Metrics]
    
    Metrics -->|Qubits| A[Physical Qubits: 14k]
    Metrics -->|Runtime| B[Runtime: 4ms]
    Metrics -->|T-States| C[T-Factories: 4]

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Module D: Advanced Frontiers

The hypervisor supports next-generation paradigms via specialized adapters:

• Holographic QEC: holographic_qec.py simulations using HaPPY (AdS/CFT) codes for error correction via spacetime geometry.
• Topological Qubits: Topological.qs models Majorana Zero Modes and braiding statistics for fault tolerance.
• Neutral Atoms: src/qpu/neutral_atom.rs provides analog Hamiltonian control for Pasqal and QuEra Rydberg arrays.

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🛡️ Reliability Engineering (SRE)

Hardware-Aware Coherence Verification

Before submission, circuits undergo a physics-based pre-flight check. The estimated circuit duration $\tau_{circ}$ is compared against the device's $T_1$ coherence time.

$$ \text{Status} = \begin{cases} \text{APPROVE} & \text{if } \tau_{circ} < 0.5 \times T_1 \ \text{REJECT} & \text{otherwise} \end{cases} $$

graph TD
    Start[Job Request] --> Calc[Estimate Duration]
    Calc --> Query{Query Hardware T1}
    Query -->|Safe Margin| Run[Execute Job]
    Query -->|Decoherence Risk| Abort[Abort & Log]
    
    style Pass fill:#9f9,stroke:#333
    style Fail fill:#f99,stroke:#333

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🛠️ Technical Stack

Domain	Technology	Feature Implementation
Orchestration	Rust (Tokio, Actix concepts)	Low-latency event loop, actor concurrency
Algorithmics	Python (Qiskit, NumPy)	Circuit transpilation, pulse scheduling
Simulation	Stochastic SDEs	Heston Volatility Model implementation
Security	Dilithium	NIST FIPS 204 Digital Signatures
Verification	LTL Automata	Runtime safety invariant formulation

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🔮 Roadmap (Future Horizons)

• Real-Time Error Correction: Integration of real-time syndrome decoding on FPGA control hardware.
• Quantum Machine Learning (QML): Variational Quantum Classifier (VQC) for market regime detection.
• Distributed Quantum Computing: Entanglement distribution protocols for multi-QPU strategies.
• Zero-Knowledge Proofs: Quantum-safe ZKP for private strategy verification.

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Built by Sandeep Kumar Sahoo Copyright © 2025