HyQOS

HyQOS (Hybrid Quantum Optimization System) is a decision-first hybrid optimization framework that combines classical solvers with selective quantum execution.
Instead of defaulting to quantum computation, HyQOS evaluates problem characteristics and escalates to quantum methods only when they are likely to provide an advantage.

This design makes HyQOS practical, efficient, and compatible with near-term quantum simulators and NISQ-era hardware.

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Key Idea

Most hybrid quantum systems are quantum-first.
HyQOS is decision-first.

1. Analyze the optimization problem
2. Estimate difficulty and structure
3. Solve classically when sufficient
4. Escalate to quantum solvers only when justified

This avoids unnecessary quantum overhead while preserving quantum advantage where it matters.

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Project Structure

HyQOS/ ├─ core/ │ ├─ classical_solver.py │ ├─ decision_engine.py │ ├─ difficulty_model.py │ ├─ feature_extractor.py │ ├─ quantum_solver.py │ └─ workload_generator.py ├─ demo/ ├─ evaluation/ ├─ specs/ │ └─ architecture.md └─ README.md

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Core Components

• Decision Engine
  Determines whether a problem should be solved classically or escalated to quantum execution.

• Classical Solver
  Handles low-to-medium complexity optimization tasks efficiently.

• Quantum Solver
  Uses parameterized quantum circuits and samplers for hard optimization cases.

• Difficulty Model
  Estimates problem hardness to guide solver selection.

• Feature Extractor & Workload Generator
  Prepares and characterizes optimization problems.

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Technology Stack

• Python 3.11
• NumPy
• SciPy
• Qiskit
• Qiskit Aer

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Status

HyQOS is currently in active development.
The architecture is stable, and core modules are implemented. Future work includes benchmarking, expanded problem classes, and real hardware execution.

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Documentation

• Detailed system design: specs/architecture.md

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