Introduction to QCOS Core

What is QCOS Core?

QCOS Core is the Quantum Circuit Optimization System - a production-ready platform for optimizing variational quantum algorithms (VQA) with unprecedented efficiency.

The Problem

Traditional quantum optimization approaches like VQE (Variational Quantum Eigensolver) and QAOA (Quantum Approximate Optimization Algorithm) suffer from:

• Excessive Evaluations: Thousands of circuit evaluations needed
• Noisy Results: Hardware noise degrades optimization quality
• Resource Waste: Expensive quantum hardware time wasted on suboptimal configurations
• Black Box: No visibility into optimization decisions

The QCOS Solution

QCOS Core addresses these challenges with:

Capability	Benefit
Adaptive Autopilots	Automatic algorithm selection based on problem scale
Surrogate Modeling	Reduce evaluations by 5-10x using ML surrogates
Multi-Backend Orchestration	Seamlessly switch between quantum providers
Glass-Box Transparency	Full visibility into every optimization decision
Evidence Trail	Cryptographic proof of computations for audit

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Key Concepts

Autopilots

Autopilots are scale-specific optimization strategies:

Autopilot	Scale	Strategy
micro	1-5 qubits	Direct optimization, exhaustive search
small	6-20 qubits	Gradient-based with noise mitigation
medium	21-50 qubits	Surrogate-assisted Bayesian optimization
large	51-100 qubits	Distributed optimization with checkpointing
hpc	100+ qubits	HPC cluster optimization (LUMI/Leonardo)

Evidence System

Every optimization produces cryptographic evidence:

{
  "evidence_id": "ev_abc123...",
  "evidence_hash": "sha256:...",
  "signature": "dilithium3:...",
  "timestamp": "2026-02-06T12:00:00Z",
  "backend": "ionq_simulator",
  "result": { ... }
}

Backend Abstraction

QCOS abstracts quantum hardware differences:

# Same code, different backends
result_ionq = client.optimize(backend="ionq_qpu")
result_ibm = client.optimize(backend="ibm_brisbane")
result_aws = client.optimize(backend="braket_sv1")

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Use Cases

1. Quantum Chemistry

Optimize molecular Hamiltonians with VQE:

from softqcos import VQEOptimizer

optimizer = VQEOptimizer(
    hamiltonian=h2_hamiltonian,
    ansatz="uccsd",
    backend="ionq_simulator"
)
result = optimizer.run()
print(f"Ground state energy: {result.energy}")

2. Combinatorial Optimization

Solve optimization problems with QAOA:

from softqcos import QAOAOptimizer

optimizer = QAOAOptimizer(
    problem=max_cut_graph,
    p_layers=3,
    backend="rigetti_qvm"
)
result = optimizer.run()
print(f"Best solution: {result.solution}")

3. Portfolio Optimization

Financial optimization with quantum advantage:

from softqcos import PortfolioOptimizer

optimizer = PortfolioOptimizer(
    assets=portfolio_data,
    risk_tolerance=0.1,
    backend="quantinuum_h1"
)
result = optimizer.run()
print(f"Optimal allocation: {result.weights}")

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Next Steps

1. Quick Start - Get running in 5 minutes
2. Installation - Detailed setup guide
3. API Overview - REST API reference
4. Python SDK - SDK documentation

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