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An open-source, software-agnostic analyzer and optimizer for hybrid quantum-classical projects. The system inspects a user's quantum codebase and answers:

Which planned quantum operations actually need QPU hardware?

Core distinction

Existing hybrid quantum systems often solve: Where should task T execute?

WestQuant solves the deeper problem: Should task T require a QPU at all?

This is Semantic QPU Minimization.

Quick start

pip install westquant-qcsc

Audit a project

westquant audit ./my_quantum_project

Output:

westquant_report.html    # Full HTML report
westquant_report.md      # Markdown report
westquant_plan.json      # Machine-readable plan (SAFE/BALANCED/AGGRESSIVE)
westquant_workflow.json  # WQFlow IR graph

QPU Budget Predictor

Estimate the required QPU budget from problem structure — before any quantum execution:

from qcsc import QPUBudgetPredictor, ProblemProfile

profile = ProblemProfile(
    problem="MaxCut",
    n_qubits=16,
    p=3,
    graph_family="GEO",
    quality_target=0.95,
)
predictor = QPUBudgetPredictor()
budget = predictor.estimate(profile)

print(f"Shots: {budget.recommended_shots:,}")
print(f"Reduction vs naive: {budget.reduction_vs_naive:.0f}x")
print(f"Estimated quality: {budget.estimated_quality:.4f}")
print(f"Strategies: {budget.strategies}")

Or from the CLI:

westquant budget --problem MaxCut --qubits 16 --depth 3 --family GEO --compare

Output:

  [B0_naive]
    Shots:         2,119,680
    Est. quality:  0.9961

  [B1_best_practice]
    Shots:         1,024
    Est. quality:  0.9961
    vs naive:      2070.0x reduction

  [B2_classical]
    Shots:         0
    Est. quality:  1.0000

The predictor is calibrated from 12,600 exact simulation results across 6 problems, 7 graph families, and depths p=1,2,3. See Paper A for the underlying research.

What it detects

ID	Transformation	Guarantee	Description
T01	Redundant Measurement Bases	EXACT	Observables obtainable from same basis
T02	Measurement Reuse	EXACT	Observables recoverable from existing samples
T03	Commuting Measurement Grouping	EXACT	Group compatible Pauli observables
T04	Shot Reduction Opportunity	APPROXIMATE	Detect high shot budgets, propose ablation
T05	Adaptive Shot Allocation	APPROXIMATE	Variable shot budgets by optimization stage
T06	Simulator Pretraining	APPROXIMATE	Pretrain variational parameters on simulator
T07	Parameter Transfer	APPROXIMATE	Transfer params between structurally similar instances
T08	Repeated-Circuit Caching	EXACT	Cache identical circuit+parameter submissions
T09	Graph/Problem Preprocessing	EXACT	Classical graph reduction (components, symmetry)
T10	Workload Packing	EXPERIMENTAL	Pack independent circuits on larger QPU
T11	Candidate Pruning	APPROXIMATE	Surrogate filtering for large parameter sweeps
T12	Circuit Cutting Candidate	EXACT	Identify cuttable interaction graphs
T13	CPU/GPU Simulator Routing	EXACT	Recommend best simulator class
T14	Circuit Architecture Reduction	EXACT	Remove mathematically redundant gates
T15	Quantum/Classical Decomposition	EQUIVALENT	Split quantum sampling from classical post-processing

Three plans

• SAFE: Only EXACT transformations. Mathematical result preserved.
• BALANCED: EXACT + EQUIVALENT + low-risk APPROXIMATE. Requires tolerance ε.
• AGGRESSIVE: All transformations including EXPERIMENTAL. Always shows uncertainty.

Architecture

qcsc/
├── qpu_budget.py              # QPU budget predictor (Paper A integration)
├── flow_ir/                    # WQFlow IR — directed graph of hybrid workflows
├── resource_model/             # QPU backend + local compute capabilities
├── transformation_registry/    # 15 QPU-saving transformations (T01-T15)
├── analyzer/                   # Framework-specific source analysis (Qiskit first)
├── detectors/                  # Detectors that produce recommendations
├── planner/                    # Pareto planner (SAFE/BALANCED/AGGRESSIVE)
├── reporting/                  # HTML + Markdown + JSON report generation
├── benchmark/                  # Benchmark projects with known opportunities
├── cost_model.py               # QPU cost estimation
├── closed_loop.py              # Closed-loop measurement tracking
├── learned_policy.py           # Learned policy for transformation ranking
├── patch_generator.py         # Advisory patch generation
├── runner.py                   # Supervised plan execution
└── cli.py                      # CLI entry point

Design philosophy

WestQuant asks three questions in order:

1. Does this operation need to happen? (Eliminate)
2. Does it need to be quantum? (Replace with classical)
3. Only then: which resource should execute it? (Schedule)

That ordering is the defining idea of the project.

CUDA-Q and cuQuantum

Version 0.1.1 adds advisory capability records for CUDA-Q CPU/GPU targets and cuStateVec, cuTensorNet, cuDensityMat, cuPauliProp, and cuStabilizer. Install westquant-qcsc[cudaq] to compose QPU minimization with westquant-cudaq. The integration is domain-neutral and does not include vertical-specific scientific methods.

V0.1 — Advisory only

V0.1 performs static analysis + cost estimation + recommendations. It does NOT:

• modify user code
• execute user code
• submit jobs to hardware
• automatically rewrite experiments

Community

• Discussions: Join the conversation
• Contributing: See CONTRIBUTING.md
• Code of Conduct: See CODE_OF_CONDUCT.md

License

MIT