comet-constraint-preserving-qaoa

name	comet-constraint-preserving-qaoa
description	Constraint-preserving QAOA using XY-mixer for multiplex CRISPR gene editing optimization. Systematically compares structural constraint enforcement (XY-mixer) vs penalty-based approaches across simulator and real hardware. XY-mixer achieves >95% optimum probability by p=3 vs <6% for penalty variants. Activation: constraint-preserving QAOA, XY-mixer, CRISPR optimization, QUBO constraint enforcement, quantum gene editing, 约束保持QAOA
metadata	{"arxiv_id":"2607.02622","published":"2026-07-02","authors":"COMET authors","tags":["qaoa","constraint-preservation","xy-mixer","crispr","combinatorial-optimization"]}

COMET: Constraint-Preserving QAOA

Core Methodology

Problem: Multiplex CRISPR-Cas9 gene editing requires selecting one guide RNA per target gene — a constrained combinatorial problem. Conventional QAOA uses quadratic penalty terms to enforce constraints, but penalty coefficient selection is heuristic and penalties amplify hardware noise.

Solution: Enforce constraints structurally via XY-mixer instead of penalty terms.

Key Results (3-gene, 12-qubit instance)

Method	Simulator (p=3)	Hardware Gap
XY-mixer	>95% optimum prob
Penalty (best λ)	<6% optimum prob

Findings:

• XY-mixer preserves feasibility by construction — no penalty tuning needed
• Penalty variants span order of magnitude in coefficient, all underperform
• On real hardware (ibm_kingston, Heron r2): XY-mixer simulator-hardware gap stays within |0.8|
• Structural guarantee partially breaks under gate-level noise — honest accounting provided

Usage Patterns

Pattern 1: Constraint-Preserving QAOA Design

When formulating constrained QUBO problems for QAOA:

1. Identify one-hot constraints (e.g., exactly-one-per-group)
2. Replace penalty Hamiltonian with XY-mixer
3. Mixer preserves feasible subspace by construction — no penalty coefficient tuning
4. Compare against penalty baseline across λ values

Pattern 2: Penalty vs Mixer Comparison

When evaluating constraint enforcement strategies:

1. Test penalty method across order of magnitude in penalty coefficient (λ)
2. Test XY-mixer with same QAOA depth
3. Measure: optimum probability, simulator-hardware energy gap
4. Note: penalty tuning is heuristic; mixer is principled

Pattern 3: Hardware Validation

When validating on real quantum hardware:

1. Run both penalty and mixer variants at same depths
2. Measure simulator-hardware energy gap
3. Account for gate-level noise breaking structural guarantees
4. Report honest hardware performance, not just simulator results

Activation Keywords

• constraint-preserving QAOA
• XY-mixer quantum optimization
• CRISPR gene editing optimization
• QUBO constraint enforcement
• penalty-free quantum optimization
• quantum combinatorial optimization
• 约束保持QAOA
• XY混合器

Related Skills

• qaoa-xy-mixers-portfolio — XY-mixers for portfolio optimization (same constraint technique)
• penalty-free-quantum-annealing-portfolio — penalty-free optimization
• qaoa-optimization — general QAOA methodology
• qaoa-manifold-optimization — QAOA optimization techniques
• qaoa-zne-portfolio — QAOA with error mitigation