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Benchmark methodology for comparing quantum ground state preparation algorithms (cooling, adiabatic, QAOA) under realistic noise conditions. Provides phase-dependent performance analysis using quadratic fermionic Hamiltonians with depolarizing noise.
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| name | quantum-ground-state-preparation-benchmark |
| description | Benchmark methodology for comparing quantum ground state preparation algorithms (cooling, adiabatic, QAOA) under realistic noise conditions. Provides phase-dependent performance analysis using quadratic fermionic Hamiltonians with depolarizing noise. |
Benchmark methodology for comparing quantum ground state preparation algorithms under realistic noise conditions. Based on arXiv:2606.20551 (Molpeceres et al., 2026), which derives scaling laws for achievable relative energy as a function of noise rate across cooling, adiabatic, and optimization algorithms. Key finding: algorithm performance depends critically on the quantum phase — adiabatic evolution dominates in the trivial phase while multi-frequency cooling is superior in the topological phase where gap-closing limits adiabatic protocols.
| Algorithm | Best Phase | Noise Robustness | Key Advantage |
|---|---|---|---|
| Adiabatic Evolution | Trivial | Moderate | Conceptual simplicity |
| Multi-Frequency Cooling | Topological | High | Gap-closing resilience |
| QAOA | Trivial | Low-Moderate | Competitive with cooling in trivial phase |
Step 1: Define Hamiltonian Family
Step 2: Model Noise
Step 3: Run Each Algorithm
Step 4: Evaluate Metrics
The critical insight: performance depends on the quantum phase:
For quadratic fermionic models with depolarizing noise:
