Classical protein folding has been revolutionized by AI (AlphaFold). QAOA for lattice protein models has no proven advantage. The quantum approach would need to handle realistic all-atom models to compete.

Problem 08 · Quantum-Assisted Protein Folding

Overview

Protein folding encodes how linear amino-acid chains spontaneously organize into three-dimensional structures that dictate biological function. Quantum resources promise tighter coupling between electronic interactions and conformational search compared to classical heuristics. This scaffold provides a deterministic classical baseline using knowledge-based contact potentials while preparing a Q# project for future amplitude-encoded folding experiments and quantum Boltzmann sampling.

Directory Layout

08_protein_folding/
├── estimates/                  # JSON artifacts from classical/quantum workflows
├── instances/                  # Protein sequences with coarse contact maps
├── plots/                      # Generated figures from analyze.py
├── python/
│   ├── classical_baseline.py   # Knowledge-based scoring of contact maps
│   └── analyze.py              # Visual analytics for folding metrics
└── qsharp/
    ├── qsharp.json            # Modern QDK project file
    └── Program.qs              # Placeholder quantum workflow

Quick Start

cd problems/08_protein_folding

# Classical evaluation (writes estimates/classical_baseline.json)
python python/classical_baseline.py

# Visualize folding metrics
python python/analyze.py

# Quantum placeholder
 python -c "import qsharp; qsharp.init(project_root='qsharp'); print('Build OK')"
 python tooling/run_all_qsharp.py  # runs via qsharp Python package

Next Quantum Milestones

1. Amplitude Encoding – Load coarse-grained contact weights into amplitude registers for downstream energy estimation.

2. Quantum Boltzmann Sampling – Prototype a quantum-enhanced sampler over lattice conformations or fragment libraries.

3. Hybrid Refinement – Combine quantum-evaluated energies with classical gradient-based relaxations.

4. Resource Estimation – Benchmark logical qubits and T-depth for realistic fold sizes using the Azure Quantum Resource Estimator.

This scaffold keeps the classical baseline reproducible while we iterate toward chemistry-informed quantum folding simulations. 🧬⚛️

Objective Maturity Gate

• **Current gate**: **Stage B complete** (classical baseline and Q# scaffold/build path are in place).
• **Next gate target**: **Stage C** (hardware-aware validation with uncertainty-bounded comparisons).

Stage C exit criteria for this problem:

• Execute at least one non-placeholder quantum workflow path tied to the problem objective.
• Report uncertainty-bounded comparisons between classical and quantum outputs on `small` and `medium` instances.
• Document transpilation/connectivity and backend assumptions used for reported quantum runs.
• Add calibration/noise-sensitivity evidence for the reported quantum metrics.

DiVincenzo Readiness (Stage C/D Overlay)

| Criterion | Status | Evidence / Notes |

|---|---|---|

| Scalable qubit system | partial | Problem-scoped instance baselines are in place; full hardware-scale projections are tracked as Stage C work. |

| Initialization | partial | Input/state initialization path is defined for current workflows, with backend-ready loading fidelity still to be hardened. |

| Coherence vs gate time | not-yet | Backend-calibrated coherence-vs-depth evidence is pending and required for Stage C/D promotion. |

| Universal gate set | partial | Q# scaffold/build path exists; gate-basis decomposition and transpilation evidence remain Stage C tasks. |

| Qubit-specific measurement | partial | Measurement outputs are defined for current validation flows; hardware readout characterization is pending. |

Advantage Claim Contract

• **Claim category (current)**: `theoretical`.
• **Problem class and regime**: Problem-specific challenge instances defined in this directory.
• **Fair baseline**: Problem-local classical baseline in `python/` outputs.
• **Quantum resource scaling claim**: Expected asymptotic advantage depends on algorithm family and implementation assumptions; no hardware-demonstrated speedup claim yet.
• **Data-loading and I/O assumptions**: Must be documented alongside future advantage claims.
• **Noise/error model assumptions**: Backend-specific model and calibration assumptions to be added at Stage C.
• **Confidence/uncertainty method**: To be reported using shot-based confidence intervals or equivalent statistical bounds.
• **Residual risks**: Oracle/state-preparation/transpilation overhead may dominate for near-term instance sizes.