This run was recorded in April 2026 against a VQE kernel that has since been replaced by phase estimation. The shot distribution below is a real measurement, but it is a measurement of a program that no longer exists in this repository, and it has not been re-run against the current kernel.

Upgraded to QPE for molecular binding energy Hamiltonians. A quantum advantage in drug discovery would need strongly correlated electronic structure, such as some transition states and metal-containing active sites, that DFT and coupled cluster cannot treat reliably, and QPE is a candidate there only if a state with good ground-state overlap can be prepared. Our instance is a labelled toy model.

Problem 07 · Quantum-Assisted Drug Discovery

Overview

Drug discovery requires exploring vast chemical design spaces to identify ligands with strong binding affinity and favorable pharmacokinetics. Quantum computing promises more accurate electronic-structure evaluation and molecular similarity search. This problem pairs a classical baseline that scores ligand–protein interaction energy using coarse-grained Lennard-Jones plus Coulomb terms with a Q# project whose hardware kernel runs quantum phase estimation on small active-site models.

Directory Layout

07_drug_discovery/
├── estimates/                  # JSON artifacts from classical/quantum workflows
├── instances/                  # Molecule parameter sets (small/medium/large)
├── plots/                      # Generated figures from analyze.py
├── python/
│   ├── classical_baseline.py   # Deterministic scoring of ligand poses
│   └── analyze.py              # Visualization of energy histograms & rankings
└── qsharp/
    ├── qsharp.json            # Modern QDK project file
    └── Program.qs              # Placeholder quantum workflow

Quick Start

cd problems/07_drug_discovery

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

# Visualize score distributions and top candidates
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. Fragment Encoding – Map small active-site Hamiltonians (H₂, LiH, minimal basis) into qubit Hamiltonians.

2. Deeper QPE – Extend the phase register and Trotter depth toward chemically relevant precision.

3. Pose Re-ranking – Combine quantum energy estimates with classical docking scores.

4. Resource Estimation – Benchmark fault-tolerant requirements for chemically relevant precision.

This scaffold keeps the classical baseline reproducible while we iterate toward genuine quantum chemical modeling. 🧪⚛️

Objective Maturity Gate

• **Current gate**: **Stage C complete** (hardware-aware validation evidence in place: QPE binding-energy kernel runnable, calibration ensemble across runs, backend assumptions documented, dual-model estimator profile, OpenQASM export for cross-platform validation, Azure smoke validation).
• **Next gate target**: **Stage D** (advantage evidence package: fairness review against DFT/coupled-cluster baselines, residual risks for naturally-quantum molecular Hamiltonians, claim category locked).

Stage C evidence references for this problem:

• Calibration ensemble: `estimates/quantum_calibration_ensemble.json` and `estimates/calibration_evidence.json`.
• Backend assumptions: `estimates/backend_assumptions.md`.
• Estimator profile (ns-e3 + surface-code-generic): `estimates/estimator_profile_summary.md`, `estimates/latest_qubit_gate_ns_e3.json`, `estimates/latest_surface_code_generic_v1.json`.
• Cross-platform OpenQASM export: `estimates/vqe_binding.qasm`.
• Azure smoke validation: `estimates/azure_smoke_report_small_d1.md`, `estimates/azure_job_manifest_small_d1.json`.

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.