This run was recorded in April 2026 against a quantum-walk kernel whose position never depended on its coin, rewritten in September 2026. 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.

Exciton transport in photosynthetic systems is inherently quantum, but the open-system dynamics of light-harvesting complexes of the size studied so far are routinely simulated classically, with hierarchical equations of motion or Lindblad master equations. An advantage would need systems large and strongly coupled enough to defeat those methods.

Problem 18 · Quantum Photovoltaics

Overview

Maximizing photovoltaic conversion efficiency hinges on balancing light absorption, carrier extraction, and recombination pathways. This scaffold pairs a reproducible classical baseline based on a simplified Shockley-Queisser style radiative limit with multi-junction heuristics, while reserving space for Q# experiments that explore excitonic transport and quantum-enhanced light harvesting. The objective is to compare classical efficiency projections against quantum-inspired proposals for coherent exciton management.

Directory Layout

18_photovoltaics/
├── estimates/                        # JSON artifacts from classical / quantum workflows
├── instances/                        # Bandgap selections, temperatures, recombination parameters
├── plots/                            # Generated figures from analyze.py
├── python/
│   ├── classical_baseline.py         # Shockley–Queisser style efficiency estimator
│   └── analyze.py                    # Visualization of efficiency and voltage trends
└── qsharp/
    ├── qsharp.json            # Modern QDK project file
    └── Program.qs                    # Stubbed quantum workflow

Quick Start

cd problems/18_photovoltaics

# Classical efficiency baseline
python python/classical_baseline.py

# Plot efficiency vs. bandgap and temperature trends
python python/analyze.py

# Quantum placeholder (uses modern QDK  qsharp Python package)
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. Excitonic Network Encoding – Map donor-acceptor lattices into qubit graphs for coherent transport studies.

2. Open Quantum Dynamics – Model phonon-assisted hopping using Lindblad style channels or variational ansätze.

3. Light-Harvesting Circuits – Prototype quantum walk or cavity-assisted absorption kernels.

4. Resource Estimation – Track qubit counts and Trotter depths against realistic cell architectures.

This scaffold keeps the classical photovoltaic baseline reproducible while we explore quantum coherence for next-generation solar materials.

Objective Maturity Gate

• **Current gate**: **Stage C complete** (hardware-aware validation evidence in place: quantum walk exciton-transport kernel runnable, calibration ensemble across runs, backend assumptions documented, dual-model estimator profile, OpenQASM export, Azure smoke validation).
• **Next gate target**: **Stage D** (advantage evidence package: fairness review against Lindblad master-equation baseline, residual risks for naturally-quantum open-system transport, claim category locked).

Stage C evidence references for this problem:

• Calibration ensemble: `estimates/quantum_calibration_ensemble.json`, regenerated on 2026-09-25 after the walk was corrected. Until then the conditional shift swapped the two position qubits whichever way the coin pointed, so the walker never depended on the coin and every run ended on site 1; `estimates/calibration_evidence.json` (2026-03-25) records that defect as 100% at site 1 across three runs.
• 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`. Both versions of the walk use two Toffoli-class gates and one rotation per step, so the resource estimate, which counts non-Clifford operations, is unchanged.
• Cross-platform OpenQASM export: `estimates/quantum_walk.qasm`, the same circuit as `qsharp/HardwareKernel.qs`.
• Azure smoke validation: `estimates/azure_smoke_report_small_d1.md`, `estimates/azure_job_manifest_small_d1.json` (2026-03-06, the kernel before the correction; the corrected kernel has not been submitted).

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.