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17. Nuclear Physics

Stage C - QPE (upgraded from VQE)

QPE for the deuteron (pionless EFT, two-state basis, 10 phase bits). 667k physical qubits, 35 logical. Reproduces the published -1.749 MeV; the basis is too small to reach the experimental -2.224 MeV.

Algorithm
QPE Deuteron Binding Energy
Logical Qubits
5
Framework
Modern QDK (qsharp 1.31.0)

Resource Estimation (Azure Quantum RE)

Physical Qubits
667.0k
Logical Qubits
35
T-Gates
27
Rotations
131.1k
Runtime
21.2s

Resource Breakdown

Calibration Evidence (20-Run Ensemble)

Mean Value
-1.734
95% CI
undefined
Runs
20
Std Dev
0.000

Every run returned the same value. The interval is a normal approximation, which has zero width when the runs do not vary, so it does not bound the mean; it is left undefined rather than shown as ± 0.

Noise Resilience (Depolarizing Simulation)

Ideal outcome: [One, Zero] (70% probability)

p = 0.001
98.1%
p = 0.01
81.0%
p = 0.05
79.5%

Cross-Platform Emulator Results (100 shots)

These histograms predate the current kernel

This run was recorded in April 2026 against a VQE kernel that has since been replaced by phase estimation.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.

Quantinuum H2-1E
[1, 1]
56% of shots
Rigetti QVM
[1, 1]
52% of shots
✓ Cross-platform agreement: both simulators find the same dominant outcome
H2-1E Distribution
Rigetti QVM Distribution

Troyer Utility-Scale Classification

Quantum Simulation (Native Advantage Potential)
Quantum Speedup
Exponential over exact diagonalization if the initial state overlaps the ground state well; not established against the best classical methods
Classical Competitor
Nuclear shell model / lattice QCD
Honest Assessment (Troyer Framework)

Upgraded to QPE for a nuclear EFT Hamiltonian, the deuteron model of Dumitrescu et al. (PRL 120, 210501, 2018). A quantum advantage would need heavier nuclei, where shell-model truncation becomes severe, and QPE is a candidate there only if a state with good ground-state overlap can be prepared.

These two numbers describe different programs

The resource estimate below is for Main.NuclearQPE(10, 1), the algorithm at utility scale. The Azure Quantum runs executed NuclearQPEKernel(), a small kernel sized to fit today's hardware. Qubit counts and runtimes from the estimate are not measurements of the run, and the run histogram is not a sample of the estimated circuit.

Pareto Frontier: Qubits vs Runtime

The estimator returns 4 viable configurations, not one answer. Every headline figure on this page comes from the leftmost point, which uses the fewest qubits and is therefore also the slowest. Buying 1.5x the qubits cuts runtime to 0.76x.

qubit_gate_ns_e3 with surface_code, error budget 1e-3. Points are Pareto-optimal: no other configuration is better on both axes.

Multi-Model Resource Comparison

Physical qubit requirements across 6 qubit technologies × 2 QEC schemes (inspired by Troyer Architecture Series, Part 3).

■ Gate-based + Surface code (blue)
Qubit ModelQECPhysical QubitsLogical Qubits
Superconducting (ns, 1e-4)surface_code43,99535
Trapped Ion (μs, 1e-4)surface_code43,99535
Superconducting (ns, 1e-3)surface_code666,99535
Trapped Ion (μs, 1e-3)surface_code666,99535

Problem Documentation

Problem 17 · Quantum Nuclear Physics

Overview

Simulating few-nucleon systems requires capturing strong-interaction dynamics governed by Quantum Chromodynamics (QCD). This scaffold provides a reproducible classical baseline using a pionless effective-field-theory (EFT) toy Hamiltonian solved via exact diagonalization, while preparing a Q# project for future adiabatic state preparation and Trotterized time evolution. The goal is to compare classical bound-state energy estimates with quantum-inspired workflows.

Directory Layout


17_nuclear_physics/
├── estimates/                        # JSON artifacts from classical / quantum workflows
├── instances/                        # EFT coupling grids and cutoff parameters
├── plots/                            # Generated figures from analyze.py
├── python/
│   ├── classical_baseline.py         # Few-body EFT Hamiltonian builder + diagonalization
│   └── analyze.py                    # Visualization of binding energies and scattering lengths
└── qsharp/
    ├── qsharp.json            # Modern QDK project file
    └── Program.qs                    # Stubbed quantum workflow

Quick Start


cd problems/17_nuclear_physics

# Classical EFT baseline
python python/classical_baseline.py

# Plot binding energies and coupling 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. Hamiltonian Encoding – Map low-energy nucleon interactions into qubit-friendly operators.

2. Adiabatic State Prep – Implement adiabatic ramps or variational ansätze for ground-state estimation.

3. Phase Estimation / Spectroscopy – Use quantum phase estimation to extract binding energies.

4. Resource Estimation – Track qubit counts and Trotter step complexity across cutoff scales.

This scaffold keeps the EFT baseline reproducible while we work toward quantum-enhanced nuclear simulations. ⚛️🪐

Objective Maturity Gate

  • **Current gate**: **Stage C complete** (hardware-aware validation evidence in place: QPE deuteron 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 shell-model/lattice-QCD baselines for >20-nucleon systems, residual risks for naturally-quantum nuclear forces, 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_deuteron.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.

Reproduce It

cd problems/17_nuclear_physics make classical # Run classical baseline make analyze # Generate plots make build # Validate Q# compilation make run # Run Q# entry point

Key Files

  • qsharp/src/Main.qs Quantum algorithm implementation
  • qsharp/HardwareKernel.qs Azure-submittable QIR kernel
  • python/classical_baseline.py Classical reference implementation
  • estimates/classical_baseline.json Baseline metrics
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