QEC is enabling infrastructure, not an application. The repetition code demonstrates error detection but is not a path to utility. Surface codes and beyond are needed for fault-tolerant computation. See errorcorrectionzoo.org for comprehensive code taxonomy.

Problem 16 · Quantum Error Correction

Overview

Fault-tolerant quantum computing demands error correction codes that suppress noisy qubit failures faster than they accumulate. This scaffold supplies a reproducible classical baseline for repetition-code style logical error analysis while standing up a Q# project that will ultimately host stabilizer simulations and surface-code primitives. By comparing physical error rates against logical failure probabilities we track where quantum error correction (QEC) begins to pay off.

Directory Layout

16_error_correction/
├── estimates/                       # JSON artifacts from classical / quantum workflows
├── instances/                       # Code distances and physical error-rate sweeps
├── plots/                           # Generated figures from analyze.py
├── python/
│   ├── classical_baseline.py        # Analytical repetition-code logical error model
│   └── analyze.py                   # Visualization of suppression factors and pseudo-thresholds
└── qsharp/
    ├── qsharp.json            # Modern QDK project file
    └── Program.qs                   # Stubbed quantum workflow

Quick Start

cd problems/16_error_correction

# Classical logical error analysis
python python/classical_baseline.py

# Plot logical error curves and suppression heatmaps
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. Stabilizer Simulation – Implement syndrome extraction cycles for repetition or surface codes.

2. Decoder Integration – Explore minimum-weight matching or belief propagation within Q#.

3. Resource Accounting – Track qubit counts, circuit depth, and ancilla demand per cycle.

4. Fault-Tolerant Benchmarks – Compare logical error suppression across physical noise models.

This scaffold keeps the analytical QEC baseline reproducible while we build toward full stabilizer simulations in Q#. 🛡️⚛️

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.