Projects With Keyword Quantum Computing

1 Introduction:

Quantum computers must detect and correct physical errors without directly measuring the encoded quantum state. Surface codes address this problem by repeatedly measuring local stabilizers on a two-dimensional qubit lattice. Changes in those measurements, called detection events, reveal where an error may have occurred, but they do not identify a unique physical-error pattern. A decoder must instead choose any correction that returns the state to the correct logical equivalence class [1]. Minimum-weight perfect matching (MWPM) is a strong standard decoder, while recent neural decoders show that learned models can exploit spatial, temporal, and device-specific error correlations [2, 3].

This project will test one narrow question: does preserving a neural model’s uncertainty about physical errors improve surface-code decoding? A spatiotemporal neural network will map repeated syndrome measurements to per-qubit probabilities for the X and Z components of Pauli errors. A second, syndrome-consistent correction stage will receive those probabilities rather than a binary error map. We will compare this soft-information pipeline with MWPM, a direct neural decoder, and an otherwise matched model whose probabilities are thresholded before correction. On the same held-out syndromes, we will assess probability calibration, logical error rate, and end-to-end single-shot decoder latency under baseline, shifted-rate, and correlated noise.

1.1 Intellectual Merit:

The project will isolate the value of uncertainty through controlled ablation rather than evaluating only whether a neural decoder can correct errors. It will distinguish improvements caused by retaining probability information from improvements caused by model size, a learned syndrome representation, or a strong conventional decoding stage. The study will also compare physical- error inference metrics with the logically meaningful outcome, testing whether better per-qubit predictions actually produce fewer logical failures.

1.2 Broader Impact:

Reliable decoding is necessary for useful fault-tolerant quantum computation, including future quantum-machine-learning workloads. A reproducible benchmark showing when soft neural infor- mation helps, has no effect, or hurts would help researchers avoid judging decoders by misleading physical-error accuracy alone. The data pipeline, ablation protocol, and logical-error analysis could also be reused by future QLab projects studying other codes, noise models, or quantum devices.