Quantum error correction for the toric code using deep reinforcement learning

Philip Andreasson, Joel Johansson, Simon Liljestrand, Mats Granath

Quantum 2019 article cited by 94

Source Open-access PDF More in Physics & Astronomy

importance 5/10 partially solved Quantum Computing Algorithms and Architecture quantum computing stage: research

Problem

Correcting bit-flip errors on the topological toric quantum code requires an effective decoder.

Why it matters

Quantum error correction is essential for building reliable, fault-tolerant quantum computers.

What the AI did

A deep reinforcement learning agent used a convolutional neural network Q-function, trained with experience replay, to move defects on the torus and correct errors.

Results

Performance close to, and for small error rates asymptotically equivalent to, Minimum Weight Perfect Matching for code distances up to d=7.

Impact

Demonstrates deep RL can decode toric-code errors near the standard MWPM benchmark within tested limits.

Record

Venue	Quantum
Published	2019
Work type	article
Authors	Philip Andreasson, Joel Johansson, Simon Liljestrand, Mats Granath
Citations	94 (OpenAlex, at import time)
References	41
Model or method	deep convolutional neural network Q-function (deep reinforcement learning)
Organization	not stated in source
Primary source	https://doi.org/10.22331/q-2019-09-02-183
Open access	gold
Keywords	quantum error correction, toric code, deep reinforcement learning, Q-function

Importance rating

5 / 10

real but narrower advance

Decoding is a sub-problem of quantum error correction for fault-tolerant quantum computing; abstract gives no scale or impact figures, only future decoder potential.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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