# Enhancing Quantum Memory Lifetime with Measurement-Free Local Error Correction and Reinforcement Learning

arXiv: https://arxiv.org/abs/2408.09524 · HTML: https://mlsanity.com/paper/2408.09524/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 117 references that are in the map.
- First posted on arXiv 18 Aug 2024.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Deep Reinforcement Learning in Large Discrete Action Spaces](https://mlsanity.com/paper/1512.07679/) (2015)
- [Data-efficient Deep Reinforcement Learning for Dexterous Manipulation](https://mlsanity.com/paper/1704.03073/) (2017)
- [Quantum Architecture Search via Deep Reinforcement Learning](https://mlsanity.com/paper/2104.07715/) (2021)
- [Instabilities of Offline RL with Pre-Trained Neural Representation](https://mlsanity.com/paper/2103.04947/) (2021)
- [Curriculum reinforcement learning for quantum architecture search under hardware errors](https://mlsanity.com/paper/2402.03500/) (2024)
- [Foundations of Reinforcement Learning and Interactive Decision Making](https://mlsanity.com/paper/2312.16730/) (2023)
- [Optimizing Quantum Error Correction Codes with Reinforcement Learning](https://mlsanity.com/paper/1812.08451/) (2018)
- [Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation](https://mlsanity.com/paper/1810.07207/) (2018)
- [Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows](https://mlsanity.com/paper/2006.02979/) (2020)
- [Advantages of versatile neural-network decoding for topological codes](https://mlsanity.com/paper/1802.08680/) (2018)
- [Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers](https://mlsanity.com/paper/2311.16082/) (2023)
- [A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis](https://mlsanity.com/paper/2401.07054/) (2024)
- [Reinforcement learning-assisted quantum architecture search for variational quantum algorithms](https://mlsanity.com/paper/2402.13754/) (2024)
- [A scalable and fast artificial neural network syndrome decoder for surface codes](https://mlsanity.com/paper/2110.05854/) (2021)
- [Proximal Policy Optimization and its Dynamic Version for Sequence Generation](https://mlsanity.com/paper/1808.07982/) (2018)
- [The END: An Equivariant Neural Decoder for Quantum Error Correction](https://mlsanity.com/paper/2304.07362/) (2023)
- [Simulated annealing for optimization of graphs and sequences](https://mlsanity.com/paper/2110.01384/) (2021)
- [Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning](https://mlsanity.com/paper/2305.06378/) (2023)
- [An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks](https://mlsanity.com/paper/2310.05808/) (2023)
- [Benchmarking Machine Learning Models for Quantum Error Correction](https://mlsanity.com/paper/2311.11167/) (2023)
- [Qubit-Wise Architecture Search Method for Variational Quantum Circuits](https://mlsanity.com/paper/2403.04268/) (2024)
- [Neural Decoder for Topological Codes using Pseudo-Inverse of Parity Check Matrix](https://mlsanity.com/paper/1901.07535/) (2019)
- [Quantum circuit synthesis of Bell and GHZ states using projective simulation in the NISQ era](https://mlsanity.com/paper/2104.13297/) (2021)

## Built on by (most cited first, 1 of 1)

- [Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction](https://mlsanity.com/paper/2506.11552/) (2025)

## Abstract (arXiv)

Reliable quantum computation requires systematic identification and correction of errors that occur and accumulate in quantum hardware. To diagnose and correct such errors, standard quantum error-correcting protocols utilize textitglobal error information across the system obtained by mid-circuit readout of ancillary qubits. We investigate circuit-level error-correcting protocols that are measurement-free and based on textitlocal error information. Such a local error correction (LEC) circuit consists of faulty multi-qubit gates to perform both syndrome extraction and ancilla-controlled error removal. We develop and implement a reinforcement learning framework that takes a fixed set of faulty gates as inputs and outputs an optimized LEC circuit. To evaluate this approach, we quantitatively characterize an extension of logical qubit lifetime by a noisy LEC circuit. For the 2D classical Ising model and 4D toric code, our optimized LEC circuit performs better at extending a memory lifetime compared to a conventional LEC circuit based on Toom's rule in a sub-threshold gate error regime. We further show that such circuits can be used to reduce the rate of mid-circuit readouts to preserve a 2D toric code memory. Finally, we discuss the application of the LEC protocol on dissipative preparation of quantum states with topological phases.