Enhancing Quantum Memory Lifetime with Measurement-Free Local Error Correction and Reinforcement Learning
Mincheol Park, Nishad Maskara, Marcin Kalinowski, Mikhail D. Lukin

TL;DR
This paper introduces a measurement-free, local error correction method for quantum memory that uses reinforcement learning to optimize circuit design, significantly extending logical qubit lifetime in noisy environments.
Contribution
It develops a reinforcement learning framework to optimize local error correction circuits that do not rely on mid-circuit measurements, improving quantum memory stability.
Findings
Optimized LEC circuits outperform traditional Toom's rule in extending qubit lifetime.
The approach reduces mid-circuit readouts while maintaining memory integrity.
Application potential in dissipative topological state preparation.
Abstract
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 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 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…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Neural Networks and Reservoir Computing · Quantum Information and Cryptography
MethodsSparse Evolutionary Training
