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Overcoming the coherence time barrier in quantum machine learning on temporal data

Engineering and Technology

Overcoming the coherence time barrier in quantum machine learning on temporal data

F. Hu, S. A. Khan, et al.

Discover how NISQRC, a groundbreaking machine learning algorithm developed by Fangjun Hu, Saeed A. Khan, Nicholas T. Bronn, Gerasimos Angelatos, Graham E. Rowlands, Guilhem J. Ribeill, and Hakan E. Türeci, allows qubit-based quantum systems to perform inference on temporal data without being hindered by coherence time. This innovative approach leverages mid-circuit measurements to maintain persistent temporal memory, showcasing its prowess through successful experiments on a 7-qubit quantum processor.

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Playback language: English
Abstract
This paper introduces NISQRC, a machine learning algorithm designed for qubit-based quantum systems to perform inference on temporal data without coherence time limitations. NISQRC uses mid-circuit measurements and deterministic reset operations to minimize circuit executions while maintaining persistent temporal memory. The algorithm's effectiveness is validated through simulations and experiments on a 7-qubit quantum processor, successfully recovering arbitrarily long test signals unaffected by coherence time constraints. The paper also presents a Quantum Volterra Theory for analyzing the memory properties of quantum systems.
Publisher
Nature Communications
Published On
Aug 30, 2024
Authors
Fangjun Hu, Saeed A. Khan, Nicholas T. Bronn, Gerasimos Angelatos, Graham E. Rowlands, Guilhem J. Ribeill, Hakan E. Türeci
Tags
NISQRC
machine learning
quantum systems
temporal data
mid-circuit measurements
quantum Volterra Theory
coherence time

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