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

• Published in: Nature communications Vol. 15; no. 1; pp. 7491 - 12
• Main Authors: Hu, Fangjun, Khan, Saeed A., Bronn, Nicholas T., Angelatos, Gerasimos, Rowlands, Graham E., Ribeill, Guilhem J., Türeci, Hakan E.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.08.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/766/259; 639/766/483/481; Algorithms; Coherence; Computers; Distance learning; Fault tolerance; Humanities and Social Sciences; Inference; Learning algorithms; Machine learning; Microprocessors; multidisciplinary; Noise monitoring; Quantum computers; Quantum computing; Quantum theory; Qubits (quantum computing); Sampling; Science; Science (multidisciplinary); Time measurement

The practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables inference on temporal data over durati...