Fault-Tolerant Quantum Machine Learning

Yasunari Suzuki

Neha Bhati, Aradhya Pokhriyal, Abeer Saber

Rapid progress in quantum computing and machine learning has unlocked new possibilities for addressing complex issues. However, fault tolerance difficulties frequently impede the practical implementation of quantum ML (QML) algorithms. Algorithmic exploration of unveiling fault tolerance in QML aims to find and create fault-tolerant algorithms specifically suited to QML use cases. The techniques are designed to reduce the impact of quantum errors, making QML models more stable and trustworthy. An increase in fault tolerance has been observed in preliminary tests, with no adverse effects on QML’s computing performance or accuracy. This work not only adds to the understanding of fault-tolerant quantum algorithms but also lays the way for the widespread use of QML in areas as varied as finance, medicine, and security.

Alexander M. Dalzell, Sam McArdle, Mario Berta +10 more

Yunfei Wang, Junyu Liu

Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. This includes techniques used in Noisy Intermediate-Scale Quantum (NISQ) technologies and approaches for algorithms compatible with fault-tolerant quantum computing hardware. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning.

I. S. Osborne

Bisma Majid, Shabir Ahmad Sofi, Zamrooda Jabeen