The effect of Quantum Time Crystal Computing to Quantum Machine Learning methods

TL;DR

This paper explores how Quantum Time Crystals can be exploited for quantum computing, demonstrating their impact on the accuracy of various quantum machine learning models and suggesting potential for quantum error mitigation.

Contribution

It introduces Quantum Time Crystal Computing as a novel method to control external noise in quantum systems and evaluates its effects on different quantum machine learning algorithms.

Findings

Quantum Time Crystal Computing lowers the accuracy of Quantum Reservoir Computing.

It improves the accuracy of Quantum Neural Network and Variational Quantum Kolmogorov-Arnold Network.

Potential implications for quantum error mitigation strategies.

Abstract

Many body localization shows the robustness for external perturbations and time reversal symmetry on Time Crystal. This Time Crystal prolongs the coherence time, hence, it is used for quantum computers as qubits. Therefore, we established the method to exploit Time Crystals for quantum computing by controlling external noise called Quantum Time Crystal Computing and demonstrated solving the problem of generating correct waves using Quantum Reservoir Computing, and fitting of given function using Quantum Neural Network and Variational Quantum Kolmogorov-Arnold Network. As a consequence, we revealed that Quantum Time Crystal Computing lower the accuracy of Quantum Reservoir Computing and improved the accuracy of Quantum Neural Network and Variational Quantum Kolmogorov-Arnold Network. This result may be the one of milestones of Quantum Error Mitigation as the case that noise improves the accuracy of Quantum Machine Learning.