Adaptive Genetic Algorithms for Pulse-Level Quantum Error Mitigation
This work addresses noise challenges in quantum computing to improve circuit performance, but it appears incremental as it builds on existing error mitigation techniques.
The paper tackles the problem of noise in quantum computing by introducing an adaptive algorithm for pulse-level quantum error mitigation, which dynamically responds to noise conditions to enhance fidelity without modifying circuit gates, as demonstrated by applying it to Grover's and Deutsch-Jozsa algorithms.
Noise remains a fundamental challenge in quantum computing, significantly affecting pulse fidelity and overall circuit performance. This paper introduces an adaptive algorithm for pulse-level quantum error mitigation, designed to enhance fidelity by dynamically responding to noise conditions without modifying circuit gates. By targeting pulse parameters directly, this method reduces the impact of various noise sources, improving algorithm resilience in quantum circuits. We show the latter by applying our protocol to Grover's and Deutsch-Jozsa algorithms. Experimental results show that this pulse-level strategy provides a flexible and efficient solution for increasing fidelity during the noisy execution of quantum circuits. Our work contributes to advancements in error mitigation techniques, essential for robust quantum computing.