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Curated by: Qiskit (180 videos)
Title: Machine Learning for Practical Quantum Error Mitigation Abstract: Quantum computers are now competing to surpass classical supercomputers in test-bed calculations, but errors limit their performance. Quantum error mitigation could overcome these errors, although in practice it can necessitate prohibitive computational overhead. Machine learning has been suggested as a solution to this problem, but its practical effectiveness remains uncertain. Here, we demonstrate that machine learning methods can be a key ingredient of quantum error mitigation in practice. We benchmark a variety of machine learning models---linear regression, random forests, multi-layer perceptrons, and graph neural networks---on diverse classes of quantum circuits and device noise profiles. For small-scale, simulable quantum circuits, machine learning models outperform a popular approach for quantum error mitigation (digital zero noise extrapolation) in both accuracy and runtime efficiency, even when applied to complex circuits and observables absent in training. To scale to large, classically intractable circuits, we demonstrate that machine learning methods can mimic other error mitigation methods---thereby reducing their overhead---through experiments on quantum hardware using 100 qubits. These results highlight the potential of classical machine learning for practical quantum computation. Bio: Haoran Liao is a senior Ph.D. candidate in Physics at University of California, Berkeley, where he is advised by K. Birgitta Whaley. He received B.Sc. in Mathematics and Physics from McGill University, Canada. He recently completed a research internship at IBM Quantum, under the supervision of Zlatko Minev, which resulted in the paper presented here.