AI Article Synopsis

  • Quantum computing offers capabilities that surpass classical computers, but current quantum machines produce noisy outputs, complicating result accuracy.
  • This study assesses how noise affects bit string sampling and its consequences for optimization and machine learning tasks.
  • By quantifying the sampling overhead and relating it to layer fidelity, the research establishes provable bounds on noise-free expectation values, validated through experiments on quantum computers with up to 127 qubits, showing good alignment with theoretical outcomes.*

Article Abstract

Quantum computing has emerged as a powerful computational paradigm capable of solving problems beyond the reach of classical computers. However, today's quantum computers are noisy, posing challenges to obtaining accurate results. Here, we explore the impact of noise on quantum computing, focusing on the challenges in sampling bit strings from noisy quantum computers and the implications for optimization and machine learning. We formally quantify the sampling overhead to extract good samples from noisy quantum computers and relate it to the layer fidelity, a metric to determine the performance of noisy quantum processors. Further, we show how this allows us to use the conditional value at risk of noisy samples to determine provable bounds on noise-free expectation values. We discuss how to leverage these bounds for different algorithms and demonstrate our findings through experiments on real quantum computers involving up to 127 qubits. The results show strong alignment with theoretical predictions.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11578887PMC
http://dx.doi.org/10.1038/s43588-024-00709-1DOI Listing

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