Adaptive channel estimation for mitigating circuits executed on noisy quantum devices

TL;DR

This paper proposes an adaptive method to estimate quantum channel parameters dynamically from noisy outputs, aiming to improve the stability and reproducibility of results on noisy quantum devices.

Contribution

It introduces a novel adaptive algorithm for real-time channel estimation to mitigate errors in NISQ quantum devices, enhancing reliability.

Findings

The adaptive method reduces the Hellinger distance to ideal distributions.

The approach demonstrates effectiveness on canonical quantum circuits.

Scalability of the method is discussed with respect to circuit complexity.

Abstract

Conventional computers have evolved to device components that demonstrate failure rates of 1e-17 or less, while current quantum computing devices typically exhibit error rates of 1e-2 or greater. This raises concerns about the reliability and reproducibility of the results obtained from quantum computers. The problem is highlighted by experimental observation that today's NISQ devices are inherently unstable. Remote quantum cloud servers typically do not provide users with the ability to calibrate the device themselves. Using inaccurate characterization data for error mitigation can have devastating impact on reproducibility. In this study, we investigate if one can infer the critical channel parameters dynamically from the noisy binary output of the executed quantum circuit and use it to improve program stability. An open question however is how well does this methodology scale. We discuss the efficacy and efficiency of our adaptive algorithm using canonical quantum circuits such as the uniform superposition circuit. Our metric of performance is the Hellinger distance between the post-stabilization observations and the reference (ideal) distribution.