Just-in-time Quantum Circuit Transpilation Reduces Noise

TL;DR

This paper introduces a just-in-time quantum circuit transpilation technique that measures noise immediately before execution, significantly improving the fidelity of quantum computations on NISQ devices compared to standard daily calibration methods.

Contribution

It presents a novel method for real-time noise measurement and adaptive circuit mapping, enhancing quantum result accuracy beyond existing calibration-based approaches.

Findings

Circuit fidelity improved by up to 400% with just-in-time calibration.

Real-time noise assessment benefits qubit mapping and reduces errors.

Average accuracy improvements range from 3% to 304%.

Abstract

Running quantum programs is fraught with challenges on on today's noisy intermediate scale quantum (NISQ) devices. Many of these challenges originate from the error characteristics that stem from rapid decoherence and noise during measurement, qubit connections, crosstalk, the qubits themselves, and transformations of qubit state via gates. Not only are qubits not "created equal", but their noise level also changes over time. IBM is said to calibrate their quantum systems once per day and reports noise levels (errors) at the time of such calibration. This information is subsequently used to map circuits to higher quality qubits and connections up to the next calibration point. This work provides evidence that there is room for improvement over this daily calibration cycle. It contributes a technique to measure noise levels (errors) related to qubits immediately before executing one or more sensitive circuits and shows that just-in-time noise measurements benefit late physical qubit mappings. With this just-in-time recalibrated transpilation, the fidelity of results is improved over IBM's default mappings, which only uses their daily calibrations. The framework assess two major sources of noise, namely readout errors (measurement errors) and two-qubit gate/connection errors. Experiments indicate that the accuracy of circuit results improves by 3-304% on average and up to 400% with on-the-fly circuit mappings based on error measurements just prior to application execution.