On the use of calibration data in error-aware compilation techniques for NISQ devices

TL;DR

This paper investigates how using calibration data affects quantum circuit fidelity on NISQ devices, revealing that historical calibration data can be beneficial when real-time data is unavailable due to system drift or latency.

Contribution

The study provides a framework for comparing noise-aware and non-noise-aware compilation techniques, analyzing the impact of calibration data on circuit fidelity across different quantum processors.

Findings

Historical calibration data can improve fidelity when real-time data is unavailable.

Noise-aware compilation techniques generally enhance circuit success probability.

Calibration data's effectiveness varies with system drift and latency conditions.

Abstract

Reliably executing quantum algorithms on noisy intermediate-scale quantum (NISQ) devices is challenging, as they are severely constrained and prone to errors. Efficient quantum circuit compilation techniques are therefore crucial for overcoming their limitations and dealing with their high error rates. These techniques consider the quantum hardware restrictions, such as the limited qubit connectivity, and perform some transformations to the original circuit that can be executed on a given quantum processor. Certain compilation methods use error information based on calibration data to further improve the success probability or the fidelity of the circuit to be run. However, it is uncertain to what extent incorporating calibration information in the compilation process can enhance the circuit performance. For instance, considering the most recent error data provided by vendors after calibrating the processor might not be functional enough as quantum systems are subject to drift, making the latest calibration data obsolete within minutes. In this paper, we explore how different usage of calibration data impacts the circuit fidelity, by using several compilation techniques and quantum processors (IBM Perth and Brisbane). To this aim, we implemented a framework that incorporates some of the state-of-the-art noise-aware and non-noise-aware compilation techniques and allows the user to perform fair comparisons under similar processor conditions. Our experiments yield valuable insights into the effects of noise-aware methodologies and the employment of calibration data. The main finding is that pre-processing historical calibration data can improve fidelity when real-time calibration data is not available due to factors such as cloud service latency and waiting queues between compilation and execution on the quantum backend.