# Addressing Temporal Variations in Qubit Quality Metrics for Parameterized Quantum Circuits

## TL;DR

This paper introduces training methods for parameterized quantum circuits that account for temporal variations in qubit quality, significantly enhancing their fidelity on noisy intermediate-scale quantum hardware.

## Contribution

It proposes classical training methodologies for PQC that improve fidelity by addressing temporal variations in qubit quality metrics.

## Key findings

- Fidelity improvements up to 42.5% on target NISQ hardware.
- Training methods effectively mitigate temporal variations in qubit quality.
- Enhanced robustness of PQC against hardware noise over time.

## Abstract

The public access to noisy intermediate-scale quantum (NISQ) computers facilitated by IBM, Rigetti, D-Wave, etc., has propelled the development of quantum applications that may offer quantum supremacy in the future large-scale quantum computers. Parameterized quantum circuits (PQC) have emerged as a major driver for the development of quantum routines that potentially improve the circuit's resilience to the noise. PQC's have been applied in both generative (e.g. generative adversarial network) and discriminative (e.g. quantum classifier) tasks in the field of quantum machine learning. PQC's have been also considered to realize high fidelity quantum gates with the available imperfect native gates of a target quantum hardware. Parameters of a PQC are determined through an iterative training process for a target noisy quantum hardware. However, temporal variations in qubit quality metrics affect the performance of a PQC. Therefore, the circuit that is trained without considering temporal variations exhibits poor fidelity over time. In this paper, we present training methodologies for PQC in a completely classical environment that can improve the fidelity of the trained PQC on a target NISQ hardware by as much as 42.5%.