Q-fid: Quantum Circuit Fidelity Improvement with LSTM Networks

TL;DR

Q-fid introduces an LSTM-based system to predict quantum circuit fidelity accurately, enabling better optimization of quantum circuits by accounting for hardware noise and dynamic conditions.

Contribution

The paper presents a novel LSTM-based fidelity prediction model and a new metric for quantum circuit fidelity, improving accuracy and adaptability over existing methods.

Findings

Achieves an average RMSE of 0.0515 in fidelity prediction.

Up to 24.7x more accurate than existing tools like Qiskit's mapomatic.

Provides a dynamic, adaptable approach for noise performance estimation.

Abstract

The fidelity of quantum circuits (QC) is influenced by several factors, including hardware characteristics, calibration status, and the transpilation process, all of which impact their susceptibility to noise. However, existing methods struggle to estimate and compare the noise performance of different circuit layouts due to fluctuating error rates and the absence of a standardized fidelity metric. In this work, Q-fid is introduced, a Long Short-Term Memory (LSTM) based fidelity prediction system accompanied by a novel metric designed to quantify the fidelity of quantum circuits. Q-fid provides an intuitive way to predict the noise performance of Noisy Intermediate-Scale Quantum (NISQ) circuits. This approach frames fidelity prediction as a Time Series Forecasting problem to analyze the tokenized circuits, capturing the causal dependence of the gate sequences and their impact on overall fidelity. Additionally, the model is capable of dynamically adapting to changes in hardware characteristics, ensuring accurate fidelity predictions under varying conditions. Q-fid achieves a high prediction accuracy with an average RMSE of 0.0515, up to 24.7x more accurate than the Qiskit transpile tool mapomatic. By offering a reliable method for fidelity prediction, Q-fid empowers developers to optimize transpilation strategies, leading to more efficient and noise-resilient quantum circuit implementations.