Q-fid: Quantum Circuit Fidelity Improvement with LSTM Networks
arXiv:2303.17523 · doi:10.1002/qute.202500022
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.
Code: github.com/yikaimao/Q_fid Dataset: kaggle.com/datasets/ykmaoykmao/q-fid-datasets
References in corpus (11)
- Randomized Benchmarking of Quantum Gates
- Robust randomized benchmarking of quantum processes
- The Future of Quantum Computing with Superconducting Qubits
- Measuring the Capabilities of Quantum Computers
- Storage of photonic time-bin qubits for up to 20 ms in a rare-earth doped crystal
- Scalable randomized benchmarking of quantum computers using mirror circuits
- Statistical Methods for Quantum State Verification and Fidelity Estimation
- Tight bounds on the convergence of noisy random circuits to the uniform distribution
- Quantum Algorithm for Fidelity Estimation
- Quantum circuit fidelity estimation using machine learning
- A SAT approach to the initial mapping problem in SWAP gate insertion for commuting gates