# Q-fid: Quantum Circuit Fidelity Improvement with LSTM Networks

arXiv: https://arxiv.org/abs/2303.17523 · HTML: https://mlsanity.com/paper/2303.17523/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 69 references that are in the map.
- First posted on arXiv 30 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval](https://mlsanity.com/paper/1502.06922/) (2015)
- [Effects of padding on LSTMs and CNNs](https://mlsanity.com/paper/1903.07288/) (2019)
- [Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation](https://mlsanity.com/paper/2211.08737/) (2022)
- [Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation](https://mlsanity.com/paper/1903.10963/) (2019)
- [A Software Methodology for Compiling Quantum Programs](https://mlsanity.com/paper/1604.01401/) (2016)
- [Comparing and Integrating Constraint Programming and Temporal Planning for Quantum Circuit Compilation](https://mlsanity.com/paper/1803.06775/) (2018)
- [Quantum Random Numbers generated by the Cloud Superconducting Quantum Computer](https://mlsanity.com/paper/1906.04410/) (2019)

## Built on by (most cited first, 1 of 1)

- [Fidelity-Aware Scheduling of Quantum Circuits on Multi-QPU Systems](https://mlsanity.com/paper/2609.09980/) (2026)

## Abstract (arXiv)

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.