# FIDDLE: Reinforcement Learning for Quantum Fidelity Enhancement

arXiv: https://arxiv.org/abs/2510.15833 · HTML: https://mlsanity.com/paper/2510.15833/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Quantum machine learning beyond kernel methods](https://mlsanity.com/paper/2110.13162/) (2021)
- [Reinforcement learning for optimization of variational quantum circuit architectures](https://mlsanity.com/paper/2103.16089/) (2021)
- [Quantum algorithms for feedforward neural networks](https://mlsanity.com/paper/1812.03089/) (2018)
- [Quantum Machine Learning Algorithm for Knowledge Graphs](https://mlsanity.com/paper/2001.01077/) (2020)
- [The Quantum Alternating Operator Ansatz for Satisfiability Problems](https://mlsanity.com/paper/2301.11292/) (2023)
- [Scalable Experimental Bounds for Entangled Quantum State Fidelities](https://mlsanity.com/paper/2210.03048/) (2022)

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

- [Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing](https://mlsanity.com/paper/2606.12816/) (2026)

## Abstract (arXiv)

Quantum computing has the potential to revolutionize fields like quantum optimization and quantum machine learning. However, current quantum devices are hindered by noise, reducing their reliability. A key challenge in gate-based quantum computing is improving the reliability of quantum circuits, measured by process fidelity, during the transpilation process, particularly in the routing stage. In this paper, we address the Fidelity Maximization in Routing Stage (FMRS) problem by introducing FIDDLE, a novel learning framework comprising two modules: a Gaussian Process-based surrogate model to estimate process fidelity with limited training samples and a reinforcement learning module to optimize routing. Our approach is the first to directly maximize process fidelity, outperforming traditional methods that rely on indirect metrics such as circuit depth or gate count. We rigorously evaluate FIDDLE by comparing it with state-of-the-art fidelity estimation techniques and routing optimization methods. The results demonstrate that our proposed surrogate model is able to provide a better estimation on the process fidelity compared to existing learning techniques, and our end-to-end framework significantly improves the process fidelity of quantum circuits across various noise models.