# Neural Guided Sampling for Quantum Circuit Optimization

arXiv: https://arxiv.org/abs/2510.12430 · HTML: https://mlsanity.com/paper/2510.12430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 61 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [SNAS: Stochastic Neural Architecture Search](https://mlsanity.com/paper/1812.09926/) (2018)
- [Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses](https://mlsanity.com/paper/1905.04132/) (2019)
- [Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm](https://mlsanity.com/paper/1906.02290/) (2019)
- [CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus](https://mlsanity.com/paper/2001.02643/) (2020)
- [Towards modular and programmable architecture search](https://mlsanity.com/paper/1909.13404/) (2019)
- [Synthesizing Quantum-Circuit Optimizers](https://mlsanity.com/paper/2211.09691/) (2022)
- [Exact and practical pattern matching for quantum circuit optimization](https://mlsanity.com/paper/1909.05270/) (2019)
- [Robust Shape Fitting for 3D Scene Abstraction](https://mlsanity.com/paper/2403.10452/) (2024)
- [TetrisLock: Quantum Circuit Split Compilation with Interlocking Patterns](https://mlsanity.com/paper/2503.11982/) (2025)
- [Tackling Coherent Noise in Quantum Computing via Cross-Layer Compiler Optimization](https://mlsanity.com/paper/2410.09664/) (2024)
- [Inverse-Transpilation: Reverse-Engineering Quantum Compiler Optimization Passes from Circuit Snapshots](https://mlsanity.com/paper/2504.19113/) (2025)

## Abstract (arXiv)

Translating a general quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, comes with a substantial increase in the length of the equivalent circuit. Due to decoherence, the quality of the computational outcome can degrade seriously with increasing circuit length. Thus, there is major interest to reduce a transpiled quantum circuit to an equivalent circuit which is in its gate count as short as possible. This is, what we call quantum circuit reduction: Finding a quantum circuit with a reduced amount of gates without changing its unitary. One method to address efficient transpilation, e.g. as a post-transpilation process, is based on approaches known from stochastic optimization, e.g. by using random sampling and local resynthesis strategies. Here, a core challenge is that these methods can suffer from sampling efficiency, causing long and energy consuming optimization time. As a remedy, we propose in this work 2D neural guided sampling. Thus, given a 2D representation of a quantum circuit, a neural network predicts groups of gates in the quantum circuit, which are likely reducible. It leads to a sampling prior which can heavily reduce the compute time for quantum circuit reduction. In several experiments, we demonstrate that our method is superior to results obtained from different qiskit or BQSKit optimization levels.