# Quantum Circuit Pre-Synthesis: Learning Local Edits to Reduce T-count

arXiv: https://arxiv.org/abs/2601.19738 · HTML: https://mlsanity.com/paper/2601.19738/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 69 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 14 of 14)

- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Generalization in quantum machine learning from few training data](https://mlsanity.com/paper/2111.05292/) (2021)
- [Reinforcement learning for optimization of variational quantum circuit architectures](https://mlsanity.com/paper/2103.16089/) (2021)
- [Out-of-distribution generalization for learning quantum dynamics](https://mlsanity.com/paper/2204.10268/) (2022)
- [Quantum Circuit Optimization with AlphaTensor](https://mlsanity.com/paper/2402.14396/) (2024)
- [A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver](https://mlsanity.com/paper/1910.09694/) (2019)
- [A semi-agnostic ansatz with variable structure for quantum machine learning](https://mlsanity.com/paper/2103.06712/) (2021)
- [Compiler Optimization for Quantum Computing Using Reinforcement Learning](https://mlsanity.com/paper/2212.04508/) (2022)
- [Quarl: A Learning-Based Quantum Circuit Optimizer](https://mlsanity.com/paper/2307.10120/) (2023)
- [Optimizing the non-Clifford-count in unitary synthesis using Reinforcement Learning](https://mlsanity.com/paper/2509.21709/) (2025)
- [Quantum Compiling with Reinforcement Learning on a Superconducting Processor](https://mlsanity.com/paper/2406.12195/) (2024)
- [Scalable quantum dynamics compilation via quantum machine learning](https://mlsanity.com/paper/2409.16346/) (2024)

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

- [AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL](https://mlsanity.com/paper/2608.18946/) (2026)

## Abstract (arXiv)

Compiling quantum circuits into Clifford+T gates is a central task for fault-tolerant quantum computing using stabilizer codes. In the near term, T gates will dominate the cost of fault tolerant implementations, and any reduction in the number of such expensive gates could mean the difference between being able to run a circuit or not. While exact synthesis is exponentially hard in the number of qubits, local synthesis approaches are commonly used to compile large circuits by decomposing them into substructures. However, composing local methods leads to suboptimal compilations in key metrics such as T-count or circuit depth, and their performance strongly depends on circuit representation. In this work, we address this challenge by proposing textscQ-PreSyn, a strategy that, given a set of local edits preserving circuit equivalence, uses a RL agent to identify effective sequences of such actions and thereby obtain circuit representations that yield a reduced T-count upon synthesis. Experimental results of our proposed strategy, applied on top of well-known synthesis algorithms, show up to a 20% reduction in T-count on circuits with up to 25 qubits, without introducing any additional approximation error prior to synthesis.