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

2026-01-27

AI Summary

This work addresses the challenge of high T-count in Clifford+T circuits, which significantly increases resource overhead in fault-tolerant quantum computing. Existing local synthesis methods are constrained by circuit representations and struggle to achieve optimal T-count and depth. To overcome this limitation, the paper introduces Q-PreSyn, the first approach to integrate reinforcement learning into the pre-synthesis phase of quantum circuit compilation. By training an agent to learn sequences of function-preserving local editing operations, Q-PreSyn produces circuit representations that are more amenable to downstream synthesis. Without introducing any approximation error, the method achieves up to a 20% reduction in T-count compared to state-of-the-art techniques on circuits with up to 25 qubits, substantially improving synthesis efficiency.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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 \textsc{Q-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.

Problem

• quantum circuit synthesis
• T-count reduction
• Clifford+T
• circuit optimization
• fault-tolerant quantum computing

Innovation

• Quantum Circuit Optimization
• T-count Reduction
• Reinforcement Learning
• Clifford+T Synthesis
• Circuit Pre-synthesis