Reusability Report: Optimizing T-count in General Quantum Circuits with AlphaTensor-Quantum

TL;DR

This paper extends AlphaTensor-Quantum to optimize T-count in diverse quantum circuits without retraining, demonstrating improved performance and generalizability across different circuit sizes.

Contribution

It introduces a general reinforcement learning agent capable of optimizing T-count in various quantum circuits, reducing the need for circuit-specific training.

Findings

General agent trained on 5-8 qubits outperforms previous methods.

The agent effectively reduces T-count across different circuit sizes.

Enhanced generalizability of the optimization method.

Abstract

Quantum computing has the potential to solve problems that are intractable for classical computers, with possible applications in areas such as drug discovery and high-energy physics. However, the practical implementation of quantum computation is hindered by the complexity of executing quantum circuits on hardware. In particular, minimizing the number of T-gates is crucial for implementing efficient quantum algorithms. AlphaTensor-Quantum is a reinforcement learning-based method designed to optimize the T-count of quantum circuits by formulating the problem as a tensor decomposition task. While it has demonstrated superior performance over existing methods on benchmark quantum arithmetic circuits, its applicability has so far been restricted to specific circuit families, requiring separate, time-intensive training for each new application. This report reproduces some of the key results of the original work and extends AlphaTensor-Quantum's capabilities to simplify random quantum circuits with varying qubit counts, eliminating the need for retraining on new circuits. Our experiments show that a general agent trained on 5- to 8-qubit circuits achieves greater T-count reduction than previous methods for a large fraction of quantum circuits. Furthermore, we demonstrate that a general agent trained on circuits with varying qubit numbers outperforms agents trained on fixed qubit numbers, highlighting the method's generalizability and its potential for broader quantum circuit optimization tasks.