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-Quantum1 is a reinforcement-learning-based method designed to optimize the T count of quantum circuits by formulating the problem as a tensor decomposition task. Although 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 five- to eight-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.
