Abstract
We address the problem of T-count optimization, i.e., minimizing the number of the most expensive gates in fault-tolerant quantum computation (namely, the T gates) that are needed to implement a given circuit. For that, we develop AlphaTensor-Quantum, a method based on deep reinforcement learning that exploits the relationship between optimizing T-count and tensor decomposition. Unlike existing methods for T-count optimization, AlphaTensor-Quantum can incorporate domain-specific knowledge about quantum computation and leverage gadgets, which significantly reduces the T-count of the optimized circuits. AlphaTensor-Quantum outperforms all existing methods for T-count optimization on a set of arithmetic benchmarks. Remarkably, it rediscovers an efficient multiplication algorithm akin to Karatsuba's method for multiplication in finite fields, and it finds the best human-designed solutions for relevant arithmetic computations used in Shor's algorithm and for quantum chemistry simulation. Thus, AlphaTensor-Quantum can save hundreds of hours of research by optimizing relevant quantum circuits in a fully automated way.
Authors
Francisco Ruiz, Tuomas Laakkonen*, Johannes Bausch, Matej Balog, Mohammadamin Barekatain*, Francisco Heras, Alexander Novikov, Nathan Fitzpatrick*, Bernardino Romera Paredes, John van de Wetering*, Alhussein Fawzi, Konstantinos Meichanetzidis*, Pushmeet Kohli
- *
- External author
Venue
arXiv