Quantum compiling by deep reinforcement learning

Abstract

The general problem of quantum compiling is to approximate any unitary transformation that describes the quantum computation as a sequence of elements selected from a finite base of universal quantum gates. The Solovay-Kitaev theorem guarantees the existence of such an approximating sequence. Though, the solutions to the quantum compiling problem suffer from a tradeoff between the length of the sequences, the precompilation time, and the execution time. Traditional approaches are time-consuming, unsuitable to be employed during computation. Here, we propose a deep reinforcement learning method as an alternative strategy, which requires a single precompilation procedure to learn a general strategy to approximate single-qubit unitaries. We show that this approach reduces the overall execution time, improving the tradeoff between the length of the sequence and execution time, potentially allowing real-time operations.

Quantum compilers are characterized by a trade-off between the length of the sequences, the precompilation time, and the execution time. Here, the authors propose an approach based on deep reinforcement learning to approximate unitary operators as circuits, and show that this approach decreases the execution time, potentially allowing real-time quantum compiling.