Quantum Compiling by Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning approach to quantum compiling, enabling real-time approximation of unitary transformations into quantum gate sequences, addressing the complexity and inefficiency of traditional methods.

Contribution

It presents a novel deep reinforcement learning method for quantum compiling that offers faster, real-time approximation of quantum gates compared to traditional algorithms.

Findings

Deep reinforcement learning enables real-time quantum compiling.

The method significantly reduces computation time for quantum gate sequences.

It provides a scalable alternative to traditional quantum compiling algorithms.

Abstract

The architecture of circuital quantum computers requires computing layers devoted to compiling high-level quantum algorithms into lower-level circuits of quantum gates. 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 existence of an approximating sequence of one qubit quantum gates is guaranteed by the Solovay-Kitaev theorem, which implies sub-optimal algorithms to establish it explicitly. Since a unitary transformation may require significantly different gate sequences, depending on the base considered, such a problem is of great complexity and does not admit an efficient approximating algorithm. Therefore, traditional approaches are time-consuming tasks, unsuitable to be employed during quantum computation. We exploit the deep reinforcement learning method as an alternative strategy, which has a significantly different trade-off between search time and exploitation time. Deep reinforcement learning allows creating single-qubit operations in real time, after an arbitrary long training period during which a strategy for creating sequences to approximate unitary operators is built. The deep reinforcement learning based compiling method allows for fast computation times, which could in principle be exploited for real-time quantum compiling.