Quantum compiling by deep reinforcement learning

Lorenzo Moro,Enrico Prati,Matteo G A Paris,Marcello Restelli

doi:10.1038/s42005-021-00684-3

Lorenzo Moro, Enrico Prati + Show 2 more

Open Access

https://doi.org/10.1038/s42005-021-00684-3

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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.

Highlights

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
Every quantum compiler has its own trade-off between the length of the sequences, which should be as short as possible, the precompilation time, i.e., the time taken by the algorithm to be ready for use, and the execution time, i.e, the time the algorithm takes to return the sequence[7]
We propose an approach to quantum compiling, exploiting deep reinforcement learning to approximate single-qubit unitary operators as circuits made by an arbitrary initial set of elementary quantum gates

Summary

Introduction

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. We propose an approach to quantum compiling, exploiting deep reinforcement learning to approximate single-qubit unitary operators as circuits made by an arbitrary initial set of elementary quantum gates.

Results

Conclusion