Enhancing variational quantum state diagonalization using reinforcement learning techniques

New Journal of Physics, vol. 26, pp. 013034

Abstract

Abstract The variational quantum algorithms are crucial for the application of NISQ computers. Such algorithms require short quantum circuits, which are more amenable to implementation on near-term hardware, and many such methods have been developed. One of particular interest is the so-called variational quantum state diagonalization method, which constitutes an important algorithmic subroutine and can be used directly to work with data encoded in quantum states. In particular, it can be applied to discern the features of quantum states, such as entanglement properties of a system, or in quantum machine learning algorithms. In this work, we tackle the problem of designing a very shallow quantum circuit, required in the quantum state diagonalization task, by utilizing reinforcement learning (RL). We use a novel encoding method for the RL-state, a dense reward function, and an ε-greedy policy to achieve this. We demonstrate that the circuits proposed by the RL methods are shallower than the standard variational quantum state diagonalization algorithm and thus can be used in situations where hardware capabilities limit the depth of quantum circuits. The methods we propose in the paper can be readily adapted to address a wide range of variational quantum algorithms.

Authors 7

1. Akash Kundu corresponding

   Institute of Theoretical and Applied Informatics · Polish Academy of Sciences

   Affiliation as printed

   Polish Academy of Sciences Institute of Theoretical and Applied Informatics, Bałtycka 5, 44-100 Gliwice, Gliwice, 44-100, POLAND

2. Przemysław Bedełek

   Warsaw University of Technology

   Affiliation as printed

   Warsaw University of Technology, Warszawa, POLAND

3. Mateusz Ostaszewski

   Warsaw University of Technology

   Affiliation as printed

   Warsaw University of Technology, Warszawa, POLAND

4. Onur Danaci Aachen

   Leiden University

   Affiliation as printed

   , Leiden University Leiden Institute of Advanced Computer Science, Leiden, Zuid-Holland, NETHERLANDS

5. Yash J. Patel Aachen

   Leiden University

   Affiliation as printed

   Leiden University Leiden Institute of Advanced Computer Science, Leiden, Zuid-Holland, NETHERLANDS

6. Vedran Dunjko Aachen applied Quantum algorithms (aQa)

   Leiden University

   Affiliation as printed

   Applied Quantum Algorithms,, Universiteit Leiden Instituut-Lorentz, Leiden, Zuid-Holland, NETHERLANDS

7. Jarosław Adam Miszczak

   Institute of Theoretical and Applied Informatics · Polish Academy of Sciences

   Affiliation as printed

   Institute of Theoretical and Applied Informatics, Institute of Theoretical and Applied Informatics Polish Academy of Sciences, Gliwice, Slaskie, POLAND