Enhancing variational quantum state diagonalization using reinforcement learning techniques. (arXiv:2306.11086v2 [quant-ph] UPDATED)


Enhancing variational quantum state diagonalization using reinforcement learning techniques. (arXiv:2306.11086v2 [quant-ph] UPDATED)
By: <a href="http://arxiv.org/find/quant-ph/1/au:+Kundu_A/0/1/0/all/0/1">Akash Kundu</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Bedelek_P/0/1/0/all/0/1">Przemys&#x142;aw Bede&#x142;ek</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Ostaszewski_M/0/1/0/all/0/1">Mateusz Ostaszewski</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Danaci_O/0/1/0/all/0/1">Onur Danaci</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Patel_Y/0/1/0/all/0/1">Yash J. Patel</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Dunjko_V/0/1/0/all/0/1">Vedran Dunjko</a>, <a href="http://arxiv.org/find/quant-ph/1/au:+Miszczak_J/0/1/0/all/0/1">Jaros&#x142;aw A. Miszczak</a> Posted: June 23, 2023

The development of variational quantum algorithms is 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 the
variational diagonalization method, which constitutes an important algorithmic
subroutine, and it can be used directly for working 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. To achieve this, we utilize a novel
encoding method that can be used to tackle the problem of circuit depth
optimization using a reinforcement learning approach. We demonstrate that our
approach provides a solid approximation to the diagonalization task while using
a small number of gates. The circuits proposed by the reinforcement learning
methods are shallower than the standard variational quantum state
diagonalization algorithm, and thus can be used in situations where the depth
of quantum circuits is limited by the hardware capabilities.

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