01805nas a2200193   4500000000100000000000100001008004100002260001200043100001600055700002500071700002400096700001600120700001500136700001800151700002200169245009600191490000700287520131700294       2024                            d  c01/20241 aAkash Kundu1 aPrzemysław Bedełek1 aMateusz Ostaszewski1 aOnur Danaci1 aYash Patel1 aVedran Dunjko1 aJaroslaw Miszczak00aEnhancing quantum variational state diagonalization using reinforcement learning techniques0 v263 a<p>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 $\epsilon$-greedy policy to achieve this. We demonstrate that 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 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.</p>