A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search
- URL: http://arxiv.org/abs/2404.06174v2
- Date: Mon, 15 Apr 2024 05:37:07 GMT
- Title: A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search
- Authors: Abhishek Sadhu, Aritra Sarkar, Akash Kundu,
- Abstract summary: This study investigates RL-QAS for crafting ansatzes tailored to variational quantum state diagonalisation problem.
We leverage these insights to devise entanglement-guided admissible ansatz in QAS to diagonalise random quantum states using optimal resources.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the field of quantum computing, variational quantum algorithms (VQAs) represent a pivotal category of quantum solutions across a broad spectrum of applications. These algorithms demonstrate significant potential for realising quantum computational advantage. A fundamental aspect of VQAs involves formulating expressive and efficient quantum circuits (namely ansatz) and automating the search of such ansatz is known as quantum architecture search (QAS). RL-QAS involves optimising QAS using reinforcement learning techniques. This study investigates RL-QAS for crafting ansatzes tailored to the variational quantum state diagonalisation problem. Our investigation includes a comprehensive analysis of various dimensions, such as the entanglement thresholds of the resultant states, the impact of initial conditions on the performance of RL-agent, the phase change behaviour of correlation in concurrence bounds, and the discrete contributions of qubits in deducing eigenvalues through conditional entropy metrics. We leverage these insights to devise entanglement-guided admissible ansatz in QAS to diagonalise random quantum states using optimal resources. Furthermore, the methodologies presented herein offer a generalised framework for constructing reward functions within RL-QAS applicable to variational quantum algorithms.
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