Hamiltonian Quantum Generative Adversarial Networks

• URL: http://arxiv.org/abs/2211.02584v2
• Date: Mon, 8 Jul 2024 03:18:32 GMT
• Title: Hamiltonian Quantum Generative Adversarial Networks
• Authors: Leeseok Kim, Seth Lloyd, Milad Marvian,
• Abstract summary: We propose Hamiltonian Quantum Generative Adversarial Networks (HQuGANs) to learn to generate unknown input quantum states. We numerically demonstrate the capabilities of the proposed framework to learn various highly entangled many-body quantum states.
• Score: 4.806505912512235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Hamiltonian Quantum Generative Adversarial Networks (HQuGANs), to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is inspired by the success of classical generative adversarial networks in learning high-dimensional distributions. The quantum optimal control approach not only makes the algorithm naturally adaptable to the experimental constraints of near-term hardware, but also offers a more natural characterization of overparameterization compared to the circuit model. We numerically demonstrate the capabilities of the proposed framework to learn various highly entangled many-body quantum states, using simple two-body Hamiltonians and under experimentally relevant constraints such as low-bandwidth controls. We analyze the computational cost of implementing HQuGANs on quantum computers and show how the framework can be extended to learn quantum dynamics. Furthermore, we introduce a new cost function that circumvents the problem of mode collapse that prevents convergence of HQuGANs and demonstrate how to accelerate the convergence of them when generating a pure state.

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