Entanglement Forging with generative neural network models

• URL: http://arxiv.org/abs/2205.00933v1
• Date: Mon, 2 May 2022 14:29:17 GMT
• Title: Entanglement Forging with generative neural network models
• Authors: Patrick Huembeli, Giuseppe Carleo, Antonio Mezzacapo
• Abstract summary: We show that a hybrid quantum-classical variational ans"atze can forge entanglement to lower quantum resource overhead. The method is efficient in terms of the number of measurements required to achieve fixed precision on expected values of observables.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The optimal use of quantum and classical computational techniques together is important to address problems that cannot be easily solved by quantum computations alone. This is the case of the ground state problem for quantum many-body systems. We show here that probabilistic generative models can work in conjunction with quantum algorithms to design hybrid quantum-classical variational ans\"atze that forge entanglement to lower quantum resource overhead. The variational ans\"atze comprise parametrized quantum circuits on two separate quantum registers, and a classical generative neural network that can entangle them by learning a Schmidt decomposition of the whole system. The method presented is efficient in terms of the number of measurements required to achieve fixed precision on expected values of observables. To demonstrate its effectiveness, we perform numerical experiments on the transverse field Ising model in one and two dimensions, and fermionic systems such as the t-V Hamiltonian of spinless fermions on a lattice.

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