Quantum search by measurements assisted by pre-trained tensor network states for Hamiltonian simulations

2024/07/27 by Younes Javanmard, Javanmard, Younes
Computer Science · Engineering · #Advanced Memory and Neural Computing #FOS: Physical sciences #Ferroelectric and Negative Capacitance Devices #Neural Networks and Applications #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2407.19348

openalex publication_date 2024/07/27 · openalex created_date 2024/08/01 · openalex updated_date 2026/07/28

Abstract

We present a quantum algorithm for simulating complex many-body systems and finding their ground states, combining the use of tensor networks and density matrix renormalization group (DMRG) techniques. The algorithm is based on von Neumann's measurement prescription, which serves as a conceptual building block for quantum phase estimation. We describe the implementation and simulation of the algorithm, including the estimation of resources required and the use of matrix product operators (MPOs) to represent the Hamiltonian. We highlight the potential applications of the algorithm in simulating quantum spin systems and electronic structure problems.

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