2020/09/30 by Reza Haghshenas
Mathematics · Physics and Astronomy · #Ansatz #Computer science #Mathematics #Physics #Pure mathematics #Quantum #Quantum algorithm #Quantum and electron transport phenomena #Quantum circuit #Quantum computer #Quantum gate #Quantum many-body systems #Quantum mechanics #Quantum network #Qubit #Renormalization #Statistical physics #Tensor (intrinsic definition) #Tensor decomposition and applications #Theoretical computer science #cond-mat.stat-mech #cond-mat.str-el #quant-ph
paper · pdf · doi:10.1103/physrevresearch.3.023148
published as Phys. Rev. Research 3, 023148 (2021) · 9 pages, 6 figures, sample source codes are available at https://github.com/rezah/unitary-tensor-network-operator
arxiv created 2021/03/30 · openalex publication_date 2021/05/26 · arxiv updated 2021/06/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
An efficient representation of a quantum circuit is of great importance in achieving a quantum advantage on current noisy intermediate scale quantum (NISQ) devices and the classical simulation of quantum manybody systems. The quantum circuits are playing the key ingredient in the performance of variational quantum algorithms and quantum dynamics in problems of physics and chemistry. In this paper, we study the role of the network structure of a quantum circuit in its performance. We discuss the variational optimization of quantum circuit (a unitary tensor-network circuit) with different network structures. The ansatz is performed based on a generalization of well-developed multiscale entanglement renormalization algorithm and also the conjugategradient method with an effective line search. We present the benchmarking calculations for different network structures by studying the Heisenberg model in a strongly disordered magnetic field and a tensor-network QR decomposition. Our work can contribute to achieve the most out of NISQ hardware and to classically develop isometric tensor network states.