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arXiv:1802.06952 · 中国科学通报：英文版

64-qubit quantum circuit simulation

Zhaoyun Chen, Qi Zhou, Cheng Xue, Yang Xia, Guang‐Can Guo, Guo‐Ping Guo

Published Jun 20, 2018• 136 citations
arXivHTMLPDF
DOI
10.1016/j.scib.2018.06.007

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We consider recent works on the simulation of quantum circuits using the formalism of matrix product states and the formalism of contracting tensor networks. We provide simplified direct proofs of many of these results, extending an explicit class of efficiently simulable circuits (log depth circuits with 2-qubit gates of limited range) to the following: let C be any poly sized quantum circuit (generally of poly depth too) on n qubits comprising 1- and 2- qubit gates and 1-qubit measurements (with 2-qubit gates acting on arbitrary pairs of qubit lines). For each qubit line j let D_j be the number of 2-qubit gates that touch or cross the line j i.e. the number of 2-qubit gates that are applied to qubits i,k with i \leq j \leq k. Let D=max_j D_j. Then the quantum process can be classically simulated in time n poly(2^D). Thus if D=O(log n) then C may be efficiently classically simulated.
Due to the unreliability and limited capacity of existing quantum computer prototypes, quantum circuit simulation continues to be a vital tool for validating next generation quantum computers and for studying variational quantum algorithms, which are among the leading candidates for useful quantum computation. Existing quantum circuit simulators do not address the common traits of variational algorithms, namely: 1) their ability to work with noisy qubits and operations, 2) their repeated execution of the same circuits but with different parameters, and 3) the fact that they sample from circuit final wavefunctions to drive a classical optimization routine. We present a quantum circuit simulation toolchain based on logical abstractions targeted for simulating variational algorithms. Our proposed toolchain encodes quantum amplitudes and noise probabilities in a probabilistic graphical model, and it compiles the circuits to logical formulas that support efficient repeated simulation of and sampling from quantum circuits for different parameters. Compared to state-of-the-art state vector and density matrix quantum circuit simulators, our simulation approach offers greater performance when sampling from noisy circuits with at least eight to 20 qubits and with around 12 operations on each qubit, making the approach ideal for simulating near-term variational quantum algorithms. And for simulating noise-free shallow quantum circuits with 32 qubits, our simulation approach offers a 66× reduction in sampling cost versus quantum circuit simulation techniques based on tensor network contraction.