qTorch
qTorch: The Quantum Tensor Contraction Handler. Classical simulation of quantum computation is necessary for studying the numerical behavior of quantum algorithms, as there does not yet exist a large viable quantum computer on which to perform numerical tests. Tensor network (TN) contraction is an algorithmic method that can efficiently simulate some quantum circuits, often greatly reducing the computational cost over methods that simulate the full Hilbert space. In this study we implement a tensor network contraction program for simulating quantum circuits using multi-core compute nodes. We show simulation results for the Max-Cut problem on 3- through 7-regular graphs using the quantum approximate optimization algorithm (QAOA), successfully simulating up to 100 qubits. We test two different methods for generating the ordering of tensor index contractions: one is based on the tree decomposition of the line graph, while the other generates ordering using a straight-forward stochastic scheme. Through studying instances of QAOA circuits, we show the expected result that as the treewidth of the quantum circuit’s line graph decreases, TN contraction becomes significantly more efficient than simulating the whole Hilbert space. The results in this work suggest that tensor contraction methods are superior only when simulating Max-Cut/QAOA with graphs of regularities approximately five and below. Insight into this point of equal computational cost helps one determine which simulation method will be more efficient for a given quantum circuit. The stochastic contraction method outperforms the line graph based method only when the time to calculate a reasonable tree decomposition is prohibitively expensive. Finally, we release our software package, qTorch (Quantum TensOR Contraction Handler), intended for general quantum circuit simulation.
Keywords for this software
References in zbMATH (referenced in 5 articles , 1 standard article )
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Sorted by year (- Cai, Dong-Qi; Chen, Xi; Han, Yu-Hong; Yi, Xin; Jia, Jin-Ping; Cao, Cong; Fan, Ling: Implementation of an E-payment security evaluation system based on quantum blind computing (2020)
- Nathan Killoran, Josh Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, Christian Weedbrook: Strawberry Fields: A Software Platform for Photonic Quantum Computing (2018) arXiv
- Tyson Jones, Anna Brown, Ian Bush, Simon Benjamin: QuEST and High Performance Simulation of Quantum Computers (2018) arXiv
- E. Schuyler Fried, Nicolas P. D. Sawaya, Yudong Cao, Ian D. Kivlichan, Jhonathan Romero, Alán Aspuru-Guzik: qTorch: The Quantum Tensor Contraction Handler (2017) arXiv
- Jarrod R. McClean, Ian D. Kivlichan, Kevin J. Sung, Damian S. Steiger, Yudong Cao, Chengyu Dai, E. Schuyler Fried, Craig Gidney, Brendan Gimby, Pranav Gokhale, Thomas Häner, Tarini Hardikar, Vojtěch Havlíček, Cupjin Huang, Josh Izaac, Zhang Jiang, Xinle Liu, Matthew Neeley, Thomas O’Brien, Isil Ozfidan, Maxwell D. Radin, Jhonathan Romero, Nicholas Rubin, Nicolas P. D. Sawaya, Kanav Setia, Sukin Sim, Mark Steudtner, Qiming Sun, Wei Sun, Fang Zhang, Ryan Babbush: OpenFermion: The Electronic Structure Package for Quantum Computers (2017) arXiv