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Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization

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Dominic W. Berry1, Craig Gidney2, Mario Motta3, Jarrod R. McClean2, and Ryan Babbush2

1Department of Physics and Astronomy, Macquarie University, Sydney, NSW 2109, Australia
2Google Research, Venice, CA 90291, United States
3Division of Chemistry, California Institute of Technology, Pasadena, CA 91125, United States

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Abstract

Recent work has dramatically reduced the gate complexity required to quantum simulate chemistry by using linear combinations of unitaries based methods to exploit structure in the plane wave basis Coulomb operator. Here, we show that one can achieve similar scaling even for arbitrary basis sets (which can be hundreds of times more compact than plane waves) by using qubitized quantum walks in a fashion that takes advantage of structure in the Coulomb operator, either by directly exploiting sparseness, or via a low rank tensor factorization. We provide circuits for several variants of our algorithm (which all improve over the scaling of prior methods) including one with $widetilde{cal O}(N^{3/2} lambda)$ T complexity, where $N$ is number of orbitals and $lambda$ is the 1-norm of the chemistry Hamiltonian. We deploy our algorithms to simulate the FeMoco molecule (relevant to Nitrogen fixation) and obtain circuits requiring about seven hundred times less surface code spacetime volume than prior quantum algorithms for this system, despite us using a larger and more accurate active space.

Simulation of quantum chemistry is one of the most important potential applications of quantum computers, because it could be used to design new molecules for a wide range of applications. For example, it could be used to gain understanding of biological Nitrogen fixation by simulation of the FeMoco molecule. We show how to greatly accelerate the simulation of quantum chemistry by taking advantage of the structure of the system, together with several other new advances in quantum algorithms. Our most efficient approach takes advantage of the fact that the description of the energy has many terms that are close to zero and can be ignored. Together with a more efficient way of inputting information about the nonzero terms into the quantum algorithm, for the example of FeMoco we achieve a speedup of about a factor of 700.

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Cited by

[1] Tyler Takeshita, Nicholas C. Rubin, Zhang Jiang, Eunseok Lee, Ryan Babbush, and Jarrod R. McClean, “Increasing the Representation Accuracy of Quantum Simulations of Chemistry without Extra Quantum Resources”, arXiv:1902.10679, Physical Review X 10 1, 011004 (2020).

[2] Yuta Matsuzawa and Yuki Kurashige, “Jastrow-type Decomposition in Quantum Chemistry for Low-Depth Quantum Circuits”, arXiv:1909.12410, Journal of Chemical Theory and Computation acs.jctc.9b00963 (2020).

[3] Ryan Babbush, Dominic W. Berry, Jarrod R. McClean, and Hartmut Neven, “Quantum simulation of chemistry with sublinear scaling in basis size”, npj Quantum Information 5 1, 92 (2019).

[4] Jarrod R. McClean, Kevin J. Sung, Ian D. Kivlichan, Yudong Cao, Chengyu Dai, E. Schuyler Fried, Craig Gidney, Brendan Gimby, Pranav Gokhale, Thomas Häner, Tarini Hardikar, Vojtěch Havlíček, Oscar Higgott, Cupjin Huang, Josh Izaac, Zhang Jiang, Xinle Liu, Sam McArdle, Matthew Neeley, Thomas O’Brien, Bryan O’Gorman, Isil Ozfidan, Maxwell D. Radin, Jhonathan Romero, Nicholas Rubin, Nicolas P. D. Sawaya, Kanav Setia, Sukin Sim, Damian S. Steiger, Mark Steudtner, Qiming Sun, Wei Sun, Daochen Wang, Fang Zhang, and Ryan Babbush, “OpenFermion: The Electronic Structure Package for Quantum Computers”, arXiv:1710.07629.

[5] Ian D. Kivlichan, Craig Gidney, Dominic W. Berry, Nathan Wiebe, Jarrod McClean, Wei Sun, Zhang Jiang, Nicholas Rubin, Austin Fowler, Alán Aspuru-Guzik, Hartmut Neven, and Ryan Babbush, “Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization”, arXiv:1902.10673.

[6] Ryan Babbush, Dominic W. Berry, and Hartmut Neven, “Quantum simulation of the Sachdev-Ye-Kitaev model by asymmetric qubitization”, Physical Review A 99 4, 040301 (2019).

[7] William M. Kirby and Peter J. Love, “Contextuality Test of the Nonclassicality of Variational Quantum Eigensolvers”, Physical Review Letters 123 20, 200501 (2019).

[8] Craig Gidney and Martin Ekerå, “How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits”, arXiv:1905.09749.

[9] Craig Gidney, “Windowed quantum arithmetic”, arXiv:1905.07682.

[10] William J. Huggins, Jarrod McClean, Nicholas Rubin, Zhang Jiang, Nathan Wiebe, K. Birgitta Whaley, and Ryan Babbush, “Efficient and Noise Resilient Measurements for Quantum Chemistry on Near-Term Quantum Computers”, arXiv:1907.13117.

[11] Craig Gidney and Austin G. Fowler, “Flexible layout of surface code computations using AutoCCZ states”, arXiv:1905.08916.

[12] Jarrod R. McClean, Fabian M. Faulstich, Qinyi Zhu, Bryan O’Gorman, Yiheng Qiu, Steven R. White, Ryan Babbush, and Lin Lin, “Discontinuous Galerkin discretization for quantum simulation of chemistry”, arXiv:1909.00028.

[13] Yingkai Ouyang, David R. White, and Earl Campbell, “Compilation by stochastic Hamiltonian sparsification”, arXiv:1910.06255.

[14] Kenji Sugisaki, Shigeaki Nakazawa, Kazuo Toyota, Kazunobu Sato, Daisuke Shiomi, and Takeji Takui, “Quantum chemistry on quantum computers: quantum simulations of the time evolution of wave functions under the S2 operator and determination of the spin quantum number S”, Physical Chemistry Chemical Physics (Incorporating Faraday Transactions) 21 28, 15356 (2019).

The above citations are from Crossref’s cited-by service (last updated successfully 2020-01-22 20:52:08) and SAO/NASA ADS (last updated successfully 2020-01-22 20:52:09). The list may be incomplete as not all publishers provide suitable and complete citation data.

Source: https://quantum-journal.org/papers/q-2019-12-02-208/

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