Efficient quantum measurement of Pauli operators in the presence of finite sampling error

Ophelia Crawford1, Barnaby van Straaten1, Daochen Wang1,2, Thomas Parks1, Earl Campbell1,3, and Stephen Brierley1

1Riverlane, Cambridge, UK
2Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, USA
3Department of Physics and Astronomy, University of Sheffield, Sheffield, UK

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Abstract

Estimating the expectation value of an operator corresponding to an observable is a fundamental task in quantum computation. It is often impossible to obtain such estimates directly, as the computer is restricted to measuring in a fixed computational basis. One common solution splits the operator into a weighted sum of Pauli operators and measures each separately, at the cost of many measurements. An improved version collects mutually commuting Pauli operators together before measuring all operators within a collection simultaneously. The effectiveness of doing this depends on two factors. Firstly, we must understand the improvement offered by a given arrangement of Paulis in collections. In our work, we propose two natural metrics for quantifying this, operating under the assumption that measurements are distributed optimally among collections so as to minimise the overall finite sampling error. Motivated by the mathematical form of these metrics, we introduce $\large{S}$ORTED $\large{I}$NSERTION, a collecting strategy that exploits the weighting of each Pauli operator in the overall sum. Secondly, to measure all Pauli operators within a collection simultaneously, a circuit is required to rotate them to the computational basis. In our work, we present two efficient circuit constructions that suitably rotate any collection of $\boldsymbol{k}$ independent commuting $\boldsymbol{n}$-qubit Pauli operators using at most $\boldsymbol{kn-k(k+1)/2}$ and $\boldsymbol{O(kn/\log k)}$ two-qubit gates respectively. Our methods are numerically illustrated in the context of the Variational Quantum Eigensolver, where the operators in question are molecular Hamiltonians. As measured by our metrics, $\large{S}$ORTED $\large{I}$NSERTION outperforms four conventional greedy colouring algorithms that seek the minimum number of collections.

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[19] Ignacio Loaiza, Alireza Marefat Khah, Nathan Wiebe, and Artur F Izmaylov, "Reducing molecular electronic Hamiltonian simulation cost for linear combination of unitaries approaches", Quantum Science and Technology 8 3, 035019 (2023).

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[22] Mahabubul Alam and Swaroop Ghosh, "QNet: A Scalable and Noise-Resilient Quantum Neural Network Architecture for Noisy Intermediate-Scale Quantum Computers", Frontiers in Physics 9, 755139 (2022).

[23] Nicolas PD Sawaya and Joonsuk Huh, "Improved Resource‐Tunable Near‐Term Quantum Algorithms for Transition Probabilities, with Applications in Physics and Variational Quantum Linear Algebra", Advanced Quantum Technologies 6 9, 2300042 (2023).

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[55] Wenyang Qian, Robert Basili, Soham Pal, Glenn Luecke, and James P. Vary, "Solving hadron structures using the basis light-front quantization approach on quantum computers", Physical Review Research 4 4, 043193 (2022).

[56] Guglielmo Mazzola, "Quantum computing for chemistry and physics applications from a Monte Carlo perspective", The Journal of Chemical Physics 160 1, 010901 (2024).

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[59] Jie Liu, Zhenyu Li, and Jinlong Yang, "An efficient adaptive variational quantum solver of the Schrödinger equation based on reduced density matrices", The Journal of Chemical Physics 154 24, 244112 (2021).

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[64] Igor O. Sokolov, Panagiotis Kl. Barkoutsos, Lukas Moeller, Philippe Suchsland, Guglielmo Mazzola, and Ivano Tavernelli, "Microcanonical and finite-temperature ab initio molecular dynamics simulations on quantum computers", Physical Review Research 3 1, 013125 (2021).

[65] Seonghoon Choi, Ignacio Loaiza, and Artur F. Izmaylov, "Fluid fermionic fragments for optimizing quantum measurements of electronic Hamiltonians in the variational quantum eigensolver", Quantum 7, 889 (2023).

[66] Francisco Escudero, David Fernández-Fernández, Gabriel Jaumà, Guillermo F. Peñas, and Luciano Pereira, "Hardware-Efficient Entangled Measurements for Variational Quantum Algorithms", Physical Review Applied 20 3, 034044 (2023).

[67] Smik Patel and Artur F. Izmaylov, "Exactly solvable Hamiltonian fragments obtained from a direct sum of Lie algebras", The Journal of Chemical Physics 160 19, 194107 (2024).

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[69] Bálint Koczor and Simon C. Benjamin, "Quantum analytic descent", Physical Review Research 4 2, 023017 (2022).

[70] Philipp Schleich, Jakob S. Kottmann, and Alán Aspuru-Guzik, "Improving the accuracy of the variational quantum eigensolver for molecular systems by the explicitly-correlated perturbative [2]R12-correction", Physical Chemistry Chemical Physics 24 22, 13550 (2022).

[71] Gregory Boyd and Bálint Koczor, "Training Variational Quantum Circuits with CoVaR: Covariance Root Finding with Classical Shadows", Physical Review X 12 4, 041022 (2022).

[72] Thomas E. O'Brien, Michael Streif, Nicholas C. Rubin, Raffaele Santagati, Yuan Su, William J. Huggins, Joshua J. Goings, Nikolaj Moll, Elica Kyoseva, Matthias Degroote, Christofer S. Tautermann, Joonho Lee, Dominic W. Berry, Nathan Wiebe, and Ryan Babbush, "Efficient quantum computation of molecular forces and other energy gradients", Physical Review Research 4 4, 043210 (2022).

[73] Daniel McNulty, Filip B. Maciejewski, and Michał Oszmaniec, "Estimating Quantum Hamiltonians via Joint Measurements of Noisy Noncommuting Observables", Physical Review Letters 130 10, 100801 (2023).

[74] Yumin Dong, Jianshe Xie, Wanbin Hu, Cheng Liu, and Yi Luo, "Variational algorithm of quantum neural network based on quantum particle swarm", Journal of Applied Physics 132 10, 104401 (2022).

[75] Edison M. Murairi, Michael J. Cervia, Hersh Kumar, Paulo F. Bedaque, and Andrei Alexandru, "How many quantum gates do gauge theories require?", Physical Review D 106 9, 094504 (2022).

[76] Yong-Xin Yao, Niladri Gomes, Feng Zhang, Cai-Zhuang Wang, Kai-Ming Ho, Thomas Iadecola, and Peter P. Orth, "Adaptive Variational Quantum Dynamics Simulations", PRX Quantum 2 3, 030307 (2021).

[77] Hsin-Yuan Huang, Richard Kueng, and John Preskill, "Efficient Estimation of Pauli Observables by Derandomization", Physical Review Letters 127 3, 030503 (2021).

[78] Wataru Inoue, Koki Aoyama, Yusuke Teranishi, Keita Kanno, Yuya O. Nakagawa, and Kosuke Mitarai, "Almost optimal measurement scheduling of molecular Hamiltonian via finite projective plane", Physical Review Research 6 1, 013096 (2024).

[79] Chee-Kong Lee, Chang-Yu Hsieh, Shengyu Zhang, and Liang Shi, "Variational Quantum Simulation of Chemical Dynamics with Quantum Computers", Journal of Chemical Theory and Computation 18 4, 2105 (2022).

[80] Joshua Morris, Valeria Saggio, Aleksandra Gočanin, and Borivoje Dakić, "Quantum Verification and Estimation with Few Copies", Advanced Quantum Technologies 5 5, 2100118 (2022).

[81] Mario Motta, William Kirby, Ieva Liepuoniute, Kevin J Sung, Jeffrey Cohn, Antonio Mezzacapo, Katherine Klymko, Nam Nguyen, Nobuyuki Yoshioka, and Julia E Rice, "Subspace methods for electronic structure simulations on quantum computers", Electronic Structure 6 1, 013001 (2024).

[82] Jérôme F. Gonthier, Maxwell D. Radin, Corneliu Buda, Eric J. Doskocil, Clena M. Abuan, and Jhonathan Romero, "Measurements as a roadblock to near-term practical quantum advantage in chemistry: Resource analysis", Physical Review Research 4 3, 033154 (2022).

[83] Baraa Tantawi, Hamza Kamel Ahmed, Malak Magdy, and Gehad Ismail Sayed, Advances in Healthcare Information Systems and Administration 87 (2023) ISBN:9781668489130.

[84] Anirban Mukherjee, Noah F. Berthusen, João C. Getelina, Peter P. Orth, and Yong-Xin Yao, "Comparative study of adaptive variational quantum eigensolvers for multi-orbital impurity models", Communications Physics 6 1, 4 (2023).

[85] Tomochika Kurita, Hammam Qassim, Masatoshi Ishii, Hirotaka Oshima, Shintaro Sato, and Joseph Emerson, "Synergetic quantum error mitigation by randomized compiling and zero-noise extrapolation for the variational quantum eigensolver", Quantum 7, 1184 (2023).

[86] Luis A. Martínez-Martínez, Tzu-Ching Yen, and Artur F. Izmaylov, "Assessment of various Hamiltonian partitionings for the electronic structure problem on a quantum computer using the Trotter approximation", Quantum 7, 1086 (2023).

[87] Lewis W. Anderson, Martin Kiffner, Panagiotis Kl. Barkoutsos, Ivano Tavernelli, Jason Crain, and Dieter Jaksch, "Coarse-grained intermolecular interactions on quantum processors", Physical Review A 105 6, 062409 (2022).

[88] Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S. Kottmann, Tim Menke, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, and Alán Aspuru-Guzik, "Noisy intermediate-scale quantum algorithms", Reviews of Modern Physics 94 1, 015004 (2022).

[89] Sam McArdle, Suguru Endo, Alán Aspuru-Guzik, Simon C. Benjamin, and Xiao Yuan, "Quantum computational chemistry", Reviews of Modern Physics 92 1, 015003 (2020).

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[91] James Stokes, Josh Izaac, Nathan Killoran, and Giuseppe Carleo, "Quantum Natural Gradient", Quantum 4, 269 (2020).

[92] Chris Cade, Lana Mineh, Ashley Montanaro, and Stasja Stanisic, "Strategies for solving the Fermi-Hubbard model on near-term quantum computers", Physical Review B 102 23, 235122 (2020).

[93] Sam McArdle, Suguru Endo, Alan Aspuru-Guzik, Simon Benjamin, and Xiao Yuan, "Quantum computational chemistry", arXiv:1808.10402, (2018).

[94] Andrew Arrasmith, Lukasz Cincio, Rolando D. Somma, and Patrick J. Coles, "Operator Sampling for Shot-frugal Optimization in Variational Algorithms", arXiv:2004.06252, (2020).

[95] Andrew Zhao, Andrew Tranter, William M. Kirby, Shu Fay Ung, Akimasa Miyake, and Peter J. Love, "Measurement reduction in variational quantum algorithms", Physical Review A 101 6, 062322 (2020).

[96] Jonas M. Kübler, Andrew Arrasmith, Lukasz Cincio, and Patrick J. Coles, "An Adaptive Optimizer for Measurement-Frugal Variational Algorithms", Quantum 4, 263 (2020).

[97] Xavier Bonet-Monroig, Ryan Babbush, and Thomas E. O'Brien, "Nearly Optimal Measurement Scheduling for Partial Tomography of Quantum States", Physical Review X 10 3, 031064 (2020).

[98] Zhenyu Cai, "Resource Estimation for Quantum Variational Simulations of the Hubbard Model", Physical Review Applied 14 1, 014059 (2020).

[99] Charles Hadfield, Sergey Bravyi, Rudy Raymond, and Antonio Mezzacapo, "Measurements of Quantum Hamiltonians with Locally-Biased Classical Shadows", arXiv:2006.15788, (2020).

[100] Barnaby van Straaten and Bálint Koczor, "Measurement Cost of Metric-Aware Variational Quantum Algorithms", PRX Quantum 2 3, 030324 (2021).

[101] Charles Hadfield, Sergey Bravyi, Rudy Raymond, and Antonio Mezzacapo, "Measurements of Quantum Hamiltonians with Locally-Biased Classical Shadows", Communications in Mathematical Physics 391 3, 951 (2022).

[102] Ikko Hamamura and Takashi Imamichi, "Efficient evaluation of quantum observables using entangled measurements", npj Quantum Information 6, 56 (2020).

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[104] Giacomo Torlai, Guglielmo Mazzola, Giuseppe Carleo, and Antonio Mezzacapo, "Precise measurement of quantum observables with neural-network estimators", Physical Review Research 2 2, 022060 (2020).

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[106] Tzu-Ching Yen and Artur F. Izmaylov, "Cartan Subalgebra Approach to Efficient Measurements of Quantum Observables", PRX Quantum 2 4, 040320 (2021).

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[120] Shi-Ning Sun, Mario Motta, Ruslan N. Tazhigulov, Adrian T. K. Tan, Garnet Kin-Lic Chan, and Austin J. Minnich, "Quantum Computation of Finite-Temperature Static and Dynamical Properties of Spin Systems Using Quantum Imaginary Time Evolution", arXiv:2009.03542, (2020).

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[124] Gregory Boyd, "Low-Overhead Parallelisation of LCU via Commuting Operators", arXiv:2312.00696, (2023).

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The above citations are from Crossref's cited-by service (last updated successfully 2024-05-27 03:23:44) and SAO/NASA ADS (last updated successfully 2024-05-27 03:23:45). The list may be incomplete as not all publishers provide suitable and complete citation data.

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