Simulating challenging correlated molecules and materials on the Sycamore quantum processor

Tazhigulov, Ruslan N.; Haghshenas, Reza; Zhai, Huan-Chen; Tan, Adrian T. K.; Rubin, Nicholas C.; Babbush, Ryan; Minnich, Austin J.; Chan, Garnet Kin‐Lic

doi:10.48550/arxiv.2203.15291

Cited by 11 publications

(16 citation statements)

References 19 publications

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“…We are aware of some studies that are making the effort to realize quantum simulation as a practical application for quantum chemistry. Recently, Tazhigulov et al [38] simulated stronglycorrelated molecules that are more relevant to real world problems on Google's Sycamore quantum processor. They mapped the electronic structures of iron-sulfur molecular clusters and α-RuCl 3 into low-energy spin models using the results from theoretical and spectroscopic studies.…”

Section: Discussionmentioning

confidence: 99%

“…Researchers have worked towards molecules (BeH 2 , H 2 O, and H 12 ) with improved algorithms and hardware design [25,34,35]. Others also provide strategies to simulate slightly larger systems, such as CO 2 , C 2 H 4 , C 18 , and the nitrogenase iron-sulfur molecular clusters [36][37][38], using symmetries, fragmentation of molecules, spin-model simplifications, or novel qubit encoding methods [39][40][41][42][43][44]. However, these quantum simulation results are still quite limited in the problem size and often lack real world applications.…”

Section: Introductionmentioning

confidence: 99%

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Quantum Simulation of Preferred Tautomeric State Prediction

Shee¹,

Yeh²,

Hsiao³

et al. 2022

Preprint

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Prediction of tautomers plays an essential role in computer-aided drug discovery. However, it remains a challenging task nowadays to accurately predict the canonical tautomeric form of a given drug-like molecule. Lack of extensive tautomer databases, most likely due to the difficulty in experimental studies, hampers the development of effective empirical methods for tautomer predictions. A more accurate estimation of the stable tautomeric form can be achieved by quantum chemistry calculations. Yet, the computational cost required prevents quantum chemistry calculation as a standard tool for tautomer prediction in computer-aided drug discovery. In this paper we propose a hybrid quantum chemistry-quantum computation workflow to efficiently predict the dominant tautomeric form. Specifically, we select active-space molecular orbitals based on quantum chemistry methods. Then we utilize efficient encoding methods to map the Hamiltonian onto quantum devices to reduce the qubit resources and circuit depth. Finally, variational quantum eigensolver (VQE) algorithms are employed for ground state estimation where hardware-efficient ansatz circuits are used. To demonstrate the applicability of our methodology, we perform experiments on two tautomeric systems: acetone and Edaravone, each having 52 and 150 spin-orbitals in the STO-3G basis set, respectively. Our numerical results show that their tautomeric state prediction agrees with the CCSD benchmarks. Moreover, the required quantum resources are efficient: in the example of Edaravone, we could achieve chemical accuracy with only eight qubits and 80 two-qubit gates.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantum Simulation of Preferred Tautomeric State Prediction

Shee¹,

Yeh²,

Hsiao³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Following the same design principles as their fault-tolerant counterparts, most of the known NISQ algorithms for property estimation were derived by replacing demanding computational subroutines with their near-term versions. For example, Variational Quantum Simulation [23] and Quantum Imaginary Time Evolution [24] were used to calculate correlation functions in real [25] and imaginary time [26][27][28], respectively. Similarly, variational linear system solvers were applied to response function calculations [29].…”

Section: Introductionmentioning

confidence: 99%

A Near-Term Quantum Algorithm for Computing Molecular and Materials Properties based on Recursive Variational Series Methods

Jensen¹,

Johnson²,

Kunitsa³

2022

Preprint

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Determining properties of molecules and materials is one of the premier applications of quantum computing. A major question in the field is: how might we use imperfect near-term quantum computers to solve problems of practical value? We propose a quantum algorithm to estimate properties of molecules using near-term quantum devices. The method is a recursive variational series estimation method, where we expand an operator of interest in terms of Chebyshev polynomials, and evaluate each term in the expansion using a variational quantum algorithm. We test our method by computing the one-particle Green's function in energy domain and the autocorrelation function in time domain, finding some advantages to this approach over existing methods.

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“…Strongly correlated condensed matter systems are amongst those most likely to yield a quantum advantage [1][2][3][4][5][6]. These are systems in which the underlying spins or electrons are strongly renormalised and whose quantum properties are beyond the reach of standard perturbative or density functional type approaches.…”

Section: Introductionmentioning

confidence: 99%

Simulating groundstate and dynamical quantum phase transitions on a superconducting quantum computer

Dborin,

Wimalaweera,

Barratt

et al. 2022

Preprint

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We optimise a translationally invariant, sequential quantum circuit on a superconducting quantum device to simulate the groundstate of the quantum Ising model through its quantum critical point. We further demonstrate how the dynamical quantum critical point found in quenches of this model across its quantum critical point can be simulated. Our approach avoids finite-size scaling effects by using sequential quantum circuits inspired by infinite matrix product states. We provide efficient circuits and a variety of error mitigation strategies to implement, optimise and time-evolve these states. CONTENTS I. Introduction 1 II. Results 2 A. Quantum Phase Transitions 3 B. Groundstate Optimisation 3 C. Calculating and Optimising Overlaps 5 D. Quantum Dynamics 6 III. Discussion 7 IV. Methods 8 A. Fixed-point equations 8 B. Error Mitigation Strategies 8 References 9

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Simulating challenging correlated molecules and materials on the Sycamore quantum processor

Cited by 11 publications

References 19 publications

Quantum Simulation of Preferred Tautomeric State Prediction

Quantum Simulation of Preferred Tautomeric State Prediction

A Near-Term Quantum Algorithm for Computing Molecular and Materials Properties based on Recursive Variational Series Methods

Simulating groundstate and dynamical quantum phase transitions on a superconducting quantum computer

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