Generative Data Intelligence

Molecular Quantum Circuit Design: A Graph-Based Approach

Date:

Jakob S. Kottmann

Institute of Computer Science, University of Augsburg, Germany

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Abstract

Science is rich in abstract concepts that capture complex processes in astonishingly simple ways. A prominent example is the reduction of molecules to simple graphs. This work introduces a design principle for parametrized quantum circuits based on chemical graphs, providing a way forward in three major obstacles in quantum circuit design for molecular systems: Operator ordering, parameter initialization and initial state preparation. It allows physical interpretation of each individual component and provides an heuristic to qualitatively estimate the difficulty of preparing ground states for individual instances of molecules.

https://jakobkottmann.com/posts/mol-circuits/

An integral part of science is the formulation of abstract concepts capable to capture the essential aspects of complex processes while remaining as simple as possible. For the construction of quantum circuits, such concepts are in high demand as most methodologies often lack simplicity and interpretability.

In Chemistry, one of the most prominent examples is the reduction of molecules to simple graphs with the atomic nuclei as vertices connected by edges representing so-called chemical bonds. In this work circuit designs based on chemical graphs are developed. This allows to construct quantum circuits that prepare electronic states of molecules, form two types of primitive elements: Orbital rotations and pair-correlators. The chemical graph is employed as a guiding heuristic to correctly arrange and initialize these building blocks.

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[1] 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. Rev. Mod. Phys., 94: 015004, Feb 2022. 10.1103/​RevModPhys.94.015004. URL https:/​/​link.aps.org/​doi/​10.1103/​RevModPhys.94.015004.
https:/​/​doi.org/​10.1103/​RevModPhys.94.015004

[2] Andrew Arrasmith, Ryan Babbush, Simon C Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, et al. Variational quantum algorithms. Nature Reviews Physics, 3 (9): 625–644, 2021. 10.1038/​s42254-021-00348-9. URL https:/​/​doi.org/​10.1038/​s42254-021-00348-9.
https:/​/​doi.org/​10.1038/​s42254-021-00348-9

[3] Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik. The theory of variational hybrid quantum-classical algorithms. New J. Phys., 18 (2): 023023, 2016. 10.1088/​1367-2630/​18/​2/​023023. URL https:/​/​doi.org/​10.1088/​1367-2630/​18/​2/​023023.
https:/​/​doi.org/​10.1088/​1367-2630/​18/​2/​023023

[4] Sukin Sim, Peter D Johnson, and Alán Aspuru-Guzik. Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Adv. Quantum Technol., 2 (12): 1900070, 2019. 10.1002/​qute.201900070. URL https:/​/​doi.org/​10.1002/​qute.201900070.
https:/​/​doi.org/​10.1002/​qute.201900070

[5] Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M. Chow, and Jay M. Gambetta. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature, 549 (7671): 242–246, 2017. ISSN 14764687. 10.1038/​nature23879. URL http:/​/​arxiv.org/​abs/​1704.05018.
https:/​/​doi.org/​10.1038/​nature23879
arXiv:1704.05018

[6] Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, Ryan Babbush, and Hartmut Neven. Barren plateaus in quantum neural network training landscapes. Nat. Commun., 9 (1): 1–6, 2018. 10.1038/​s41467-018-07090-4. URL https:/​/​doi.org/​10.1038/​s41467-018-07090-4.
https:/​/​doi.org/​10.1038/​s41467-018-07090-4

[7] Alba Cervera-Lierta, Jakob S. Kottmann, and Alá n Aspuru-Guzik. Meta-variational quantum eigensolver: Learning energy profiles of parameterized hamiltonians for quantum simulation. PRX Quantum, 2 (2), may 2021. 10.1103/​prxquantum.2.020329. URL https:/​/​doi.org/​10.1103/​prxquantum.2.020329.
https:/​/​doi.org/​10.1103/​prxquantum.2.020329

[8] Dan-Bo Zhang and Tao Yin. Collective optimization for variational quantum eigensolvers. Phys. Rev. A, 101: 032311, Mar 2020. 10.1103/​PhysRevA.101.032311. URL https:/​/​link.aps.org/​doi/​10.1103/​PhysRevA.101.032311.
https:/​/​doi.org/​10.1103/​PhysRevA.101.032311

[9] Frederic Sauvage, Sukin Sim, Alexander A. Kunitsa, William A. Simon, Marta Mauri, and Alejandro Perdomo-Ortiz. Flip: A flexible initializer for arbitrarily-sized parametrized quantum circuits. 2021. 10.48550/​arXiv.2103.08572. URL https:/​/​doi.org/​10.48550/​arXiv.2103.08572.
https:/​/​doi.org/​10.48550/​arXiv.2103.08572

[10] Jack Ceroni, Torin F. Stetina, Maria Kieferova, Carlos Ortiz Marrero, Juan Miguel Arrazola, and Nathan Wiebe. Generating approximate ground states of molecules using quantum machine learning. 2023. 10.48550/​arXiv.2210.05489. URL https:/​/​doi.org/​10.48550/​arXiv.2210.05489.
https:/​/​doi.org/​10.48550/​arXiv.2210.05489

[11] Abhinav Anand, Philipp Schleich, Sumner Alperin-Lea, Phillip W. K. Jensen, Sukin Sim, Manuel Díaz-Tinoco, Jakob S. Kottmann, Matthias Degroote, Artur F. Izmaylov, and Alán Aspuru-Guzik. A quantum computing view on unitary coupled cluster theory. Chem. Soc. Rev., 51: 1659–1684, 2022. 10.1039/​D1CS00932J. URL http:/​/​dx.doi.org/​10.1039/​D1CS00932J.
https:/​/​doi.org/​10.1039/​D1CS00932J

[12] Jakob S. Kottmann and Alán Aspuru-Guzik. Optimized low-depth quantum circuits for molecular electronic structure using a separable-pair approximation. Phys. Rev. A, 105: 032449, Mar 2022. 10.1103/​PhysRevA.105.032449. URL https:/​/​link.aps.org/​doi/​10.1103/​PhysRevA.105.032449.
https:/​/​doi.org/​10.1103/​PhysRevA.105.032449

[13] Joonho Lee, William J Huggins, Martin Head-Gordon, and K Birgitta Whaley. Generalized unitary coupled cluster wave functions for quantum computation. J. Chem. Theory Comput., 15 (1): 311–324, 2018. 10.1021/​acs.jctc.8b01004. URL https:/​/​doi.org/​10.1021/​acs.jctc.8b01004.
https:/​/​doi.org/​10.1021/​acs.jctc.8b01004

[14] Panagiotis Kl. Barkoutsos, Jerome F. Gonthier, Igor Sokolov, Nikolaj Moll, Gian Salis, Andreas Fuhrer, Marc Ganzhorn, Daniel J. Egger, Matthias Troyer, Antonio Mezzacapo, Stefan Filipp, and Ivano Tavernelli. Quantum algorithms for electronic structure calculations: Particle-hole Hamiltonian and optimized wave-function expansions. Phys. Rev. A, 98 (2): 022322, August 2018. ISSN 2469-9926, 2469-9934. 10.1103/​PhysRevA.98.022322. URL https:/​/​link.aps.org/​doi/​10.1103/​PhysRevA.98.022322.
https:/​/​doi.org/​10.1103/​PhysRevA.98.022322

[15] Dave Wecker, Matthew B Hastings, and Matthias Troyer. Progress towards practical quantum variational algorithms. Phys. Rev. A, 92 (4): 042303, 2015. 10.1103/​PhysRevA.92.042303. URL https:/​/​doi.org/​10.1103/​PhysRevA.92.042303.
https:/​/​doi.org/​10.1103/​PhysRevA.92.042303

[16] Harper R Grimsley, Daniel Claudino, Sophia E Economou, Edwin Barnes, and Nicholas J Mayhall. Is the trotterized uccsd ansatz chemically well-defined? J. Chem. Theory Comput., 2019a. 10.1021/​acs.jctc.9b01083. URL https:/​/​doi.org/​10.1021/​acs.jctc.9b01083.
https:/​/​doi.org/​10.1021/​acs.jctc.9b01083

[17] Francesco A. Evangelista, Garnet Kin-Lic Chan, and Gustavo E. Scuseria. Exact parameterization of fermionic wave functions via unitary coupled cluster theory. J. Chem. Phys., 151 (24): 244112, 2019. 10.1063/​1.5133059. URL https:/​/​doi.org/​10.1063/​1.5133059.
https:/​/​doi.org/​10.1063/​1.5133059

[18] Artur F. Izmaylov, Manuel Díaz-Tinoco, and Robert A. Lang. On the order problem in construction of unitary operators for the variational quantum eigensolver. Phys. Chem. Chem. Phys., 22: 12980–12986, 2020. 10.1039/​D0CP01707H. URL http:/​/​dx.doi.org/​10.1039/​D0CP01707H.
https:/​/​doi.org/​10.1039/​D0CP01707H

[19] Ilya G Ryabinkin, Tzu-Ching Yen, Scott N Genin, and Artur F Izmaylov. Qubit coupled cluster method: a systematic approach to quantum chemistry on a quantum computer. J. Chem. Theory Comput., 14 (12): 6317–6326, 2018. 10.1021/​acs.jctc.8b00932. URL https:/​/​doi.org/​10.1021/​acs.jctc.8b00932.
https:/​/​doi.org/​10.1021/​acs.jctc.8b00932

[20] Ilya G Ryabinkin, Robert A Lang, Scott N Genin, and Artur F Izmaylov. Iterative qubit coupled cluster approach with efficient screening of generators. J. Chem. Theory Comput., 16 (2): 1055–1063, 2020. 10.1021/​acs.jctc.9b01084. URL https:/​/​doi.org/​10.1021/​acs.jctc.9b01084.
https:/​/​doi.org/​10.1021/​acs.jctc.9b01084

[21] Harper R Grimsley, Sophia E Economou, Edwin Barnes, and Nicholas J Mayhall. An adaptive variational algorithm for exact molecular simulations on a quantum computer. Nat. commun., 10 (1): 1–9, 2019b. 10.1038/​s41467-019-10988-2. URL https:/​/​doi.org/​10.1038/​s41467-019-10988-2.
https:/​/​doi.org/​10.1038/​s41467-019-10988-2

[22] Ho Lun Tang, V.O. Shkolnikov, George S. Barron, Harper R. Grimsley, Nicholas J. Mayhall, Edwin Barnes, and Sophia E. Economou. Qubit-adapt-vqe: An adaptive algorithm for constructing hardware-efficient ansätze on a quantum processor. PRX Quantum, 2: 020310, Apr 2021. 10.1103/​PRXQuantum.2.020310. URL https:/​/​link.aps.org/​doi/​10.1103/​PRXQuantum.2.020310.
https:/​/​doi.org/​10.1103/​PRXQuantum.2.020310

[23] Harper R. Grimsley, George S. Barron, Edwin Barnes, Sophia E. Economou, and Nicholas J. Mayhall. Adaptive, problem-tailored variational quantum eigensolver mitigates rough parameter landscapes and barren plateaus. npj Quantum Information, 9 (1), mar 2023. 10.1038/​s41534-023-00681-0. URL https:/​/​doi.org/​10.1038.
https:/​/​doi.org/​10.1038/​s41534-023-00681-0

[24] Dmitry A. Fedorov, Yuri Alexeev, Stephen K. Gray, and Matthew Otten. Unitary Selective Coupled-Cluster Method. Quantum, 6: 703, May 2022. ISSN 2521-327X. 10.22331/​q-2022-05-02-703. URL https:/​/​doi.org/​10.22331/​q-2022-05-02-703.
https:/​/​doi.org/​10.22331/​q-2022-05-02-703

[25] Gian-Luca R Anselmetti, David Wierichs, Christian Gogolin, and Robert M Parrish. Local, expressive, quantum-number-preserving vqe ansätze for fermionic systems. New Journal of Physics, 23 (11): 113010, nov 2021. 10.1088/​1367-2630/​ac2cb3. URL https:/​/​dx.doi.org/​10.1088/​1367-2630/​ac2cb3.
https:/​/​doi.org/​10.1088/​1367-2630/​ac2cb3

[26] Bryan T. Gard, Linghua Zhu, George S. Barron, Nicholas J. Mayhall, Sophia E. Economou, and Edwin Barnes. Efficient symmetry-preserving state preparation circuits for the variational quantum eigensolver algorithm. npj Quantum Inf., 6 (1): 1–9, January 2020. ISSN 2056-6387. 10.1038/​s41534-019-0240-1. URL https:/​/​www.nature.com/​articles/​s41534-019-0240-1.
https:/​/​doi.org/​10.1038/​s41534-019-0240-1
https:/​/​www.nature.com/​articles/​s41534-019-0240-1

[27] Nicholas C Rubin, Joonho Lee, and Ryan Babbush. Compressing many-body fermion operators under unitary constraints. Journal of Chemical Theory and Computation, 18 (3): 1480–1488, 2022. 10.1021/​acs.jctc.1c00912. URL https:/​/​doi.org/​10.1021/​acs.jctc.1c00912.
https:/​/​doi.org/​10.1021/​acs.jctc.1c00912

[28] Jakob S. Kottmann, Abhinav Anand, and Alán Aspuru-Guzik. A feasible approach for automatically differentiable unitary coupled-cluster on quantum computers. Chem. Sci., 12: 3497–3508, 2021a. 10.1039/​D0SC06627C. URL http:/​/​dx.doi.org/​10.1039/​D0SC06627C.
https:/​/​doi.org/​10.1039/​D0SC06627C

[29] Armin Khamoshi, Francesco A Evangelista, and Gustavo E Scuseria. Correlating AGP on a quantum computer. Quantum Science and Technology, 6 (1): 014004, nov 2020. 10.1088/​2058-9565/​abc1bb. URL https:/​/​doi.org/​10.1088.
https:/​/​doi.org/​10.1088/​2058-9565/​abc1bb

[30] Armin Khamoshi, Guo P Chen, Francesco A Evangelista, and Gustavo E Scuseria. AGP-based unitary coupled cluster theory for quantum computers. Quantum Science and Technology, 8 (1): 015006, nov 2022. 10.1088/​2058-9565/​ac93ae. URL https:/​/​doi.org/​10.1088.
https:/​/​doi.org/​10.1088/​2058-9565/​ac93ae

[31] Alán Aspuru-Guzik, Anthony D Dutoi, Peter J Love, and Martin Head-Gordon. Simulated quantum computation of molecular energies. Science, 309 (5741): 1704–1707, 2005. 10.1126/​science.1113479. URL https:/​/​www.science.org/​doi/​10.1126/​science.1113479.
https:/​/​doi.org/​10.1126/​science.1113479

[32] Mario Motta, Chong Sun, Adrian TK Tan, Matthew J O’Rourke, Erika Ye, Austin J Minnich, Fernando GSL Brandão, and Garnet Kin-Lic Chan. Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution. Nat. Phys., 16 (2): 205–210, 2020. 10.1038/​s41567-019-0704-4. URL https:/​/​doi.org/​10.1038/​s41567-019-0704-4.
https:/​/​doi.org/​10.1038/​s41567-019-0704-4

[33] 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. PRX Quantum, 2 (1), feb 2021. 10.1103/​prxquantum.2.010317. URL https:/​/​doi.org/​10.1103.
https:/​/​doi.org/​10.1103/​prxquantum.2.010317

[34] Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J Love, Alán Aspuru-Guzik, and Jeremy L O’brien. A variational eigenvalue solver on a photonic quantum processor. Nat. Commun., 5: 4213, 2014. 10.1038/​ncomms5213. URL https:/​/​doi.org/​10.1038/​ncomms5213.
https:/​/​doi.org/​10.1038/​ncomms5213

[35] Mario Krenn, Jakob S. Kottmann, Nora Tischler, and Alán Aspuru-Guzik. Conceptual understanding through efficient automated design of quantum optical experiments. Physical Review X, 11 (3), aug 2021. 10.1103/​physrevx.11.031044. URL https:/​/​doi.org/​10.1103.
https:/​/​doi.org/​10.1103/​physrevx.11.031044

[36] Jakob S Kottmann, Mario Krenn, Thi Ha Kyaw, Sumner Alperin-Lea, and Alán Aspuru-Guzik. Quantum computer-aided design of quantum optics hardware. Quantum Science and Technology, 6 (3): 035010, aug 2021b. 10.1088/​2058-9565/​abfc94. URL https:/​/​dx.doi.org/​10.1088/​2058-9565/​abfc94.
https:/​/​doi.org/​10.1088/​2058-9565/​abfc94

[37] Mario Krenn, Xuemei Gu, and Anton Zeilinger. Quantum experiments and graphs: Multiparty states as coherent superpositions of perfect matchings. Phys. Rev. Lett., 119: 240403, Dec 2017. 10.1103/​PhysRevLett.119.240403. URL https:/​/​link.aps.org/​doi/​10.1103/​PhysRevLett.119.240403.
https:/​/​doi.org/​10.1103/​PhysRevLett.119.240403

[38] Jakob S. Kottmann and Francesco Scala. Compact effective basis generation: Insights from interpretable circuit design. arxiv:2302.10660, 2023. 10.48550/​arXiv.2302.10660. URL https:/​/​doi.org/​10.48550/​arXiv.2302.10660.
https:/​/​doi.org/​10.48550/​arXiv.2302.10660
arXiv:2302.10660

[39] Jakob S Kottmann, Philipp Schleich, Teresa Tamayo-Mendoza, and Alán Aspuru-Guzik. Reducing qubit requirements while maintaining numerical precision for the variational quantum eigensolver: A basis-set-free approach. J. Phys. Chem. Lett., 12 (1): 663, 2021c. 10.1021/​acs.jpclett.0c03410. URL https:/​/​doi.org/​10.1021/​acs.jpclett.0c03410.
https:/​/​doi.org/​10.1021/​acs.jpclett.0c03410

[40] Jakob S Kottmann, Florian A Bischoff, and Edward F Valeev. Direct determination of optimal pair-natural orbitals in a real-space representation: The second-order moller–plesset energy. The Journal of Chemical Physics, 152 (7): 074105, 2020. 10.1063/​1.5141880. URL https:/​/​doi.org/​10.1063/​1.5141880.
https:/​/​doi.org/​10.1063/​1.5141880

[41] Sason Shaik, David Danovich, and Philippe C. Hiberty. On the nature of the chemical bond in valence bond theory. The Journal of Chemical Physics, 157 (9): 090901, 09 2022. 10.1063/​5.0095953. URL https:/​/​doi.org/​10.1063/​5.0095953.
https:/​/​doi.org/​10.1063/​5.0095953

[42] William A Goddard III, Thom H Dunning Jr, William J Hunt, and P Jeffrey Hay. Generalized valence bond description of bonding in low-lying states of molecules. Accounts of Chemical Research, 6 (11): 368–376, 1973. 10.1021/​ar50071a002. URL https:/​/​doi.org/​10.1021/​ar50071a002.
https:/​/​doi.org/​10.1021/​ar50071a002

[43] Sason Shaik and Philippe C. Hiberty. Valence Bond Theory, Its History, Fundamentals, and Applications: A Primer, chapter 1, pages 1–100. John Wiley & Sons, Ltd, 2004. ISBN 9780471678854. https:/​/​doi.org/​10.1002/​0471678856.ch1. URL https:/​/​doi.org/​10.1002/​0471678856.ch1.
https:/​/​doi.org/​10.1002/​0471678856.ch1

[44] Henrik R. Larsson, Carlos A. Jiménez-Hoyos, and Garnet Kin-Lic Chan. Minimal matrix product states and generalizations of mean-field and geminal wave functions. Journal of Chemical Theory and Computation, 16 (8): 5057–5066, Jun 2020. ISSN 1549-9626. 10.1021/​acs.jctc.0c00463. URL http:/​/​dx.doi.org/​10.1021/​acs.jctc.0c00463.
https:/​/​doi.org/​10.1021/​acs.jctc.0c00463

[45] Wataru Mizukami, Kosuke Mitarai, Yuya O. Nakagawa, Takahiro Yamamoto, Tennin Yan, and Yu-ya Ohnishi. Orbital optimized unitary coupled cluster theory for quantum computer. Phys. Rev. Research, 2: 033421, Sep 2020. 10.1103/​PhysRevResearch.2.033421. URL https:/​/​link.aps.org/​doi/​10.1103/​PhysRevResearch.2.033421.
https:/​/​doi.org/​10.1103/​PhysRevResearch.2.033421

[46] Igor O. Sokolov, Panagiotis Kl. Barkoutsos, Pauline J. Ollitrault, Donny Greenberg, Julia Rice, Marco Pistoia, and Ivano Tavernelli. Quantum orbital-optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical equivalents? J. Chem. Phys., 152 (12): 124107, 2020. 10.1063/​1.5141835. URL https:/​/​doi.org/​10.1063/​1.5141835.
https:/​/​doi.org/​10.1063/​1.5141835

[47] Jonathan Romero, Jonathan P Olson, and Alan Aspuru-Guzik. Quantum autoencoders for efficient compression of quantum data. Quantum Sci. Technol., 2 (4): 045001, Aug 2017. ISSN 2058-9565. 10.1088/​2058-9565/​aa8072. URL http:/​/​dx.doi.org/​10.1088/​2058-9565/​aa8072.
https:/​/​doi.org/​10.1088/​2058-9565/​aa8072

[48] Yordan S Yordanov, David RM Arvidsson-Shukur, and Crispin HW Barnes. Efficient quantum circuits for quantum computational chemistry. Phys. Rev. A, 102 (6): 062612, 2020. 10.1103/​PhysRevA.102.062612. URL https:/​/​doi.org/​10.1103/​PhysRevA.102.062612.
https:/​/​doi.org/​10.1103/​PhysRevA.102.062612

[49] Ian D. Kivlichan, Jarrod McClean, Nathan Wiebe, Craig Gidney, Alán Aspuru-Guzik, Garnet Kin-Lic Chan, and Ryan Babbush. Quantum simulation of electronic structure with linear depth and connectivity. Phys. Rev. Lett., 120: 110501, Mar 2018. 10.1103/​PhysRevLett.120.110501. URL https:/​/​link.aps.org/​doi/​10.1103/​PhysRevLett.120.110501.
https:/​/​doi.org/​10.1103/​PhysRevLett.120.110501

[50] Google AI Quantum et al. Hartree-fock on a superconducting qubit quantum computer. Science, 369 (6507): 1084–1089, 2020. 10.1126/​science.abb981. URL https:/​/​science.sciencemag.org/​content/​369/​6507/​1084.
https:/​/​doi.org/​10.1126/​science.abb981
https:/​/​science.sciencemag.org/​content/​369/​6507/​1084

[51] Henrik R. Larsson, Huanchen Zhai, C. J. Umrigar, and Garnet Kin-Lic Chan. The chromium dimer: Closing a chapter of quantum chemistry. Journal of the American Chemical Society, 144 (35): 15932–15937, 2022. 10.1021/​jacs.2c06357. URL https:/​/​doi.org/​10.1021/​jacs.2c06357. PMID: 36001866.
https:/​/​doi.org/​10.1021/​jacs.2c06357

[52] Hugh G. A. Burton, Daniel Marti-Dafcik, David P. Tew, and David J. Wales. Exact electronic states with shallow quantum circuits through global optimisation, 2022. URL https:/​/​arxiv.org/​abs/​2207.00085.
arXiv:2207.00085

[53] Jakob S. Kottmann, Sumner Alperin-Lea, Teresa Tamayo-Mendoza, Alba Cervera-Lierta, Cyrille Lavigne, Yen Tzu-Ching, Vladislav Verteletsky, Philipp Schleich, Matthias Degroote, Skylar Chaney, Maha Kesibo, Naomi G. Curnow, Brandon Solo, Georgios Tsilimigkounakis, Claudia Zendejas-Morales, Artur Izmaylov, Alan Aspuru-Guzik, Francesco Scala, and Gaurav Saxena. Tequila: A platform for rapid development of quantum algorithms, November 2023. URL https:/​/​github.com/​tequilahub/​tequila.
https:/​/​github.com/​tequilahub/​tequila

[54] Jakob S Kottmann, Sumner Alperin-Lea, Teresa Tamayo-Mendoza, Alba Cervera-Lierta, Cyrille Lavigne, Tzu-Ching Yen, Vladyslav Verteletskyi, Philipp Schleich, Abhinav Anand, Matthias Degroote, Skylar Chaney, Maha Kesibi, Naomi Grace Curnow, Brandon Solo, Georgios Tsilimigkounakis, Claudia Zendejas-Morales, Artur F Izmaylov, and Alán Aspuru-Guzik. TEQUILA: a platform for rapid development of quantum algorithms. Quantum Science and Technology, 6 (2): 024009, mar 2021d. 10.1088/​2058-9565/​abe567. URL https:/​/​doi.org/​10.1088/​2058-9565/​abe567.
https:/​/​doi.org/​10.1088/​2058-9565/​abe567

[55] Qiming Sun, Timothy C. Berkelbach, Nick S. Blunt, George H. Booth, Sheng Guo, Zhendong Li, Junzi Liu, James D. McClain, Elvira R. Sayfutyarova, Sandeep Sharma, Sebastian Wouters, and Garnet Kin-Lic Chan. Pyscf: the python-based simulations of chemistry framework. Wiley Interdiscip. Rev. Comput. Mol. Sci., 8 (1): e1340, 2018. 10.1002/​wcms.1340. URL https:/​/​doi.org/​10.1002/​wcms.1340.
https:/​/​doi.org/​10.1002/​wcms.1340

[56] Qiming Sun, Xing Zhang, Samragni Banerjee, Peng Bao, Marc Barbry, Nick S. Blunt, Nikolay A. Bogdanov, George H. Booth, Jia Chen, Zhi-Hao Cui, Janus J. Eriksen, and et. al. Recent developments in the pyscf program package. J. Chem. Phys., 153 (2): 024109, 2020. 10.1063/​5.0006074. URL https:/​/​doi.org/​10.1063/​5.0006074.
https:/​/​doi.org/​10.1063/​5.0006074

[57] Robert J Harrison, Gregory Beylkin, Florian A Bischoff, Justus A Calvin, George I Fann, Jacob Fosso-Tande, Diego Galindo, Jeff R Hammond, Rebecca Hartman-Baker, Judith C Hill, et al. Madness: A multiresolution, adaptive numerical environment for scientific simulation. SIAM Journal on Scientific Computing, 38 (5): S123–S142, 2016. 10.1137/​15M1026171. URL https:/​/​doi.org/​10.1137/​15M1026171.
https:/​/​doi.org/​10.1137/​15M1026171

[58] Robert J Harrison, George I Fann, Takeshi Yanai, Zhengting Gan, and Gregory Beylkin. Multiresolution quantum chemistry: Basic theory and initial applications. The Journal of chemical physics, 121 (23): 11587–11598, 2004. 10.1063/​1.1791051. URL https:/​/​doi.org/​10.1063/​1.1791051.
https:/​/​doi.org/​10.1063/​1.1791051

[59] Florian A Bischoff. Regularizing the molecular potential in electronic structure calculations. I. SCF methods. The Journal of chemical physics, 141 (18): 184105, 2014. 10.1063/​1.4901021. URL https:/​/​doi.org/​10.1063/​1.4901021.
https:/​/​doi.org/​10.1063/​1.4901021

[60] Yasunari Suzuki, Yoshiaki Kawase, Yuya Masumura, Yuria Hiraga, Masahiro Nakadai, Jiabao Chen, Ken M. Nakanishi, Kosuke Mitarai, Ryosuke Imai, Shiro Tamiya, Takahiro Yamamoto, Tennin Yan, Toru Kawakubo, Yuya O. Nakagawa, Yohei Ibe, Youyuan Zhang, Hirotsugu Yamashita, Hikaru Yoshimura, Akihiro Hayashi, and Keisuke Fujii. Qulacs: a fast and versatile quantum circuit simulator for research purpose. Quantum, 5: 559, October 2021. ISSN 2521-327X. 10.22331/​q-2021-10-06-559. URL https:/​/​doi.org/​10.22331/​q-2021-10-06-559.
https:/​/​doi.org/​10.22331/​q-2021-10-06-559

[61] Jarrod McClean, Nicholas Rubin, Kevin Sung, Ian David Kivlichan, Xavier Bonet-Monroig, Yudong Cao, Chengyu Dai, Eric Schuyler Fried, Craig Gidney, Brendan Gimby, et al. Openfermion: the electronic structure package for quantum computers. Quantum Sci. Technol., 2020. 10.1088/​2058-9565/​ab8ebc. URL https:/​/​doi.org/​10.1088/​2058-9565/​ab8ebc.
https:/​/​doi.org/​10.1088/​2058-9565/​ab8ebc

[62] James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, and Skye Wanderman-Milne. JAX: composable transformations of Python+NumPy programs, 2018. URL http:/​/​github.com/​google/​jax.
http:/​/​github.com/​google/​jax

[63] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake Vand erPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1. 0 Contributors. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17: 261–272, 2020. 10.1038/​s41592-019-0686-2. URL https:/​/​doi.org/​10.1038/​s41592-019-0686-2.
https:/​/​doi.org/​10.1038/​s41592-019-0686-2

[64] Christopher J. Stein and Markus Reiher. Automated selection of active orbital spaces. Journal of Chemical Theory and Computation, 12 (4): 1760–1771, mar 2016. 10.1021/​acs.jctc.6b00156. URL https:/​/​doi.org/​10.1021.
https:/​/​doi.org/​10.1021/​acs.jctc.6b00156

[65] 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. Phys. Chem. Chem. Phys., 24: 13550–13564, 2022. 10.1039/​D2CP00247G. URL http:/​/​dx.doi.org/​10.1039/​D2CP00247G.
https:/​/​doi.org/​10.1039/​D2CP00247G

[66] Lexin Ding, Stefan Knecht, Zoltán Zimborás, and Christian Schilling. Quantum correlations in molecules: A quantum information toolbox for chemists. 2022. 10.48550/​ARXIV.2205.15881. URL https:/​/​arxiv.org/​abs/​2205.15881.
https:/​/​doi.org/​10.48550/​ARXIV.2205.15881
arXiv:2205.15881

[67] Katharina Boguslawski, Pawel Tecmer, Gergely Barcza, Örs Legeza, and Markus Reiher. Orbital entanglement in bond-formation processes. Journal of Chemical Theory and Computation, 9 (7): 2959–2973, 2013. 10.1021/​ct400247p. URL https:/​/​doi.org/​10.1021/​ct400247p. PMID: 26583979.
https:/​/​doi.org/​10.1021/​ct400247p

[68] C. Krumnow, L. Veis, Ö. Legeza, and J. Eisert. Fermionic orbital optimization in tensor network states. Physical Review Letters, 117 (21), nov 2016. 10.1103/​physrevlett.117.210402. URL https:/​/​doi.org/​10.1103.
https:/​/​doi.org/​10.1103/​physrevlett.117.210402

[69] Zi-Jian Zhang, Thi Ha Kyaw, Jakob S. Kottmann, Matthias Degroote, and Alan Aspuru-Guzik. Mutual information-assisted adaptive variational quantum eigensolver. Quantum Sci. and Technol., 2021. 10.1088/​2058-9565/​abdca4. URL http:/​/​iopscience.iop.org/​article/​10.1088/​2058-9565/​abdca4.
https:/​/​doi.org/​10.1088/​2058-9565/​abdca4

[70] Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik. Self-referencing embedded strings (selfies): A 100 string representation. Machine Learning: Science and Technology, 1 (4): 045024, oct 2020. 10.1088/​2632-2153/​aba947. URL https:/​/​dx.doi.org/​10.1088/​2632-2153/​aba947.
https:/​/​doi.org/​10.1088/​2632-2153/​aba947

[71] Richard Meister, Cica Gustiani, and Simon C. Benjamin. Exploring ab initio machine synthesis of quantum circuits. 2022. 10.48550/​ARXIV.2206.11245. URL https:/​/​arxiv.org/​abs/​2206.11245.
https:/​/​doi.org/​10.48550/​ARXIV.2206.11245
arXiv:2206.11245

[72] Cica Gustiani, Richard Meister, and Simon C. Benjamin. Exploiting subspace constraints and ab initio variational methods for quantum chemistry. 2022. 10.48550/​ARXIV.2206.11246. URL https:/​/​arxiv.org/​abs/​2206.11246.
https:/​/​doi.org/​10.48550/​ARXIV.2206.11246
arXiv:2206.11246

[73] Sergey B Bravyi and Alexei Yu Kitaev. Fermionic quantum computation. Ann. Phys., 298 (1): 210, 2002. URL https:/​/​doi.org/​10.1006/​aphy.2002.6254.
https:/​/​doi.org/​10.1006/​aphy.2002.6254

[74] Riley W. Chien and James D. Whitfield. Custom fermionic codes for quantum simulation. arXiv:2009.11860, 2020. URL https:/​/​arxiv.org/​abs/​2009.11860.
arXiv:2009.11860

[75] Kanav Setia and James D. Whitfield. Bravyi-kitaev superfast simulation of electronic structure on a quantum computer. J. Chem. Phys., 148 (16): 164104, 2018. 10.1063/​1.5019371. URL https:/​/​doi.org/​10.1063/​1.5019371.
https:/​/​doi.org/​10.1063/​1.5019371

[76] Charles Derby, Joel Klassen, Johannes Bausch, and Toby Cubitt. Compact fermion to qubit mappings. Phys. Rev. B, 104: 035118, Jul 2021. 10.1103/​PhysRevB.104.035118. URL https:/​/​link.aps.org/​doi/​10.1103/​PhysRevB.104.035118.
https:/​/​doi.org/​10.1103/​PhysRevB.104.035118

[77] Philipp Schleich, Joseph Boen, Lukasz Cincio, Abhinav Anand, Jakob S. Kottmann, Sergei Tretiak, Pavel A. Dub, and Alán Aspuru-Guzik. Partitioning quantum chemistry simulations with clifford circuits. 2023. https:/​/​doi.org/​10.48550/​arXiv.2303.01221. URL https:/​/​doi.org/​10.48550/​arXiv.2303.01221.
https:/​/​doi.org/​10.48550/​arXiv.2303.01221

[78] Saad Yalouz, Bruno Senjean, Jakob Günther, Francesco Buda, Thomas E O’Brien, and Lucas Visscher. A state-averaged orbital-optimized hybrid quantum–classical algorithm for a democratic description of ground and excited states. Quantum Science and Technology, 6 (2): 024004, jan 2021. 10.1088/​2058-9565/​abd334. URL https:/​/​doi.org/​10.1088/​2058-9565/​abd334.
https:/​/​doi.org/​10.1088/​2058-9565/​abd334

Cited by

[1] Philipp Schleich, Joseph Boen, Lukasz Cincio, Abhinav Anand, Jakob S. Kottmann, Sergei Tretiak, Pavel A. Dub, and Alán Aspuru-Guzik, “Partitioning Quantum Chemistry Simulations with Clifford Circuits”, arXiv:2303.01221, (2023).

[2] Jakob S. Kottmann and Francesco Scala, “Compact Effective Basis Generation: Insights from Interpretable Circuit Design”, arXiv:2302.10660, (2023).

The above citations are from SAO/NASA ADS (last updated successfully 2023-08-04 02:12:20). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref’s cited-by service no data on citing works was found (last attempt 2023-08-04 02:12:19).

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