Sampling electronic structure quadratic unconstrained binary optimization problems (QUBOs) with Ocean and Mukai solvers
The most advanced D-Wave Advantage quantum annealer has 5000+ qubits, however, every qubit is connected to a small number of neighbors. As such, implementation of a fully-connected graph results in an order of magnitude reduction in qubit count. To compensate for the reduced number of qubits, one ha...
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| Published in: | PloS one Vol. 17; no. 2; p. e0263849 |
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11.02.2022
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | The most advanced D-Wave Advantage quantum annealer has 5000+ qubits, however, every qubit is connected to a small number of neighbors. As such, implementation of a fully-connected graph results in an order of magnitude reduction in qubit count. To compensate for the reduced number of qubits, one has to rely on special heuristic software such as qbsolv, the purpose of which is to decompose a large quadratic unconstrained binary optimization (QUBO) problem into smaller pieces that fit onto a quantum annealer. In this work, we compare the performance of the open-source qbsolv which is a part of the D-Wave Ocean tools and a new Mukai QUBO solver from Quantum Computing Inc. (QCI). The comparison is done for solving the electronic structure problem and is implemented in a classical mode (Tabu search techniques). The Quantum Annealer Eigensolver is used to map the electronic structure eigenvalue-eigenvector equation to a QUBO problem, solvable on a D-Wave annealer. We find that the Mukai QUBO solver outperforms the Ocean qbsolv with one to two orders of magnitude more accurate energies for all calculations done in the present work, both the ground and excited state calculations. This work stimulates the further development of software to assist in the utilization of modern quantum annealers. |
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| AbstractList | The most advanced D-Wave Advantage quantum annealer has 5000+ qubits, however, every qubit is connected to a small number of neighbors. As such, implementation of a fully-connected graph results in an order of magnitude reduction in qubit count. To compensate for the reduced number of qubits, one has to rely on special heuristic software such as qbsolv, the purpose of which is to decompose a large quadratic unconstrained binary optimization (QUBO) problem into smaller pieces that fit onto a quantum annealer. In this work, we compare the performance of the open-source qbsolv which is a part of the D-Wave Ocean tools and a new Mukai QUBO solver from Quantum Computing Inc. (QCI). The comparison is done for solving the electronic structure problem and is implemented in a classical mode (Tabu search techniques). The Quantum Annealer Eigensolver is used to map the electronic structure eigenvalue-eigenvector equation to a QUBO problem, solvable on a D-Wave annealer. We find that the Mukai QUBO solver outperforms the Ocean qbsolv with one to two orders of magnitude more accurate energies for all calculations done in the present work, both the ground and excited state calculations. This work stimulates the further development of software to assist in the utilization of modern quantum annealers. The most advanced D-Wave Advantage quantum annealer has 5000+ qubits, however, every qubit is connected to a small number of neighbors. As such, implementation of a fully-connected graph results in an order of magnitude reduction in qubit count. To compensate for the reduced number of qubits, one has to rely on special heuristic software such as qbsolv, the purpose of which is to decompose a large quadratic unconstrained binary optimization (QUBO) problem into smaller pieces that fit onto a quantum annealer. In this work, we compare the performance of the open-source qbsolv which is a part of the D-Wave Ocean tools and a new Mukai QUBO solver from Quantum Computing Inc. (QCI). The comparison is done for solving the electronic structure problem and is implemented in a classical mode (Tabu search techniques). The Quantum Annealer Eigensolver is used to map the electronic structure eigenvalue-eigenvector equation to a QUBO problem, solvable on a D-Wave annealer. We find that the Mukai QUBO solver outperforms the Ocean qbsolv with one to two orders of magnitude more accurate energies for all calculations done in the present work, both the ground and excited state calculations. This work stimulates the further development of software to assist in the utilization of modern quantum annealers.The most advanced D-Wave Advantage quantum annealer has 5000+ qubits, however, every qubit is connected to a small number of neighbors. As such, implementation of a fully-connected graph results in an order of magnitude reduction in qubit count. To compensate for the reduced number of qubits, one has to rely on special heuristic software such as qbsolv, the purpose of which is to decompose a large quadratic unconstrained binary optimization (QUBO) problem into smaller pieces that fit onto a quantum annealer. In this work, we compare the performance of the open-source qbsolv which is a part of the D-Wave Ocean tools and a new Mukai QUBO solver from Quantum Computing Inc. (QCI). The comparison is done for solving the electronic structure problem and is implemented in a classical mode (Tabu search techniques). The Quantum Annealer Eigensolver is used to map the electronic structure eigenvalue-eigenvector equation to a QUBO problem, solvable on a D-Wave annealer. We find that the Mukai QUBO solver outperforms the Ocean qbsolv with one to two orders of magnitude more accurate energies for all calculations done in the present work, both the ground and excited state calculations. This work stimulates the further development of software to assist in the utilization of modern quantum annealers. |
| Audience | Academic |
| Author | Dub, Pavel A. Mniszewski, Susan M. Teplukhin, Alexander Tretiak, Sergei Kendrick, Brian K. |
| AuthorAffiliation | 1 Theoretical Division (T-1, MS B221), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America 2 Computer, Computational and Statistical Sciences Division (CCS, MS B214), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America University of Calgary, CANADA 3 Chemistry Division (C-IIAC, MS K558), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America |
| AuthorAffiliation_xml | – name: 2 Computer, Computational and Statistical Sciences Division (CCS, MS B214), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America – name: University of Calgary, CANADA – name: 1 Theoretical Division (T-1, MS B221), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America – name: 3 Chemistry Division (C-IIAC, MS K558), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America |
| Author_xml | – sequence: 1 givenname: Alexander surname: Teplukhin fullname: Teplukhin, Alexander – sequence: 2 givenname: Brian K. surname: Kendrick fullname: Kendrick, Brian K. – sequence: 3 givenname: Susan M. orcidid: 0000-0002-0077-0537 surname: Mniszewski fullname: Mniszewski, Susan M. – sequence: 4 givenname: Sergei surname: Tretiak fullname: Tretiak, Sergei – sequence: 5 givenname: Pavel A. orcidid: 0000-0001-9750-6603 surname: Dub fullname: Dub, Pavel A. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35148343$$D View this record in MEDLINE/PubMed https://www.osti.gov/biblio/1845069$$D View this record in Osti.gov |
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| CitedBy_id | crossref_primary_10_1038_s41534_022_00599_z crossref_primary_10_1038_s41598_022_21163_x crossref_primary_10_1186_s40580_024_00425_6 |
| Cites_doi | 10.1103/PhysRevLett.99.070502 10.22331/q-2018-08-06-79 10.1021/acs.jctc.7b00174 10.1021/acs.jctc.9b00402 10.1137/1.9780898719604 10.1038/s41598-021-98331-y 10.1109/HPEC43674.2020.9286230 10.1215/S0012-7094-52-01910-8 10.1016/0305-0548(86)90048-1 10.1039/D0CP04272B 10.1103/RevModPhys.71.1267 10.1038/s41598-020-77315-4 10.1088/1361-6633/ab85b8 |
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| References | A Szabo (pone.0263849.ref014) 1996 A Teplukhin (pone.0263849.ref013) 2021; 11 A Mizel (pone.0263849.ref001) 2007; 99 A Teplukhin (pone.0263849.ref010) 2019; 15 P Hauke (pone.0263849.ref003) 2020; 83 E Anderson (pone.0263849.ref017) 1999 RM Parrish (pone.0263849.ref015) 2017; 13 pone.0263849.ref004 A Teplukhin (pone.0263849.ref012) 2020; 10 JA Pople (pone.0263849.ref018) 1999; 71 pone.0263849.ref006 pone.0263849.ref007 pone.0263849.ref008 pone.0263849.ref019 pone.0263849.ref009 J Preskill (pone.0263849.ref002) 2018; 2 A Brauer (pone.0263849.ref016) 1952; 19 F Glover (pone.0263849.ref005) 1986; 13 A Teplukhin (pone.0263849.ref011) 2020; 22 |
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| SubjectTerms | Annealing Approximation Computer and Information Sciences Computer programs computer software Computing Methodologies Earth Sciences Eigenvalues Eigenvectors Electron configuration Electronic structure Electronics - instrumentation Engineering and Technology ground state Laboratories Mathematical analysis MATHEMATICS AND COMPUTING ocean waves Oceans Optimization Physical Sciences Quadratic programming Quantum computing Quantum Theory qubits Qubits (quantum computing) Software Solvers Tabu search Variables |
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| Title | Sampling electronic structure quadratic unconstrained binary optimization problems (QUBOs) with Ocean and Mukai solvers |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35148343 https://www.proquest.com/docview/2627965493 https://www.proquest.com/docview/2628295605 https://www.osti.gov/biblio/1845069 https://pubmed.ncbi.nlm.nih.gov/PMC8836298 https://doaj.org/article/d6f32a3892b54fc48e2a6d57f15e83fc http://dx.doi.org/10.1371/journal.pone.0263849 |
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