HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware

Sawaya, Nicolas P. D.; Marti-Dafcik, Daniel; Ho, Yang; Tabor, Daniel P.; Neira, David Bernal; Magann, Alicia B.; Premaratne, Shavindra; Dubey, Pradeep; Matsuura, Anne; Bishop, Nathan; Jong, Wibe de; Benjamin, Simon; Parekh, Ojas; Tubman, Norm; Klymko, Katherine; Camps, Daan

doi:10.22331/q-2024-12-11-1559

Title: HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware

Journal Article · 10 December 2024 · Quantum

DOI: https://doi.org/10.22331/q-2024-12-11-1559 · OSTI ID:2502144

Sawaya, Nicolas P. D. ^[1]; Marti-Dafcik, Daniel ^[2]; Ho, Yang ^[3]; Tabor, Daniel P. ^[4]; Neira, David Bernal ^[5]; Magann, Alicia B. ^[3]; Premaratne, Shavindra ^[6]; Dubey, Pradeep ^[7]; Matsuura, Anne ^[6]; Bishop, Nathan ^[8]; Jong, Wibe de ^[9]; Benjamin, Simon ^[10]; Parekh, Ojas ^[3]; Tubman, Norm ^[11]; Klymko, Katherine ^[12]; Camps, Daan ^[12]

Azulene Labs, San Francisco, CA (United States); Intel Labs, Santa Clara, CA (United States)
Univ. of Oxford (United Kingdom). Clarendon Lab.
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Texas A & M Univ., College Station, TX (United States)
Ames Research Center, Moffett Field, CA (United States); Universities Space Research Association, Mountain View, CA (United States); Purdue Univ., West Lafayette, IN (United States)
Intel Labs, Hillsboro, OR (United States)
Intel Labs, Santa Clara, CA (United States)
Intel Corporation, Hillsboro, OR (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Univ. of Oxford (United Kingdom)
Ames Research Center, Moffett Field, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

In order to characterize and benchmark computational hardware, software, and algorithms, it is essential to have many problem instances on-hand. This is no less true for quantum computation, where a large collection of real-world problem instances would allow for benchmarking studies that in turn help to improve both algorithms and hardware designs. To this end, here we present a large dataset of qubit-based quantum Hamiltonians. The dataset, called HamLib (for Hamiltonian Library), is freely available online and contains problem sizes ranging from 2 to 1000 qubits. HamLib includes problem instances of the Heisenberg model, Fermi-Hubbard model, Bose-Hubbard model, molecular electronic structure, molecular vibrational structure, MaxCut, Max- k -SAT, Max- k -Cut, QMaxCut, and the traveling salesperson problem. The goals of this effort are (a) to save researchers time by eliminating the need to prepare problem instances and map them to qubit representations, (b) to allow for more thorough tests of new algorithms and hardware, and (c) to allow for reproducibility and standardization across research studies.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: ESPRC; National Aeronautics and Space Administration (NASA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); Welch Foundation

Grant/Contract Number:: AC02-05CH11231; NA0003525

OSTI ID:: 2502144

Report Number(s):: SAND2025--00375J

Journal Information:: Quantum, Journal Name: Quantum Vol. 8; ISSN 2521-327X

Publisher:: Quantum Science Open CommunityCopyright Statement

Country of Publication:: United States

Language:: English

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A Quantum Approximate Optimization Algorithm Farhi, Edward; Goldstone, Jeffrey; Gutmann, Sam arXiv https://doi.org/10.48550/arXiv.1411.4028	preprint	January 2014
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Scalable Benchmarks for Gate-Based Quantum Computers Cornelissen, Arjan; Bausch, Johannes; Gilyén, András arXiv https://doi.org/10.48550/arXiv.2104.10698	preprint	January 2021
Quantum technologies for climate change: Preliminary assessment Berger, Casey; Di Paolo, Agustin; Forrest, Tracey arXiv https://doi.org/10.48550/arXiv.2107.05362	preprint	January 2021
QDataset: Quantum Datasets for Machine Learning Perrier, Elija; Youssry, Akram; Ferrie, Chris arXiv https://doi.org/10.48550/arXiv.2108.06661	preprint	January 2021
Towards a variational Jordan-Lee-Preskill quantum algorithm Liu, Junyu; Li, Zimu; Zheng, Han arXiv https://doi.org/10.48550/arXiv.2109.05547	text	January 2021
Application-Oriented Performance Benchmarks for Quantum Computing Lubinski, Thomas; Johri, Sonika; Varosy, Paul arXiv https://doi.org/10.48550/arXiv.2110.03137	preprint	January 2021
Provably accurate simulation of gauge theories and bosonic systems Tong, Yu; Albert, Victor V.; McClean, Jarrod R. arXiv https://doi.org/10.48550/arXiv.2110.06942	text	January 2021
Predicting parameters for the Quantum Approximate Optimization Algorithm for MAX-CUT from the infinite-size limit Boulebnane, Sami; Montanaro, Ashley arXiv https://doi.org/10.48550/arXiv.2110.10685	preprint	January 2021
NISQ: Error Correction, Mitigation, and Noise Simulation Cao, Ningping; Lin, Junan; Kribs, David arXiv https://doi.org/10.48550/arXiv.2111.02345	preprint	January 2021
An LLVM-based C++ Compiler Toolchain for Variational Hybrid Quantum-Classical Algorithms and Quantum Accelerators Khalate, Pradnya; Wu, Xin-Chuan; Premaratne, Shavindra arXiv https://doi.org/10.48550/arXiv.2202.11142	preprint	January 2022
Arline Benchmarks: Automated Benchmarking Platform for Quantum Compilers Kharkov, Y.; Ivanova, A.; Mikhantiev, E. arXiv https://doi.org/10.48550/arXiv.2202.14025	preprint	January 2022
Encoding trade-offs and design toolkits in quantum algorithms for discrete optimization: coloring, routing, scheduling, and other problems Sawaya, Nicolas PD; Schmitz, Albert T.; Hadfield, Stuart arXiv https://doi.org/10.48550/arXiv.2203.14432	text	January 2022
An Optimal Product-State Approximation for 2-Local Quantum Hamiltonians with Positive Terms Parekh, Ojas; Thompson, Kevin arXiv https://doi.org/10.48550/arXiv.2206.08342	preprint	January 2022
VeriQBench: A Benchmark for Multiple Types of Quantum Circuits Chen, Kean; Fang, Wang; Guan, Ji arXiv https://doi.org/10.48550/arXiv.2206.10880	preprint	January 2022
Benchmarking of Different Optimizers in the Variational Quantum Algorithms for Applications in Quantum Chemistry Singh, Harshdeep; Mishra, Sabyashachi; Majumder, Sonjoy arXiv https://doi.org/10.48550/arXiv.2208.10285	text	January 2022
Optimizing quantum circuit parameters via SDP Lee, Eunou arXiv https://doi.org/10.48550/arXiv.2209.00789	preprint	January 2022
An Improved Approximation Algorithm for Quantum Max-Cut King, Robbie arXiv https://doi.org/10.48550/arXiv.2209.02589	text	January 2022
On the practical usefulness of the Hardware Efficient Ansatz Leone, Lorenzo; Oliviero, Salvatore F. E.; Cincio, Lukasz arXiv https://doi.org/10.48550/arXiv.2211.01477	preprint	January 2022
Graphical quantum Clifford-encoder compilers from the ZX calculus Khesin, Andrey Boris; Lu, Jonathan Z.; Shor, Peter W. arXiv https://doi.org/10.48550/arXiv.2301.02356	preprint	January 2023
Optimization Applications as Quantum Performance Benchmarks Lubinski, Thomas; Coffrin, Carleton; McGeoch, Catherine arXiv https://doi.org/10.48550/arXiv.2302.02278	preprint	January 2023
Variational Benchmarks for Quantum Many-Body Problems Wu, Dian; Rossi, Riccardo; Vicentini, Filippo arXiv https://doi.org/10.48550/arXiv.2302.04919	text	January 2023
VQE-generated Quantum Circuit Dataset for Machine Learning Nakayama, Akimoto; Mitarai, Kosuke; Placidi, Leonardo arXiv https://doi.org/10.48550/arXiv.2302.09751	preprint	January 2023
Optimization at the Interface of Unitary and Non-unitary Quantum Operations in PCOAST Schmitz, Albert T.; Ibrahim, Mohannad; Sawaya, Nicolas P. D. arXiv https://doi.org/10.48550/arXiv.2305.09843	preprint	January 2023
PCOAST: A Pauli-based Quantum Circuit Optimization Framework Paykin, Jennifer; Schmitz, Albert T.; Ibrahim, Mohannad arXiv https://doi.org/10.48550/arXiv.2305.10966	preprint	January 2023
Data is often loadable in short depth: Quantum circuits from tensor networks for finance, images, fluids, and proteins Jumade, Raghav; Sawaya, Nicolas PD arXiv https://doi.org/10.48550/arXiv.2309.13108	preprint	January 2023
Particle-conserving quantum circuit ansatz with applications in variational simulation of bosonic systems Bahrami, Sina; Sawaya, Nicolas arXiv https://doi.org/10.48550/arXiv.2402.18768	preprint	January 2024
Enhancing initial state overlap through orbital optimization for faster molecular electronic ground-state energy estimation Ollitrault, Pauline J.; Cortes, Cristian L.; Gonthier, Jerome F. arXiv https://doi.org/10.48550/arXiv.2404.08565	preprint	January 2024
Quantum Computation by Adiabatic Evolution Farhi, Edward; Goldstone, Jeffrey; Gutmann, Sam arXiv https://doi.org/10.48550/arXiv.quant-ph/0001106	preprint	January 2000
Quantum Computation, Complexity, and Many-Body Physics Somma, Rolando D. arXiv https://doi.org/10.48550/arXiv.quant-ph/0512209	preprint	January 2005

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