Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions (2312.09733v3)
Abstract: Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.
- R. P. Feynman, Simulating physics with computers, International Journal of Theoretical Physics 21 (1982).
- Argonne Leadership Computing Facility, Materials Science, https://www.alcf.anl.gov/domains/materials-science (2023).
- B. Austin et al., Nersc-10 workload analysis, https://portal.nersc.gov/project/m888/nersc10/workload/N10_Workload_Analysis.latest.pdf (2020).
- Donostia International Physics Center Supercomputing Center, Supercomputing Center, https://dipc.ehu.eus/en/supercomputing-center (2023), [Online; accessed 8-December-2023].
- A. Szabo and N. S. Ostlund, Modern quantum chemistry: introduction to advanced electronic structure theory (Courier Corporation, 2012).
- J. B. Schriber and F. A. Evangelista, Adaptive configuration interaction for computing challenging electronic excited states with tunable accuracy, Journal of Chemical Theory and Computation 13, 5354 (2017), pMID: 28892621, https://doi.org/10.1021/acs.jctc.7b00725 .
- C. Mejuto-Zaera, N. M. Tubman, and K. B. Whaley, Dynamical mean field theory simulations with the adaptive sampling configuration interaction method, Phys. Rev. B 100, 125165 (2019).
- J. Olsen, P. Jørgensen, and J. Simons, Passing the one-billion limit in full configuration-interaction (fci) calculations, Chemical Physics Letters 169, 463 (1990).
- C.-C. Chang, B. M. Rubenstein, and M. A. Morales, Auxiliary-field-based trial wave functions in quantum monte carlo calculations, Phys. Rev. B 94, 235144 (2016).
- G. H. Low and I. L. Chuang, Hamiltonian simulation by qubitization, Quantum 3, 163 (2019).
- S. Lloyd, Universal quantum simulators, Science , 1073 (1996).
- J. Kempe, A. Kitaev, and O. Regev, The complexity of the local hamiltonian problem, SIAM Journal on Computing 35, 1070 (2006).
- S. E. Smart and D. A. Mazziotti, Quantum-classical hybrid algorithm using an error-mitigating n-representability condition to compute the mott metal-insulator transition, Physical Review A 100, 022517 (2019).
- A. Y. Kitaev, Quantum measurements and the abelian stabilizer problem, arxiv preprint, arXiv:quant-ph/9511026 (1995).
- L. Lin and Y. Tong, Near-optimal ground state preparation, Quantum 4, 372 (2020).
- L. Lin and Y. Tong, Heisenberg-limited ground-state energy estimation for early fault-tolerant quantum computers, PRX Quantum 3, 010318 (2022).
- Y. Dong, L. Lin, and Y. Tong, Ground-state preparation and energy estimation on early fault-tolerant quantum computers via quantum eigenvalue transformation of unitary matrices, PRX Quantum 3, 040305 (2022).
- Z. Ding, C.-F. Chen, and L. Lin, Single-ancilla ground state preparation via lindbladians (2023a), arXiv:2308.15676 [quant-ph] .
- R. M. Parrish and P. L. McMahon, Quantum filter diagonalization: Quantum eigendecomposition without full quantum phase estimation, arXiv 10.48550/arXiv.1909.08925 (2019), arXiv:1909.08925 [quant-ph] .
- N. H. Stair, R. Huang, and F. A. Evangelista, A multireference quantum krylov algorithm for strongly correlated electrons, Journal of Chemical Theory and Computation, Journal of Chemical Theory and Computation 16, 2236 (2020).
- J. Cohn, M. Motta, and R. M. Parrish, Quantum filter diagonalization with compressed double-factorized hamiltonians, PRX Quantum 2, 040352 (2021a).
- K. Seki and S. Yunoki, Quantum power method by a superposition of time-evolved states, PRX Quantum 2, 010333 (2021).
- C. L. Cortes and S. K. Gray, Quantum krylov subspace algorithms for ground- and excited-state energy estimation, Phys. Rev. A 105, 022417 (2022).
- G. Lee, D. Lee, and J. Huh, Sampling error analysis in quantum krylov subspace diagonalization, arXiv 10.48550/arXiv.2307.16279 (2023), arXiv:2307.16279 [quant-ph] .
- W. Kirby, M. Motta, and A. Mezzacapo, Exact and efficient Lanczos method on a quantum computer, Quantum 7, 1018 (2023).
- E. N. Epperly, L. Lin, and Y. Nakatsukasa, A theory of quantum subspace diagonalization, SIAM Journal on Matrix Analysis and Applications 43, 1263 (2022).
- B. Olmos, I. Lesanovsky, and J. P. Garrahan, Facilitated spin models of dissipative quantum glasses, Physical Review Letters 109, 020403 (2012).
- V. May and O. Kühn, Charge and energy transfer dynamics in molecular systems (John Wiley & Sons, 2023).
- A. Nitzan, Chemical dynamics in condensed phases: relaxation, transfer and reactions in condensed molecular systems (Oxford university press, 2006).
- T. S. Cubitt, Dissipative ground state preparation and the dissipative quantum eigensolver, arXiv preprint arXiv:2303.11962 (2023).
- J. Hubisz, B. Sambasivam, and J. Unmuth-Yockey, Quantum algorithms for open lattice field theory, Physical Review A 104, 052420 (2021).
- G. Lindblad, On the generators of quantum dynamical semigroups, Communications in Mathematical Physics 48, 119 (1976).
- H. Wang, S. Ashhab, and F. Nori, Quantum algorithm for simulating the dynamics of an open quantum system, Physical Review A 83, 062317 (2011).
- Z. Ding, X. Li, and L. Lin, Simulating open quantum systems using hamiltonian simulations, arXiv preprint arXiv:2311.15533 (2023c).
- A. M. Childs and T. Li, Efficient simulation of sparse markovian quantum dynamics, arXiv preprint arXiv:1611.05543 (2016).
- R. Cleve and C. Wang, Efficient quantum algorithms for simulating lindblad evolution, arXiv preprint arXiv:1612.09512 (2016).
- M. Pocrnic, D. Segal, and N. Wiebe, Quantum simulation of lindbladian dynamics via repeated interactions, arXiv preprint arXiv:2312.05371 (2023).
- D. Patel and M. M. Wilde, Wave matrix lindbladization i: Quantum programs for simulating markovian dynamics, Open Systems & Information Dynamics 30, 2350010 (2023a).
- D. Poulin and P. Wocjan, Sampling from the thermal quantum gibbs state and evaluating partition functions with a quantum computer, Physical Review Letters 103, 10.1103/physrevlett.103.220502 (2009).
- A. N. Chowdhury and R. D. Somma, Quantum algorithms for gibbs sampling and hitting-time estimation (2016), arXiv:1603.02940 [quant-ph] .
- M.-H. Yung and A. Aspuru-Guzik, A quantum–quantum metropolis algorithm, Proceedings of the National Academy of Sciences 109, 754–759 (2012).
- P. Wocjan and K. Temme, Szegedy walk unitaries for quantum maps, Communications in Mathematical Physics 402, 3201–3231 (2023).
- E. Davies, Quantum Theory of Open Systems (Academic Press, 1976).
- P. Rall, C. Wang, and P. Wocjan, Thermal state preparation via rounding promises, Quantum 7, 1132 (2023).
- C.-F. Chen, M. J. Kastoryano, and A. Gilyén, An efficient and exact noncommutative quantum gibbs sampler (2023b), arXiv:2311.09207 [quant-ph] .
- A. W. Harrow, A. Hassidim, and S. Lloyd, Quantum algorithm for linear systems of equations, Physical review letters 103, 150502 (2009).
- Copyright, in The Finite Element Method in Engineering (Fifth Edition), edited by S. S. Rao (Butterworth-Heinemann, Boston, 2011) fifth edition ed., p. iv.
- A. Montanaro and S. Pallister, Quantum algorithms and the finite element method, Phys. Rev. A 93, 032324 (2016).
- N. Linden, A. Montanaro, and C. Shao, Quantum vs. classical algorithms for solving the heat equation, Communications in Mathematical Physics 395, 601 (2022).
- J. Zhang, F. Feng, and Q. J. Zhang, Quantum method for finite element simulation of electromagnetic problems, in 2021 IEEE MTT-S International Microwave Symposium (IMS) (2021) pp. 120–123.
- A. M. Childs, R. Kothari, and R. D. Somma, Quantum algorithm for systems of linear equations with exponentially improved dependence on precision, SIAM Journal on Computing 46, 1920 (2017), https://doi.org/10.1137/16M1087072 .
- D. Fang, L. Lin, and Y. Tong, Time-marching based quantum solvers for time-dependent linear differential equations, Quantum 7, 955 (2023).
- H. Krovi, Improved quantum algorithms for linear and nonlinear differential equations, Quantum 7, 913 (2023).
- S. Jin, N. Liu, and Y. Yu, Quantum simulation of partial differential equations: Applications and detailed analysis, Phys. Rev. A 108, 032603 (2023).
- L.-A. Wu and D. Lidar, Qubits as parafermions, Journal of Mathematical Physics 43, 4506 (2002).
- N. P. D. Sawaya and J. Huh, Quantum algorithm for calculating molecular vibronic spectra, The Journal of Physical Chemistry Letters 10, 3586 (2019).
- A. Kan and Y. Nam, Lattice quantum chromodynamics and electrodynamics on a universal quantum computer, arXiv preprint arXiv:2107.12769 (2021).
- A. Kan and Y. Nam, Simulating lattice quantum electrodynamics on a quantum computer, Quantum Science and Technology 8, 015008 (2022).
- P. Jordan and E. Wigner, Über das paulische äquivalenzverbot., Z. Phys. 47, 631 (1928).
- S. B. Bravyi and A. Y. Kitaev, Fermionic quantum computation, Annals of Physics 298, 210 (2002).
- F. Verstraete and J. I. Cirac, Mapping local hamiltonians of fermions to local hamiltonians of spins, Journal of Statistical Mechanics: Theory and Experiment 2005, P09012 (2005).
- J. T. Seeley, M. J. Richard, and P. J. Love, The bravyi-kitaev transformation for quantum computation of electronic structure, The Journal of Chemical Physics 137, 224109 (2012).
- K. Setia and J. D. Whitfield, Bravyi-Kitaev Superfast simulation of electronic structure on a quantum computer, The Journal of Chemical Physics 148, 164104 (2018), https://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/1.5019371/13715475/164104_1_online.pdf .
- M. Steudtner and S. Wehner, Fermion-to-qubit mappings with varying resource requirements for quantum simulation, New Journal of Physics 20, 063010 (2018).
- M. Steudtner, Methods to simulate fermions on quantum computers with hardware limitations, Ph.D. thesis, Leiden University (2019).
- C. Derby and J. Klassen, A compact fermion to qubit mapping part 2: Alternative lattice geometries, arXiv preprint, arXiv:2101.10735 (2021), arXiv:2101.10735 [quant-ph] .
- R. W. Chien and J. Klassen, Optimizing fermionic encodings for both hamiltonian and hardware, arXiv preprint, arXiv:2210.05652 10.48550/arXiv.2210.05652 (2021), arXiv:2210.05652 [quant-ph] .
- Y.-A. Chen and Y. Xu, Equivalence between fermion-to-qubit mappings in two spatial dimensions, PRX Quantum 4, 010326 (2023).
- R. P. Feynman, Simulating physics with computers, in Feynman and computation (CRC Press, 2018) pp. 133–153.
- L. A. Martínez-Martínez, T.-C. Yen, and A. F. Izmaylov, Assessment of various hamiltonian partitionings for the electronic structure problem on a quantum computer using the trotter approximation, Quantum 7, 1086 (2023).
- Z. Shangnan, Quantum semi-supervised learning with quantum supremacy, arXiv preprint arXiv:2110.02343 (2021).
- Y. Nam and D. Maslov, Low-cost quantum circuits for classically intractable instances of the hamiltonian dynamics simulation problem, npj Quantum Information 5, 44 (2019).
- D. W. Berry and A. M. Childs, Black-box hamiltonian simulation and unitary implementation, Quantum Info. Comput. 12, 29 (2012).
- A. M. Childs and N. Wiebe, Hamiltonian simulation using linear combinations of unitary operations, Quantum Info. Comput. 12, 901–924 (2012).
- I. Loaiza and A. F. Izmaylov, Block-invariant symmetry shift: Preprocessing technique for second-quantized hamiltonians to improve their decompositions to linear combination of unitaries, arXiv:2304.13772 (2023a).
- I. Loaiza and A. F. Izmaylov, Majorana tensor decomposition: A unifying framework for decompositions of fermionic hamiltonians to linear combination of unitaries, arXiv:2311.XXX (2023b).
- S. Choi and A. F. Izmaylov, Measurement optimization techniques for excited electronic states in near-term quantum computing algorithms, J. Chem. Theory Comput. (arXiv:2302.11421) 19, 3184 (2023).
- H.-Y. Huang, R. Kueng, and J. Preskill, Predicting many properties of a quantum system from very few measurements, Nat. Phys. 16, 1050 (2020a).
- A. Zhao, N. C. Rubin, and A. Miyake, Fermionic partial tomography via classical shadows, Phys. Rev. Lett. 127, 110504 (2021).
- B. O’Gorman, Fermionic tomography and learning (2022), arXiv:2207.14787 [quant-ph] .
- H.-Y. Huang, R. Kueng, and J. Preskill, Predicting many properties of a quantum system from very few measurements, Nature Physics 16, 1050 (2020b).
- H.-Y. Huang, R. Kueng, and J. Preskill, Efficient estimation of Pauli observables by derandomization, Phys. Rev. Lett. 127, 030503 (2021).
- C. Hadfield, Adaptive Pauli shadows for energy estimation, arXiv:2105.12207 (2021), 2105.12207 .
- J. M. Lukens, K. J. Law, and R. S. Bennink, A Bayesian analysis of classical shadows, npj Quantum Information 7, 1 (2021).
- D. E. Koh and S. Grewal, Classical shadows with noise, Quantum 6, 776 (2022).
- A. Acharya, S. Saha, and A. M. Sengupta, Shadow tomography based on informationally complete positive operator-valued measure, Phys. Rev. A 104, 052418 (2021).
- T.-C. Yen and A. F. Izmaylov, Cartan subalgebra approach to efficient measurements of quantum observables, PRX Quantum 2, 040320 (2021).
- S. Choi, I. Loaiza, and A. F. Izmaylov, Fluid fermionic fragments for optimizing quantum measurements of electronic hamiltonians in the variational quantum eigensolver, Quantum 7, 889 (2023).
- S. Choi, T.-C. Yen, and A. F. Izmaylov, Improving quantum measurements by introducing “ghost” pauli products, J. Chem. Theory Comput. (arXiv:2208.06563) 18, 7394 (2022).
- Z. Cai, Quantum error mitigation using symmetry expansion, Quantum 5, 548 (2021).
- J. Cohn, M. Motta, and R. M. Parrish, Quantum filter diagonalization with compressed double-factorized hamiltonians, PRX Quantum 2, 040352 (2021b).
- Z. Cai, Resource estimation for quantum variational simulations of the hubbard model, Phys. Rev. Appl. 14, 014059 (2020).
- H. Ma, M. Govoni, and G. Galli, Quantum simulations of materials on near-term quantum computers, npj Computational Materials 6, 85 (2020a).
- B. O. Roos, P. R. Taylor, and P. E. Sigbahn, A complete active space scf method (casscf) using a density matrix formulated super-ci approach, Chem. Phys. 48, 157 (1980).
- S. R. White, Density matrix formulation for quantum renormalization groups, Physical Review Letters 69, 2863 (1992).
- R. J. Buenker and S. D. Peyerimhoff, Individualized configuration selection in CI calculations with subsequent energy extrapolation, Theoretica chimica acta 35, 33 (1974).
- G. Knizia and G. K.-L. Chan, Density matrix embedding: A simple alternative to dynamical mean-field theory, Phys. Rev. Lett. 109, 186404 (2012a).
- G. Knizia and G. K.-L. Chan, Density matrix embedding: A strong-coupling quantum embedding theory, J. Chem. Theory Comput. 9, 1428 (2013a), https://doi.org/10.1021/ct301044e .
- H. Q. Pham, M. R. Hermes, and L. Gagliardi, Periodic electronic structure calculations with the density matrix embedding theory, Journal of Chemical Theory and Computation 16, 130 (2019).
- Z.-H. Cui, T. Zhu, and G. K.-L. Chan, Efficient implementation of ab initio quantum embedding in periodic systems: Density matrix embedding theory, Journal of Chemical Theory and Computation 16, 119 (2020a).
- N-electron valence state perturbation theory: A fast implementation of the strongly contracted variant, Chem. Phys. Lett 350, 297 (2001).
- A. Mitra, M. R. Hermes, and L. Gagliardi, Density Matrix Embedding Using Multiconfiguration Pair-Density Functional Theory, Journal of Chemical Theory and Computation 10.1021/acs.jctc.3c00247 (2023a).
- P. Sharma, D. G. Truhlar, and L. Gagliardi, Magnetic coupling in a tris-hydroxo-bridged chromium dimer occurs through ligand mediated superexchange in conjunction with through-space coupling, Journal of the American Chemical Society 142, 16644–16650 (2020).
- M. R. Hermes and L. Gagliardi, Multiconfigurational Self-Consistent Field Theory with Density Matrix Embedding: The Localized Active Space Self-Consistent Field Method, Journal of Chemical Theory and Computation 15, 972 (2019a).
- M. R. Hermes, R. Pandharkar, and L. Gagliardi, Variational Localized Active Space Self-Consistent Field Method, J. Chem. Theory Comput. 16, 4923 (2020).
- A. A. Holmes, N. M. Tubman, and C. J. Umrigar, Heat-Bath Configuration Interaction: An Efficient Selected Configuration Interaction Algorithm Inspired by Heat-Bath Sampling, Journal of Chemical Theory and Computation 12, 3674 (2016).
- C. J. Stein and M. Reiher, autoCAS: A Program for Fully Automated Multiconfigurational Calculations, Journal of Computational Chemistry 40, 2216 (2019).
- J. Li, B. A. Jones, and S. Kais, Toward perturbation theory methods on a quantum computer, Science Advances 9, eadg4576 (2023a).
- A. Khan, B. K. Clark, and N. M. Tubman, Pre-optimizing variational quantum eigensolvers with tensor networks, arXiv e-prints , arXiv:2310.12965 (2023), arXiv:2310.12965 [quant-ph] .
- J. W. Mullinax and N. M. Tubman, Large-scale sparse wavefunction circuit simulator for applications with the variational quantum eigensolver, arXiv e-prints , arXiv:2301.05726 (2023), arXiv:2301.05726 [quant-ph] .
- V. Giovannetti, S. Lloyd, and L. Maccone, Quantum random access memory, Physical review letters 100, 160501 (2008a).
- S. Aaronson, Read the fine print, Nature Physics 11, 291 (2015).
- P. Niroula and Y. Nam, A quantum algorithm for string matching, npj Quantum Information 7, 37 (2021).
- M. A. Nielsen and I. L. Chuang, Quantum computation and quantum information (Cambridge university press, 2010).
- P. Wittek, Quantum machine learning: what quantum computing means to data mining (Academic Press, 2014).
- H. Jiang, Z.-J. M. Shen, and J. Liu, Quantum computing methods for supply chain management, in 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) (IEEE, 2022) pp. 400–405.
- J. Liu, C. T. Hann, and L. Jiang, Data centers with quantum random access memory and quantum networks, Physical Review A 108, 032610 (2023b).
- J. Liu and L. Jiang, Quantum data center: Perspectives, arXiv preprint arXiv:2309.06641 (2023).
- V. Giovannetti, S. Lloyd, and L. Maccone, Quantum private queries, Physical review letters 100, 230502 (2008b).
- D. Gottesman, T. Jennewein, and S. Croke, Longer-baseline telescopes using quantum repeaters, Physical review letters 109, 070503 (2012).
- O. Di Matteo, V. Gheorghiu, and M. Mosca, Fault-tolerant resource estimation of quantum random-access memories, IEEE Transactions on Quantum Engineering 1, 1 (2020).
- V. Giovannetti, S. Lloyd, and L. Maccone, Architectures for a quantum random access memory, Physical Review A 78, 052310 (2008c).
- C. Hann, Practicality of Quantum Random Access Memory, Ph.D. thesis, Yale University (2021).
- S. Jaques and A. G. Rattew, Qram: A survey and critique, arXiv preprint arXiv:2305.10310 (2023).
- S. Bravyi, D. Maslov, and Y. Nam, Constant-cost implementations of clifford operations and multiply-controlled gates using global interactions, Physical Review Letters 129, 230501 (2022).
- A. Wu, Y. Ding, and A. Li, Collcomm: Enabling efficient collective quantum communication based on epr buffering, arXiv preprint arXiv:2208.06724 (2022).
- C. Piveteau and D. Sutter, Circuit knitting with classical communication, IEEE Transactions on Information Theory (2023).
- W. Tang and M. Martonosi, Scaleqc: A scalable framework for hybrid computation on quantum and classical processors, arXiv preprint arXiv:2207.00933 (2022).
- A. Cowtan, W. Simmons, and R. Duncan, A generic compilation strategy for the unitary coupled cluster ansatz, arXiv preprint arXiv:2007.10515 (2020).
- G. Li, Y. Shi, and A. Javadi-Abhari, Software-hardware co-optimization for computational chemistry on superconducting quantum processors, in 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA) (IEEE, 2021) pp. 832–845.
- S. Martiel and T. G. de Brugière, Architecture aware compilation of quantum circuits via lazy synthesis, Quantum 6, 729 (2022).
- M. Amy, P. Azimzadeh, and M. Mosca, On the controlled-not complexity of controlled-not–phase circuits, Quantum Science and Technology 4, 015002 (2018).
- V. Vandaele, S. Martiel, and T. G. de Brugière, Phase polynomials synthesis algorithms for nisq architectures and beyond, Quantum Science and Technology 7, 045027 (2022).
- A. Meijer-van de Griend and R. Duncan, Architecture-aware synthesis of phase polynomials for nisq devices, Electronic Proceedings in Theoretical Computer Science (2020).
- C. Jones, Novel constructions for the fault-tolerant toffoli gate, 2013, Phys. Rev. A 87, 022328.
- Y. Nam, Y. Su, and D. Maslov, Approximate quantum fourier transform with o (n log (n)) t gates, NPJ Quantum Information 6, 26 (2020b).
- C. Gidney and M. Ekerå, How to factor 2048 bit rsa integers in 8 hours using 20 million noisy qubits, Quantum 5, 433 (2021).
- D. Coppersmith, An approximate fourier transform useful in quantum factoring, arXiv preprint quant-ph/0201067 (2002).
- E. Campbell, A. Khurana, and A. Montanaro, Applying quantum algorithms to constraint satisfaction problems, Quantum 3, 167 (2019).
- S. Pabst and Y. Nam, A quantum algorithm for network reliability, arXiv preprint arXiv:2203.10201 (2022).
- Y. Ge, J. Tura, and J. I. Cirac, Faster ground state preparation and high-precision ground energy estimation with fewer qubits, Journal of Mathematical Physics 60 (2019).
- I. Stetcu, A. Baroni, and J. Carlson, Projection algorithm for state preparation on quantum computers, Physical Review C 108, L031306 (2023).
- C. Granade and N. Wiebe, Using random walks for iterative phase estimation, arXiv preprint arXiv:2208.04526 (2022).
- K. Landsman, M. Keesan, and C. Monroe, Toward convergence of effective-field-theory simulations on digital quantum computers, Physical Review A 100, 062319 (2019).
- N. Saurabh, S. Jha, and A. Luckow, A conceptual architecture for a quantum-hpc middleware, in 2023 IEEE International Conference on Quantum Software (QSW) (IEEE, 2023) pp. 116–127.
- D. Litinski, A game of surface codes: Large-scale quantum computing with lattice surgery, Quantum 3, 128 (2019).
- Ibm quantum documentation: shares and administration., https://docs.quantum-computing.ibm.com/run/fair-share-queue#shares-and-administration (a), accessed: 2023-11-20.
- A. B. Yoo, M. A. Jette, and M. Grondona, Slurm: Simple linux utility for resource management, in Workshop on job scheduling strategies for parallel processing (Springer, 2003) pp. 44–60.
- Torque, https://hpc-wiki.info/hpc/Torque, accessed: 2023-11-20.
- Altair grid engine: Distributed resource management and optimization, https://altair.com/grid-engine, accessed: 2023-11-20.
- Ibm spectrum lsf suites, https://www.ibm.com/products/hpc-workload-management, accessed: 2023-11-20.
- Ibm quantum documentation: fair-share queue., https://docs.quantum-computing.ibm.com/run/fair-share-queue (b), accessed: 2023-11-20.
- T. Bicer, D. Chiu, and G. Agrawal, A framework for data-intensive computing with cloud bursting, in 2011 IEEE International Conference on Cluster Computing (2011) pp. 169–177.
- NVIDIA, NVIDIA CUDA Quantum: The platform for hybrid quantum-classical computing, https://developer.nvidia.com/cuda-quantum (2023).
- A. Cross, The ibm q experience and qiskit open-source quantum computing software, in APS March meeting abstracts, Vol. 2018 (2018) pp. L58–003.
- PSNC, PSNC QCG HPC & Quantum middleware platform, https://qcg.psnc.pl (2023).
- Zapata Computing, Orquestra: A platform for hybrid quantum-classical computing, Zapata Computing, https://www.zapatacomputing.com/orquestra-platform/ (2023).
- Covalent, Covalent: Open source workflow orchestration for heterogenous computing, Covalent, https://www.covalent.xyz/ (2023).
- European Parliament and Council of the European Union, Regulation (EU) 2016/679 of the European Parliament and of the Council.
- Centers for Medicare & Medicaid Services, The Health Insurance Portability and Accountability Act of 1996 (HIPAA), Online at http://www.cms.hhs.gov/hipaa/ (1996).
- Y. A. Danylo Lykov, Importance of diagonal gates in tensor network simulations, arXiv https://doi.org/10.48550/arXiv.2106.15740 (2021).
- Laying the Groundwork for Quantum Powered Use Cases, https://www.youtube.com/watch?v=Hd-IGvuARfE&ab_channel=IBMResearch (2023), [Video].
- D. Gottesman, The heisenberg representation of quantum computers, arXiv preprint quant-ph/9807006 (1998).
- S. Aaronson and D. Gottesman, Improved simulation of stabilizer circuits, Physical Review A 70, 052328 (2004).
- H. J. Garcia and I. L. Markov, Simulation of quantum circuits via stabilizer frames, IEEE Transactions on Computers 64, 2323 (2014).
- D. Aharonov, Z. Landau, and J. Makowsky, The quantum fft can be classically simulated, arXiv preprint quant-ph/0611156 (2006).
- N. Yoran and A. J. Short, Efficient classical simulation of the approximate quantum fourier transform, Physical Review A 76, 042321 (2007).
- S. Hillmich, I. L. Markov, and R. Wille, Just like the real thing: Fast weak simulation of quantum computation, in 2020 57th ACM/IEEE Design Automation Conference (DAC) (IEEE, 2020) pp. 1–6.
- A. Fatima and I. L. Markov, Faster schrödinger-style simulation of quantum circuits, in 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA) (IEEE, 2021) pp. 194–207.
- I. L. Markov and Y. Shi, Simulating quantum computation by contracting tensor networks, SIAM Journal on Computing 38, 963 (2008).
- D. Lykov and Y. Alexeev, Importance of diagonal gates in tensor network simulations (2021), arXiv:2106.15740 [quant-ph] .
- A. Hey, Feynman and computation (CRC Press, 2018).
- M. Shishkin and G. Kresse, Self-consistent gw𝑔𝑤gwitalic_g italic_w calculations for semiconductors and insulators, Phys. Rev. B 75, 235102 (2007).
- J. P. Perdew, Density functional theory and the band gap problem, International Journal of Quantum Chemistry 28, 497 (1985).
- A. J. Cohen, P. Mori-Sánchez, and W. Yang, Challenges for density functional theory, Chemical reviews 112, 289 (2012).
- M. Sparta and F. Neese, Chemical Applications Carried Out by Local Pair Natural Orbital Based Coupled-Cluster Methods, Chem. Soc. Rev. 43, 5032 (2014).
- K. A. Moltved and K. P. Kepp, Performance of density functional theory for transition metal oxygen bonds, ChemPhysChem 20, 3210 (2019).
- https://github.com/zhendongli2008 (2020).
- Z. Li and G. K.-L. Chan, Spin-projected matrix product states: Versatile tool for strongly correlated systems, J. Chem. Theory Comput. 13, 2681 (2017).
- C. W. Cady, R. H. Crabtree, and G. W. Brudvig, Functional Models for the Oxygen-Evolving Complex of Photosystem II, Coord. Chem. Rev. 252, 444 (2008).
- Y. Kurashige, G. K.-L. Chan, and T. Yanai, Entangled Quantum Electronic Wavefunctions of the Mn4CaO5𝑀subscript𝑛4𝐶𝑎subscript𝑂5Mn_{4}CaO_{5}italic_M italic_n start_POSTSUBSCRIPT 4 end_POSTSUBSCRIPT italic_C italic_a italic_O start_POSTSUBSCRIPT 5 end_POSTSUBSCRIPT Cluster in Photosystem II, Nat. Chem. 5, 660 (2013).
- P. E. Siegbahn, Structures and Energetics for O22{}_{2}start_FLOATSUBSCRIPT 2 end_FLOATSUBSCRIPT Formation in Photosystem II, Acc. Chem. Res. 42, 1871 (2009).
- J. Batool, T. Hahn, and M. R. Pederson, Magnetic Signatures of Hydroxyl-and Water-Terminated Neutral and Tetra-Anionic Mn12-Acetate, J. Comput. Chem. 40, 2301 (2019).
- V. G. Chilkuri, S. DeBeer, and F. Neese, Ligand Field Theory and Angular Overlap Model Based Analysis of the Electronic Structure of Homovalent Iron–Sulfur Dimers, Inorg. Chem. 59, 984 (2020).
- L. Cao, O. Caldararu, and U. Ryde, Protonation and Reduction of the FeMo Cluster in Nitrogenase Studied by Quantum Mechanics/Molecular Mechanics (QM/MM) Calculations, J. Chem. Theory Comput. 14, 6653 (2018).
- M. Capdevila-Cortada, Z. Łodziana, and N. López, Performance of DFT+U Approaches in the Study of Catalytic Materials, ACS Catalysis 6, 8370 (2016).
- A. M. Wodtke, Electronically Non-Adiabatic Influences in Surface Chemistry and Dynamics, Chem. Soc. Rev. 45, 3641 (2016).
- J. VandeVondele, U. Borstnik, and J. Hutter, Linear Scaling Self-Consistent Field Calculations with Millions of Atoms in the Condensed Phase, J. Chem. Theory Comput. 8, 3565 (2012).
- S.-S. Lee, Recent Developments in Non-Fermi Liquid Theory, Annu. Rev. Condens. Matter Phys. 9, 227 (2018).
- E. Fradkin, S. A. Kivelson, and J. M. Tranquada, Colloquium: Theory of Intertwined Orders in High-Temperature Superconductors, Rev. Mod. Phys. 87, 457 (2015).
- L. Balents, Spin Liquids in Frustrated Magnets, Nature 464, 199 (2010).
- G. Knizia and G. K.-L. Chan, Density matrix embedding: A simple alternative to dynamical mean-field theory, Physical Review Letters 109, 186404 (2012b).
- G. Knizia and G. K.-L. Chan, Density matrix embedding: A strong-coupling quantum embedding theory, Journal of Chemical Theory and Computation 9, 1428 (2013b).
- H. Q. Pham, V. Bernales, and L. Gagliardi, Can density matrix embedding theory with the complete activate space self-consistent field solver describe single and double bond breaking in molecular systems?, Journal of Chemical Theory and Computation 14, 1960 (2018).
- M. R. Hermes and L. Gagliardi, Multiconfigurational self-consistent field theory with density matrix embedding: The localized active space self-consistent field method, Journal of Chemical Theory and Computation 15, 972 (2019b).
- H. Q. Pham, M. R. Hermes, and L. Gagliardi, Periodic electronic structure calculations with the density matrix embedding theory, Journal of Chemical Theory and Computation 16, 130 (2020).
- Z.-H. Cui, T. Zhu, and G. K.-L. Chan, Efficient implementation of ab initio quantum embedding in periodic systems: Density matrix embedding theory, Journal of Chemical Theory and Computation 16, 119 (2020b).
- B. T. G. Lau, G. Knizia, and T. C. Berkelbach, Regional embedding enables high-level quantum chemistry for surface science, The Journal of Physical Chemistry Letters 12, 1104 (2021).
- A. Mitra, M. R. Hermes, and L. Gagliardi, Density matrix embedding using multiconfiguration pair-density functional theory, Journal of Chemical Theory and Computation 19, 3498 (2023b).
- A. I. Lichtenstein and M. I. Katsnelson, Ab initio calculations of quasiparticle band structure in correlated systems: LDA++ approach, Physical Review B 57, 6884 (1998).
- P. Sun and G. Kotliar, Extended dynamical mean-field theory and GW method, Physical Review B 66, 085120 (2002).
- S. Biermann, F. Aryasetiawan, and A. Georges, First-principles approach to the electronic structure of strongly correlated systems: Combining the GW approximation and dynamical mean-field theory, Physical Review Letters 90, 086402 (2003).
- S. Biermann, Dynamical screening effects in correlated electron materials—a progress report on combined many-body perturbation and dynamical mean field theory: GW+DMFT, Journal of Physics: Condensed Matter 26, 173202 (2014).
- T. N. Lan and D. Zgid, Generalized self-energy embedding theory, The Journal of Physical Chemistry Letters 8, 2200 (2017).
- D. Zgid and E. Gull, Finite temperature quantum embedding theories for correlated systems, New Journal of Physics 19, 023047 (2017).
- H. Ma, M. Govoni, and G. Galli, Quantum simulations of materials on near-term quantum computers, npj Computational Materials 6, 85 (2020c).
- B. Huang, M. Govoni, and G. Galli, Simulating the electronic structure of spin defects on quantum computers, PRX Quantum 3, 010339 (2022).
- H. O. Jeschke, F. Salvat-Pujol, and R. Valentí, First-principles determination of heisenberg hamiltonian parameters for the spin-1 2 kagome antiferromagnet zncu 3 (oh) 6 cl 2, Physical Review B 88, 075106 (2013).
- K. Haule, Exact double counting in combining the dynamical mean field theory and the density functional theory, Physical review letters 115, 196403 (2015).
- S. Ten-no, Stochastic determination of effective hamiltonian for the full configuration interaction solution of quasi-degenerate electronic states, The Journal of chemical physics 138 (2013).
- S. Zhou and D. Ceperley, Construction of localized wave functions for a disordered optical lattice and analysis of the resulting hubbard model parameters, Physical Review A 81, 013402 (2010).
- S. R. White, Numerical canonical transformation approach to quantum many-body problems, The Journal of chemical physics 117, 7472 (2002).
- T. Yanai and G. K. Chan, Canonical transformation theory for multireference problems, The Journal of chemical physics 124 (2006).
- L. Savary and L. Balents, Quantum spin liquids: a review, Reports on Progress in Physics 80, 016502 (2016).
- Y. Zhou, K. Kanoda, and T.-K. Ng, Quantum spin liquid states, Reviews of Modern Physics 89, 025003 (2017).
- J. R. Chamorro, T. M. McQueen, and T. T. Tran, Chemistry of quantum spin liquids, Chemical Reviews 121, 2898 (2020).
- A. Kitaev, Anyons in an exactly solved model and beyond, Annals of Physics 321, 2 (2006).
- G. Jackeli and G. Khaliullin, Mott insulators in the strong spin-orbit coupling limit: from heisenberg to a quantum compass and kitaev models, Physical review letters 102, 017205 (2009).
- P. Maksimov and A. Chernyshev, Rethinking α𝛼\alphaitalic_α- rucl 3, Physical Review Research 2, 033011 (2020).
- P. Laurell and S. Okamoto, Dynamical and thermal magnetic properties of the kitaev spin liquid candidate α𝛼\alphaitalic_α-rucl3, npj quantum materials 5, 2 (2020).
- M. Imada, A. Fujimori, and Y. Tokura, Metal-insulator transitions, Reviews of modern physics 70, 1039 (1998).
- N. A. Spaldin, Magnetic materials: fundamentals and applications (Cambridge university press, 2010).
- J. Orenstein and A. Millis, Advances in the physics of high-temperature superconductivity, Science 288, 468 (2000).
- E. Dagotto, Correlated electrons in high-temperature superconductors, Reviews of Modern Physics 66, 763 (1994).
- J. Hubbard, Electron correlations in narrow energy bands. ii. the degenerate band case, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences 277, 237 (1964).
- J. Kanamori, Electron correlation and ferromagnetism of transition metals, Progress of Theoretical Physics 30, 275 (1963).
- M. C. Gutzwiller, Effect of correlation on the ferromagnetism of transition metals, Physical Review Letters 10, 159 (1963).
- E. H. Lieb and F.-Y. Wu, Absence of mott transition in an exact solution of the short-range, one-band model in one dimension, Physical Review Letters 20, 1445 (1968).
- W. Metzner and D. Vollhardt, Correlated lattice fermions in d=∞𝑑d=\inftyitalic_d = ∞ dimensions, Physical review letters 62, 324 (1989).
- J. Oitmaa, C. Hamer, and W. Zheng, Series expansion methods for strongly interacting lattice models (Cambridge University Press, 2006).
- V. Emery, Theory of high-t c superconductivity in oxides, Physical Review Letters 58, 2794 (1987).
- M. Greiter and R. Thomale, No evidence for spontaneous orbital currents in numerical studies of three-band models for the cuo planes of high-temperature superconductors, Physical review letters 99, 027005 (2007).
- M. S. Hybertsen, M. Schlüter, and N. E. Christensen, Calculation of coulomb-interaction parameters for la22{}_{2}start_FLOATSUBSCRIPT 2 end_FLOATSUBSCRIPTcuo44{}_{4}start_FLOATSUBSCRIPT 4 end_FLOATSUBSCRIPT using a constrained-density-functional approach, Physical Review B 39, 9028 (1989).
- R. L. Martin, Electronic localization in the cuprates, Physical Review B 53, 15501 (1996).
- A. Damascelli, Probing the Electronic Structure of Complex Systems by ARPES, Physica Scripta T109, 61 (2004).
- B. D. Patterson et al., Coherent Science at the SwissFEL X-Ray Laser, New J. Phys. 12, 035012 (2010).
- S. P. Weathersby et al., Mega-Electron-Volt Ultrafast Electron Diffraction at SLAC National Accelerator Laboratory, Rev. Sci. Instrum 86, 073702 (2015).
- N. P. D. Sawaya, F. Paesani, and D. P. Tabor, Near- and long-term quantum algorithmic approaches for vibrational spectroscopy, Phys. Rev. A 104, 062419 (2021).
- B. Demmig-Adams and W. Adams, Photosynthesis and partitioning — photoinhibition, in Encyclopedia of Applied Plant Sciences, edited by B. Thomas (Elsevier, Oxford, 2003) pp. 707–714.
- T. Ritz, A. Damjanović, and K. Schulten, The quantum physics of photosynthesis, ChemPhysChem 3, 243 (2002).
- N. Cox, D. A. Pantazis, and W. Lubitz, Current understanding of the mechanism of water oxidation in photosystem ii and its relation to xfel data, Annual Review of Biochemistry 89, 795 (2020), pMID: 32208765, https://doi.org/10.1146/annurev-biochem-011520-104801 .
- N. Science and T. C. (US), Materials genome initiative for global competitiveness (Executive Office of the President, National Science and Technology Council, 2011).
- D. R. Smith, J. B. Pendry, and M. C. K. Wiltshire, Metamaterials and negative refractive index, Science 305, 788 (2004).
- S. A. Cummer, J. Christensen, and A. Alu``u\grave{\rm{u}}over` start_ARG roman_u end_ARG, Controlling sound with acoustic metamaterials, Nature Reviews Materials 1, 16001 (2016).
- S. Rendle, Factorization machines, in 2010 IEEE International Conference on Data Mining (2010) pp. 995–1000.