Abstract
Among the many computational challenges faced across different disciplines, quantum-mechanical systems pose some of the hardest ones and offer a natural playground for the growing field of quantum technologies. In this Perspective, we discuss quantum algorithmic solutions for quantum dynamics, reporting on the latest developments and offering a viewpoint on their potential and current limitations. We present some of the most promising areas of application and identify possible research directions for the coming years.
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References
Feynman, R. P. Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982).
Lloyd, S. Universal quantum simulators. Science 273, 1073–1078 (1996).
Wiesner, S. Simulations of many-body quantum systems by a quantum computer. Preprint at https://arxiv.org/abs/quant-ph/9603028 (1996).
Zalka, C. Efficient simulation of quantum systems by quantum computers. Fortschr. Phys. Prog. Phys. 46, 877–879 (1998).
Tacchino, F., Chiesa, A., Carretta, S. & Gerace, D. Quantum computers as universal quantum simulators: state-of-the-art and perspectives. Adv. Quantum Technol. 3, 1900052 (2020).
Alexeev, Y. et al. Quantum computer systems for scientific discovery. PRX Quantum 2, 017001 (2021).
Altman, E. et al. Quantum simulators: architectures and opportunities. PRX Quantum 2, 017003 (2021).
Motta, M. & Rice, J. Emerging quantum computing algorithms for quantum chemistry. WIREs Comput. Mol. Sci. 12, e1580 (2021).
Cerezo, M. et al. Variational quantum algorithms. Nat. Rev. Phys. 3, 625–644 (2020).
Childs, A. M., Maslov, D., Nam, Y., Ross, N. J. & Su, Y. Toward the first quantum simulation with quantum speedup. Proc. Natl Acad. Sci. USA 115, 9456–9461 (2018).
Schuch, N. & Verstraete, F. Computational complexity of interacting electrons and fundamental limitations of density functional theory. Nat. Phys. 5, 732–735 (2009).
O’Gorman, B., Irani, S., Whitfield, J. & Fefferman, B. Intractability of electronic structure in a fixed basis. PRX Quantum 3, 020322 (2022).
Nielsen, M. A., Bremner, M. J., Dodd, J. L., Childs, A. M. & Dawson, C. M. Universal simulation of Hamiltonian dynamics for quantum systems with finite-dimensional state spaces. Phys. Rev. A 66, 022317 (2002).
Aharonov, D. & Ta-Shma, A. Adiabatic quantum state generation and statistical zero knowledge. In Proc. Thirty-fifth Annual ACM Symposium on Theory of Computing 20–29 (ACM, 2003); https://doi.org/10.1145/780543.780546
Wiebe, N., Berry, D., Høyer, P. & Sanders, B. C. Higher order decompositions of ordered operator exponentials. J. Phys. A 43, 065203 (2010).
Childs, A. M., Su, Y., Tran, M. C., Wiebe, N. & Zhu, S. Theory of Trotter error with commutator scaling. Phys. Rev. X 11, 011020 (2021).
Babbush, R., McClean, J., Wecker, D., Aspuru-Guzik, A. & Wiebe, N. Chemical basis of Trotter-Suzuki errors in quantum chemistry simulation. Phys. Rev. A 91, 022311 (2015).
Raeisi, S., Wiebe, N. & Sanders, B. C. Quantum-circuit design for efficient simulations of many-body quantum dynamics. New J. Phys. 14, 103017 (2012).
Reiher, M., Wiebe, N., Svore, K. M., Wecker, D. & Troyer, M. Elucidating reaction mechanisms on quantum computers. Proc. Natl Acad. Sci. USA 114, 7555–7560 (2017).
Poulin, D., Qarry, A., Somma, R. & Verstraete, F. Quantum simulation of time-dependent Hamiltonians and the convenient illusion of Hilbert space. Phys. Rev. Lett. 106, 170501 (2011).
Wiebe, N., Berry, D. W., Høyer, P. & Sanders, B. C. Simulating quantum dynamics on a quantum computer. J. Phys. A 44, 445308 (2011).
Childs, A. M., Ostrander, A. & Su, Y. Faster quantum simulation by randomization. Quantum 3, 182 (2019).
Zhang, C. in Monte Carlo and Quasi-Monte Carlo Methods 2010 (eds Plaskota, L. & Woźniakowski, H.) 709–719 (Springer, 2012); https://doi.org/10.1007/978-3-642-27440-4_42
Campbell, E. Random compiler for fast Hamiltonian simulation. Phys. Rev. Lett. 123, 70503 (2019).
Chen, C.-F., Huang, H.-Y., Kueng, R. & Tropp, J. A. Concentration for random product formulas. PRX Quantum 2, 040305 (2021).
Berry, D. W., Childs, A. M., Su, Y., Wang, X. & Wiebe, N. Time-dependent Hamiltonian simulation with L1-norm scaling. Quantum 4, 254 (2020).
Chin, S. A. Multi-product splitting and runge-kutta-nyström integrators. Celest. Mech. Dyn. Astron. 106, 391–406 (2010).
Low, G. H., Kliuchnikov, V. & Wiebe, N. Well-conditioned multiproduct Hamiltonian simulation. Preprint at https://arxiv.org/abs/1907.11679 (2019).
Endo, S., Zhao, Q., Li, Y., Benjamin, S. & Yuan, X. Mitigating algorithmic errors in a Hamiltonian simulation. Phys. Rev. A 99, 012334 (2019).
Vazquez, A. C., Hiptmair, R. & Woerner, S. Enhancing the quantum linear systems algorithm using richardson extrapolation. ACM Trans. Quantum Comput. 3, 1–37 (2022).
Vazquez, A. C., Egger, D. J., Ochsner, D. & Woerner, S. Well-conditioned multi-product formulas for hardware-friendly Hamiltonian simulation. Preprint at https://arxiv.org/abs/2207.11268 (2022).
Berry, D. W., Childs, A. M., Cleve, R., Kothari, R. & Somma, R. D. Simulating Hamiltonian dynamics with a truncated Taylor series. Phys. Rev. Lett. 114, 090502 (2015).
Childs, A. M. & Wiebe, N. Hamiltonian simulation using linear combinations of unitary operations. Quantum Inf. Comput. 12, 901–924 (2012).
Berry, D. W., Childs, A. M., Cleve, R., Kothari, R. & Somma, R. D. Exponential improvement in precision for simulating sparse Hamiltonians. In Proc. Forty-sixth Annual ACM Symposium on Theory of Computing 283–292 (ACM, 2014).
Berry, D. W., Childs, A. M. & Kothari, R. Hamiltonian simulation with nearly optimal dependence on all parameters. In 2015 IEEE 56th Annual Symposium on Foundations of Computer Science 792–809 (IEEE, 2015); https://doi.org/10.1109/FOCS.2015.54
Kieferová, M., Scherer, A. & Berry, D. W. Simulating the dynamics of time-dependent Hamiltonians with a truncated dyson series. Phys. Rev. A 99, 042314 (2019).
Chen, Y.-H., Kalev, A. & Hen, I. Quantum algorithm for time-dependent Hamiltonian simulation by permutation expansion. PRX Quantum 2, 030342 (2021).
Low, G. H. & Wiebe, N. Hamiltonian simulation in the interaction picture. Preprint at https://arxiv.org/abs/1805.00675 (2019).
Childs, A. M. Universal computation by quantum walk. Phys. Rev. Lett. 102, 180501 (2009).
Childs, A. M. On the relationship between continuous- and discrete-time quantum walk. Commun. Math. Phys. 294, 581–603 (2009).
Berry, D. W. & Childs, A. M. Black-box Hamiltonian simulation and unitary implementation. Quantum Inf. Comput. 12, 29–62 (2012).
Low, G. H. & Chuang, I. L. Optimal Hamiltonian simulation by quantum signal processing. Phys. Rev. Lett. 118, 010501 (2017).
Wang, C. & Wossnig, L. A quantum algorithm for simulating non-sparse Hamiltonian. Quantum Information and Computation 20, 597–615 (2020).
Low, G. H. & Chuang, I. L. Hamiltonian simulation by qubitization. Quantum 3, 163 (2019).
Dong, Y., Meng, X., Whaley, K. B. & Lin, L. Efficient phase-factor evaluation in quantum signal processing. Phys. Rev. A 103, 042419 (2021).
Berry, D. W., Gidney, C., Motta, M., McClean, J. R. & Babbush, R. Qubitization of arbitrary basis quantum chemistry leveraging sparsity and low rank factorization. Quantum 3, 208 (2019).
Lee, J. et al. Even more efficient quantum computations of chemistry through tensor hypercontraction. PRX Quantum 2, 030305 (2021).
Elfving, V. E. et al. How will quantum computers provide an industrially relevant computational advantage in quantum chemistry? Preprint at http://arxiv.org/abs/2009.12472 (2020).
Gilyén, A., Su, Y., Low, G. H. & Wiebe, N. Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics. In Proc. Annual ACM Symposium on Theory of Computing 193–204 (ACM, 2019); https://doi.org/10.1145/3313276.3316366
Martyn, J. M., Rossi, Z. M., Tan, A. K. & Chuang, I. L. A grand unification of quantum algorithms. PRX Quantum 2, 040203 (2021).
Martyn, J. M., Liu, Y., Chin, Z. E. & Chuang, I. L. Efficient fully-coherent Hamiltonian simulation. Preprint at https://arxiv.org/abs/2110.11327 (2021).
Rajput, A., Roggero, A. & Wiebe, N. Hybridized methods for quantum simulation in the interaction picture. Quantum 6, 780 (2021).
Yuan, X., Endo, S., Zhao, Q., Li, Y. & Benjamin, S. C. Theory of variational quantum simulation. Quantum 3, 191 (2019).
Li, Y. & Benjamin, S. C. Efficient variational quantum simulator incorporating active error minimization. Phys. Rev. X 7, 021050 (2017).
Yao, Y.-X. et al. Adaptive variational quantum dynamics simulations. PRX Quantum 2, 030307 (2021).
Bharti, K. & Haug, T. Quantum assisted simulator. Phys. Rev. A 104, 042418 (2020).
Lau, J. W. Z., Bharti, K., Haug, T. & Kwek, L. C. Quantum assisted simulation of time dependent Hamiltonians. Preprint at http://arxiv.org/abs/2101.07677 (2021).
Miessen, A., Ollitrault, P. J. & Tavernelli, I. Quantum algorithms for quantum dynamics: a performance study on the spin-boson model. Phys. Rev. Res. 3, 043212 (2021).
Heya, K., Nakanishi, K. M., Mitarai, K. & Fujii, K. Subspace variational quantum simulator. Preprint at http://arxiv.org/abs/1904.08566 (2019).
Cîrstoiu, C. et al. Variational fast forwarding for quantum simulation beyond the coherence time. npj Quantum Inf. 6, 82 (2020).
Commeau, B. et al. Variational Hamiltonian diagonalization for dynamical quantum simulation. Preprint at http://arxiv.org/abs/2009.02559 (2020).
Barison, S., Vicentini, F. & Carleo, G. An efficient quantum algorithm for the time evolution of parameterized circuits. Quantum 5, 512 (2021).
Benedetti, M., Fiorentini, M. & Lubasch, M. Hardware-efficient variational quantum algorithms for time evolution. Phys. Rev. Res. 3, 033083 (2020).
Lau, J. W. Z., Haug, T., Kwek, L. C. & Bharti, K. NISQ algorithm for Hamiltonian simulation via truncated Taylor series. SciPost Phys. 12, 122 (2021).
Zhang, Z.-J., Sun, J., Yuan, X. & Yung, M.-H. Low-depth Hamiltonian simulation by adaptive product formula. Preprint at http://arxiv.org/abs/2011.05283 (2020).
Lin, S.-H., Dilip, R., Green, A. G., Smith, A. & Pollmann, F. Real- and imaginary-time evolution with compressed quantum circuits. PRX Quantum 2, 010342 (2021).
Barratt, F. et al. Parallel quantum simulation of large systems on small quantum computers. npj Quantum Inf. https://doi.org/10.1038/s41534-021-00420-3 (2021).
Zoufal, C., Sutter, D. & Woerner, S. Error bounds for variational quantum time evolution. Preprint at https://arxiv.org/abs/2108.00022 (2021).
Kliesch, M., Barthel, T., Gogolin, C., Kastoryano, M. & Eisert, J. Dissipative quantum Church–Turing theorem. Phys. Rev. Lett. 107, 120501 (2011).
Wang, H., Ashhab, S. & Nori, F. Quantum algorithm for simulating the dynamics of an open quantum system. Phys. Rev. A 83, 062317 (2011).
Han, J. et al. Experimental simulation of open quantum system dynamics via Trotterization. Phys. Rev. Lett. 127, 020504 (2021).
Cleve, R. & Wang, C. Efficient quantum algorithms for simulating Lindblad evolution. Preprint at https://arxiv.org/abs/1612.09512 (2019).
Schlimgen, A. W., Head-Marsden, K., Sager, L. M., Narang, P. & Mazziotti, D. A. Quantum simulation of open quantum systems using a unitary decomposition of operators. Phys. Rev. Lett. 127, 270503 (2021).
Endo, S., Sun, J., Li, Y., Benjamin, S. C. & Yuan, X. Variational quantum simulation of general processes. Phys. Rev. Lett. 125, 010501 (2020).
Kamakari, H., Sun, S.-N., Motta, M. & Minnich, A. J. Digital quantum simulation of open quantum systems using quantum imaginary time evolution. PRX Quantum 3, 010320 (2021).
Cattaneo, M., De Chiara, G., Maniscalco, S., Zambrini, R. & Giorgi, G. L. Collision models can efficiently simulate any multipartite Markovian quantum dynamics. Phys. Rev. Lett. 126, 130403 (2021).
Wang, D.-S., Berry, D. W., de Oliveira, M. C. & Sanders, B. C. Solovay–Kitaev decomposition strategy for single-qubit channels. Phys. Rev. Lett. 111, 130504 (2013).
Hu, Z., Xia, R. & Kais, S. A quantum algorithm for evolving open quantum dynamics on quantum computing devices. Sci. Rep. 10, 3301 (2020).
Head-Marsden, K., Krastanov, S., Mazziotti, D. A. & Narang, P. Capturing non-Markovian dynamics on near-term quantum computers. Phys. Rev. Res. 3, 013182 (2021).
Ramusat, N. & Savona, V. A quantum algorithm for the direct estimation of the steady state of open quantum systems. Quantum 5, 399 (2021).
Motta, M. et al. Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution. Nat. Phys. 16, 205–210 (2020).
Sun, S.-N. et al. Quantum computation of finite-temperature static and dynamical properties of spin systems using quantum imaginary time evolution. PRX Quantum 2, 010317 (2021).
Cirac, J. I., Pérez-García, D., Schuch, N. & Verstraete, F. Matrix product states and projected entangled pair states: concepts, symmetries, theorems. Rev. Mod. Phys. 93, 045003 (2021).
Glaser, N., Baiardi, A. & Reiher, M. in Vibrational Dynamics of Molecules (ed. Bowman, J. M.) Ch. 3, 80–144 (World Scientific, 2022).
Magnifico, G., Felser, T., Silvi, P. & Montangero, S. Lattice quantum electrodynamics in (3+1)-dimensions at finite density with tensor networks. Nat. Commun. 12, 3600 (2021).
Bañuls, M. C. & Cichy, K. Tensors cast their nets for quarks. Nat. Phys. 17, 762–763 (2021).
Werner, P., Oka, T. & Millis, A. J. Diagrammatic Monte Carlo simulation of nonequilibrium systems. Phys. Rev. B 79, 035320 (2009).
Cohen, G., Gull, E., Reichman, D. R. & Millis, A. J. Taming the dynamical sign problem in real-time evolution of quantum many-body problems. Phys. Rev. Lett. 115, 266802 (2015).
Troyer, M. & Wiese, U.-J. Computational complexity and fundamental limitations to fermionic quantum Monte Carlo simulations. Phys. Rev. Lett. 94, 170201 (2005).
Poggi, P. M., Lysne, N. K., Kuper, K. W., Deutsch, I. H. & Jessen, P. S. Quantifying the sensitivity to errors in analog quantum simulation. PRX Quantum 1, 020308 (2020).
Chiesa, A. et al. Quantum hardware simulating four-dimensional inelastic neutron scattering. Nat. Phys. 15, 455–459 (2019).
Neill, C. et al. Accurately computing electronic properties of a quantum ring. Nature 594, 508–512 (2021).
Kim, Y. et al. Scalable error mitigation for noisy quantum circuits produces competitive expectation values. Preprint at https://arxiv.org/abs/2108.09197 (2021).
Rizzi, M., Montangero, S. & Vidal, G. Simulation of time evolution with multiscale entanglement renormalization ansatz. Phys. Rev. A 77, 052328 (2008).
O’Rourke, M. J. & Chan, G. K.-L. Entanglement in the quantum phases of an unfrustrated Rydberg atom array. Preprint at https://arxiv.org/abs/2201.03189 (2022).
Sharir, O., Shashua, A. & Carleo, G. Neural tensor contractions and the expressive power of deep neural quantum states. Phys. Rev. B 106, 205136 (2022).
Murakami, Y., Golež, D., Eckstein, M. & Werner, P. Photoinduced enhancement of excitonic order. Phys. Rev. Lett. 119, 247601 (2017).
Frías-Pérez, M. & Bañuls, M. C. Light cone tensor network and time evolution. Phys. Rev. B 106, 115117 (2022).
Giudice, G. et al. Temporal entanglement, quasiparticles, and the role of interactions. Phys. Rev. Lett. 128, 220401 (2022).
Smith, A., Kim, M. S., Pollmann, F. & Knolle, J. Simulating quantum many-body dynamics on a current digital quantum computer. npj Quantum Inf. 5, 106 (2019).
Crippa, L. et al. Simulating static and dynamic properties of magnetic molecules with prototype quantum computers. Magnetochemistry 7, 117 (2021).
Berthusen, N. F., Trevisan, T. V., Iadecola, T. & Orth, P. P. Quantum dynamics simulations beyond the coherence time on NISQ hardware by variational trotter compression. Phys. Rev. Res. 4, 023097 (2022).
Arute, F. et al. Observation of separated dynamics of charge and spin in the Fermi–Hubbard model. Preprint at https://arxiv.org/abs/2010.07965 (2020).
Smith, J. et al. Many-body localization in a quantum simulator with programmable random disorder. Nat. Phys. 12, 907–911 (2016).
Roushan, P. et al. Spectroscopic signatures of localization with interacting photons in superconducting qubits. Science 358, 1175–1179 (2017).
Bernien, H. et al. Probing many-body dynamics on a 51-atom quantum simulator. Nature 551, 579–584 (2017).
Zhang, J. et al. Observation of a many-body dynamical phase transition with a 53-qubit quantum simulator. Nature 551, 601–604 (2017).
de Léséleuc, S. et al. Accurate mapping of multilevel Rydberg atoms on interacting spin-1/2 particles for the quantum simulation of ising models. Phys. Rev. Lett. 120, 113602 (2018).
Ebadi, S. et al. Quantum phases of matter on a 256-atom programmable quantum simulator. Nature 595, 227–232 (2021).
Scholl, P. et al. Quantum simulation of 2D antiferromagnets with hundreds of Rydberg atoms. Nature 595, 233–238 (2021).
Endo, S., Kurata, I. & Nakagawa, Y. O. Calculation of the Green’s function on near-term quantum computers. Phys. Rev. Res. 2, 033281 (2020).
Chen, H., Nusspickel, M., Tilly, J. & Booth, G. H. Variational quantum eigensolver for dynamic correlation functions. Phys. Rev. A 104, 032405 (2021).
Baker, T. E. Lanczos recursion on a quantum computer for the Green’s function and ground state. Phys. Rev. A 103, 032404 (2021).
Jamet, F. et al. Krylov variational quantum algorithm for first principles materials simulations. Preprint at https://arxiv.org/abs/2105.13298 (2021).
Rizzo, J. et al. One-particle Green’s functions from the quantum equation of motion algorithm. Phys. Rev. Res. 4, 043011 (2022).
Libbi, F., Rizzo, J., Tacchino, F., Marzari, N. & Tavernelli, I. Effective calculation of the Green’s function in the time domain on near-term quantum processors. Phys. Rev. Res. 4, 043038 (2022).
Gärttner, M. et al. Measuring out-of-time-order correlations and multiple quantum spectra in a trapped-ion quantum magnet. Nat. Phys. 13, 781–786 (2017).
González Alonso, J. R., Yunger Halpern, N. & Dressel, J. Out-of-time-ordered-correlator quasiprobabilities robustly witness scrambling. Phys. Rev. Lett. 122, 040404 (2019).
Joshi, M. K. et al. Quantum information scrambling in a trapped-ion quantum simulator with tunable range interactions. Phys. Rev. Lett. 124, 240505 (2020).
Braumüller, J. et al. Probing quantum information propagation with out-of-time-ordered correlators. Nat. Phys. 18, 172–178 (2022).
Pappalardi, S. et al. Scrambling and entanglement spreading in long-range spin chains. Phys. Rev. B 98, 134303 (2018).
Mi, X. et al. Information scrambling in quantum circuits. Science 374, 1479–1483 (2021).
Geller, M. R. et al. Quantum simulation of operator spreading in the chaotic Ising model. Phys. Rev. E 105, 035302 (2022).
Richter, J. & Pal, A. Simulating hydrodynamics on noisy intermediate-scale quantum devices with random circuits. Phys. Rev. Lett. 126, 230501 (2021).
Richerme, P. et al. Non-local propagation of correlations in quantum systems with long-range interactions. Nature 511, 198–201 (2014).
Turner, C. J., Michailidis, A. A., Abanin, D. A., Serbyn, M. & Papić, Z. Weak ergodicity breaking from quantum many-body scars. Nat. Phys. 14, 745–749 (2018).
Ho, W. W., Choi, S., Pichler, H. & Lukin, M. D. Periodic orbits, entanglement, and quantum many-body scars in constrained models: matrix product state approach. Phys. Rev. Lett. 122, 040603 (2019).
Desaules, J.-Y., Pietracaprina, F., Papić, Z., Goold, J. & Pappalardi, S. Extensive multipartite entanglement from SU(2) quantum many-body scars. Phys. Rev. Lett. 129, 020601 (2022).
Eisert, J., Friesdorf, M. & Gogolin, C. Quantum many-body systems out of equilibrium. Nat. Phys. 11, 124–130 (2015).
Nandkishore, R. & Huse, D. A. Many-body localization and thermalization in quantum statistical mechanics. Annu. Rev. Condens. Matter Phys. 6, 15–38 (2015).
Benenti, G., Casati, G., Montangero, S. & Shepelyansky, D. L. Efficient quantum computing of complex dynamics. Phys. Rev. Lett. 87, 227901 (2001).
Benenti, G., Casati, G., Montangero, S. & Shepelyansky, D. L. Dynamical localization simulated on a few-qubit quantum computer. Phys. Rev. A 67, 052312 (2003).
Pizzamiglio, A. et al. Dynamical localization simulated on actual quantum hardware. Entropy 23, 654 (2021).
Smith, A., Jobst, B., Green, A. G. & Pollmann, F. Crossing a topological phase transition with a quantum computer. Phys. Rev. Res. 4, L022020 (2022).
Mei, F. et al. Digital simulation of topological matter on programmable quantum processors. Phys. Rev. Lett. 125, 160503 (2020).
Malz, D. & Smith, A. Topological two-dimensional Floquet lattice on a single superconducting qubit. Phys. Rev. Lett. 126, 163602 (2021).
Rodriguez-Vega, M. et al. Real-time simulation of light-driven spin chains on quantum computers. Phys. Rev. Res. 4, 013196 (2022).
Harle, N., Shtanko, O. & Movassagh, R. Observing and braiding topological Majorana modes on programmable quantum simulators. Preprint at https://arxiv.org/abs/2203.15083 (2022).
Stenger, J. P. T., Bronn, N. T., Egger, D. J. & Pekker, D. Simulating the dynamics of braiding of Majorana zero modes using an IBM quantum computer. Phys. Rev. Res. 3, 033171 (2021).
Ippoliti, M., Kechedzhi, K., Moessner, R., Sondhi, S. L. & Khemani, V. Many-body physics in the NISQ era: quantum programming a discrete time crystal. PRX Quantum 2, 030346 (2021).
Mi, X. et al. Observation of time-crystalline eigenstate order on a quantum processor. Nature 601, 531–536 (2022).
Randall, J. et al. Many-body-localized discrete time crystal with a programmable spin-based quantum simulator. Science 374, 1474–1478 (2021).
Frey, P. & Rachel, S. Realization of a discrete time crystal on 57 qubits of a quantum computer. Sci. Adv. 8, eabm7652 (2022).
Temme, K., Bravyi, S. & Gambetta, J. M. Error mitigation for short-depth quantum circuits. Phys. Rev. Lett. 119, 180509 (2017).
Endo, S., Benjamin, S. C. & Li, Y. Practical quantum error mitigation for near-future applications. Phys. Rev. X 8, 031027 (2018).
LaRose, R. et al. Mitiq: a software package for error mitigation on noisy quantum computers. Quantum 6, 774 (2022).
Suchsland, P. et al. Algorithmic error mitigation scheme for current quantum processors. Quantum 5, 492 (2021).
Temme, K., van den Berg, E., Kandala, A. & Gambetta, J. Error mitigation is the path to quantum computing usefulness. IBM https://research.ibm.com/blog/gammabar-for-quantum-advantage (2022).
van den Berg, E., Minev, Z. K., Kandala, A. & Temme, K. Probabilistic error cancellation with sparse Pauli–Lindblad models on noisy quantum processors. Preprint at https://arxiv.org/abs/2201.09866 (2022).
Barreiro, J. T. et al. An open-system quantum simulator with trapped ions. Nature 470, 486–491 (2011).
García-Pérez, G., Rossi, M. A. C. & Maniscalco, S. IBM Q experience as a versatile experimental testbed for simulating open quantum systems. npj Quantum Inf. 6, 1 (2020).
Del, Re,L., Rost, B., Kemper, A. F. & Freericks, J. K. Driven-dissipative quantum mechanics on a lattice: simulating a fermionic reservoir on a quantum computer. Phys. Rev. B 102, 125112 (2020).
Rost, B. et al. Demonstrating robust simulation of driven-dissipative problems on near-term quantum computers. Preprint at https://arxiv.org/abs/2108.01183 (2021).
Córcoles, A. D. et al. Exploiting dynamic quantum circuits in a quantum algorithm with superconducting qubits. Phys. Rev. Lett. 127, 100501 (2021).
Potočnik, A. et al. Studying light-harvesting models with superconducting circuits. Nat. Commun. 9, 904 (2018).
Rost, B. et al. Simulation of thermal relaxation in spin chemistry systems on a quantum computer using inherent qubit decoherence. Preprint at https://arxiv.org/abs/2001.00794 (2020).
Weimer, H., Kshetrimayum, A. & Orús, R. Simulation methods for open quantum many-body systems. Rev. Mod. Phys. 93, 015008 (2021).
Kshetrimayum, A., Weimer, H. & Orús, R. A simple tensor network algorithm for two-dimensional steady states. Nat. Commun. 8, 1291 (2017).
Zhou, Y., Stoudenmire, E. M. & Waintal, X. What limits the simulation of quantum computers? Phys. Rev. X 10, 041038 (2020).
Barthel, T. & Kliesch, M. Quasilocality and efficient simulation of Markovian quantum dynamics. Phys. Rev. Lett. 108, 230504 (2012).
Helmrich, S., Arias, A. & Whitlock, S. Uncovering the nonequilibrium phase structure of an open quantum spin system. Phys. Rev. A 98, 022109 (2018).
O’Rourke, M. J. & Chan, G. K.-L. Simplified and improved approach to tensor network operators in two dimensions. Phys. Rev. B 101, 205142 (2020).
Fink, J. M., Dombi, A., Vukics, A., Wallraff, A. & Domokos, P. Observation of the photon-blockade breakdown phase transition. Phys. Rev. X 7, 011012 (2017).
Jin, J. et al. Cluster mean-field approach to the steady-state phase diagram of dissipative spin systems. Phys. Rev. X 6, 031011 (2016).
Vicentini, F., Minganti, F., Rota, R., Orso, G. & Ciuti, C. Critical slowing down in driven-dissipative Bose–Hubbard lattices. Phys. Rev. A 97, 013853 (2018).
Olmos, B., Lesanovsky, I. & Garrahan, J. P. Facilitated spin models of dissipative quantum glasses. Phys. Rev. Lett. 109, 020403 (2012).
Mascarenhas, E., Flayac, H. & Savona, V. Matrix-product-operator approach to the nonequilibrium steady state of driven-dissipative quantum arrays. Phys. Rev. A 92, 022116 (2015).
Finazzi, S., Le Boité, A., Storme, F., Baksic, A. & Ciuti, C. Corner-space renormalization method for driven-dissipative two-dimensional correlated systems. Phys. Rev. Lett. 115, 080604 (2015).
Hartmann, M. J. & Carleo, G. Neural-network approach to dissipative quantum many-body dynamics. Phys. Rev. Lett. 122, 250502 (2019).
Nagy, A. & Savona, V. Variational quantum Monte Carlo method with a neural-network ansatz for open quantum systems. Phys. Rev. Lett. 122, 250501 (2019).
Yoshioka, N., Nakagawa, Y. O., Mitarai, K. & Fujii, K. Variational quantum algorithm for nonequilibrium steady states. Phys. Rev. Res. 2, 043289 (2020).
García-Pérez, G., Chisholm, D. A., Rossi, M. A. C., Palma, G. M. & Maniscalco, S. Decoherence without entanglement and quantum Darwinism. Phys. Rev. Res. 2, 012061 (2020).
Solfanelli, A., Santini, A. & Campisi, M. Experimental verification of fluctuation relations with a quantum computer. PRX Quantum 2, 030353 (2021).
Melo, F. V. et al. Experimental implementation of a two-stroke quantum heat engine. Phys. Rev. A 106, 032410 (2022).
Iemini, F., Rossini, D., Fazio, R., Diehl, S. & Mazza, L. Dissipative topological superconductors in number-conserving systems. Phys. Rev. B 93, 115113 (2016).
Cattaneo, M., Rossi, M. A. C., García-Pérez, G., Zambrini, R. & Maniscalco, S. Quantum simulation of dissipative collective effects on noisy quantum computers. Preprint at https://arxiv.org/abs/2201.11597 (2022).
Somoza, A. D., Marty, O., Lim, J., Huelga, S. F. & Plenio, M. B. Dissipation-assisted matrix product factorization. Phys. Rev. Lett. 123, 100502 (2019).
Hu, Z., Head-Marsden, K., Mazziotti, D. A., Narang, P. & Kais, S. A general quantum algorithm for open quantum dynamics demonstrated with the Fenna–Matthews–Olson complex. Quantum 6, 726 (2022).
Tazhigulov, R. N. et al. Simulating challenging correlated molecules and materials on the Sycamore quantum processor. Preprint at https://arxiv.org/abs/2203.15291 (2022).
Ollitrault, P. J., Miessen, A. & Tavernelli, I. Molecular quantum dynamics: a quantum computing perspective. Acc. Chem. Res. 54, 4229–4238 (2021).
Dreuw, A. & Head-Gordon, M. Single-reference ab initio methods for the calculation of excited states of large molecules. Chem. Rev. 105, 4009–4037 (2005).
Lischka, H. et al. Multireference approaches for excited states of molecules. Chem. Rev. 118, 7293–7361 (2018).
Westermayr, J. & Marquetand, P. Machine learning for electronically excited states of molecules. Chem. Rev. 121, 9873–9926 (2021).
Goings, J. J., Lestrange, P. J. & Li, X. Real-time time-dependent electronic structure theory. Wiley Interdiscip. Rev. Comput. Mol. Sci. 8, e1341 (2018).
Li, X., Govind, N., Isborn, C., DePrince, A. E. III & Lopata, K. Real-time time-dependent electronic structure theory. Chem. Rev. 120, 9951–9993 (2020).
Chan, G. K.-L. & Zgid, D. The density matrix renormalization group in quantum chemistry. Annu. Rep. Comput. Chem. 5, 149–162 (2009).
Baiardi, A. J. Chem. Theory Comput. 17, 3320–3334 (2021). Electron dynamics with the time-dependent density matrix renormalization group.
Wang, Z., Peyton, B. G. & Crawford, T. D. Accelerating real-time coupled cluster methods with single-precision arithmetic and adaptive numerical integration. Preprint at https://arxiv.org/abs/2205.05175 (2022).
Meyer, H. D., Gatti, F. & Worth, G. A. Multidimensional Quantum Dynamics: MCTDH Theory and Applications (Wiley, 2009); https://doi.org/10.1002/9783527627400
Baiardi, A. & Reiher, M. Large-scale quantum dynamics with matrix product states. J. Chem. Theory Comput. 15, 3481–3498 (2019).
Persico, M. & Granucci, G. An overview of nonadiabatic dynamics simulations methods, with focus on the direct approach versus the fitting of potential energy surfaces. Theor. Chem. Acc. 133, 1526 (2014).
Worth, G. A., Robb, M. A. & Lasorne, B. Solving the time-dependent Schrödinger equation for nuclear motion in one step: direct dynamics of non-adiabatic systems. Mol. Phys. 106, 2077–2091 (2008).
Ben-Nun, M., Quenneville, J. & Martínez, T. J. Ab initio multiple spawning: photochemistry from first principles quantum molecular dynamics. J. Phys. Chem. A 104, 5161–5175 (2000).
Lasorne, B., Robb, M. A. & Worth, G. A. Direct quantum dynamics using variational multi-configuration Gaussian wavepackets. Implementation details and test case. Phys. Chem. Chem. Phys. 9, 3210–3227 (2007).
Richings, G. W. & Habershon, S. MCTDH on-the-fly: efficient grid-based quantum dynamics without pre-computed potential energy surfaces. J. Chem. Phys. 148, 134116 (2018).
Abedi, A., Maitra, N. T. & Gross, E. K. U. Exact factorization of the time-dependent electron-nuclear wave function. Phys. Rev. Lett. 105, 123002 (2010).
Mátyus, E. & Reiher, M. Molecular structure calculations: a unified quantum mechanical description of electrons and nuclei using explicitly correlated Gaussian functions and the global vector representation. J. Chem. Phys. 137, 024104 (2012).
Bubin, S., Pavanello, M., Tung, W.-C., Sharkey, K. L. & Adamowicz, L. Born–Oppenheimer and non-Born–Oppenheimer, atomic and molecular calculations with explicitly correlated Gaussians. Chem. Rev. 113, 36–79 (2013).
Pavosevic, F., Culpitt, T. & Hammes-Schiffer, S. Multicomponent quantum chemistry: integrating electronic and nuclear quantum effects via the nuclear–electronic orbital method. Chem. Rev. 120, 4222–4253 (2020).
Yang, M. & White, S. R. Density-matrix-renormalization-group study of a one-dimensional diatomic molecule beyond the Born–Oppenheimer approximation. Phys. Rev. A 99, 022509 (2019).
Muolo, A., Baiardi, A., Feldmann, R. & Reiher, M. Nuclear–electronic all-particle density matrix renormalization group. J. Chem. Phys. 152, 204103 (2020).
Chiesa, A. et al. Digital quantum simulators in a scalable architecture of hybrid spin–photon qubits. Sci. Rep. 5, 16036 (2015).
Tacchino, F., Chiesa, A., LaHaye, M. D., Carretta, S. & Gerace, D. Electromechanical quantum simulators. Phys. Rev. B 97, 214302 (2018).
Macridin, A., Spentzouris, P., Amundson, J. & Harnik, R. Electron–phonon systems on a universal quantum computer. Phys. Rev. Lett. 121, 110504 (2018).
McArdle, S., Mayorov, A., Shan, X., Benjamin, S. & Yuan, X. Digital quantum simulation of molecular vibrations. Chem. Sci. 10, 5725–5735 (2019).
Sawaya, N. P. D. et al. Resource-efficient digital quantum simulation of d-level systems for photonic, vibrational, and spin-s Hamiltonians. npj Quantum Inf. 6, 49 (2020).
Ollitrault, P. J., Baiardi, A., Reiher, M. & Tavernelli, I. Hardware efficient quantum algorithms for vibrational structure calculations. Chem. Sci. 11, 6842–6855 (2020).
Tacchino, F., Chiesa, A., Sessoli, R., Tavernelli, I. & Carretta, S. A proposal for using molecular spin qudits as quantum simulators of light–matter interactions. J. Mater. Chem. C 9, 10266–10275 (2021).
Chan, H. H. S., Meister, R., Jones, T., Tew, D. P. & Benjamin, S. C. Grid-based methods for chemistry modelling on a quantum computer. Preprint at https://arxiv.org/abs/2202.05864 (2022).
Ollitrault, P. J., Mazzola, G. & Tavernelli, I. Nonadiabatic molecular quantum dynamics with quantum computers. Phys. Rev. Lett. 125, 260511 (2020).
Mitarai, K., Kitagawa, M. & Fujii, K. Quantum analog–digital conversion. Phys. Rev. A 99, 012301 (2019).
Woerner, S. & Egger, D. J. Quantum risk analysis. npj Quantum Inf. 5, 15 (2019).
Häner, T., Roetteler, M. & Svore, K. M. Optimizing quantum circuits for arithmetic. Preprint at https://arxiv.org/abs/1805.12445 (2018).
Ollitrault, P. J. et al. Quantum algorithms for grid-based variational time evolution. Preprint at http://arxiv.org/abs/2203.02521 (2022).
Martinez, E. A. et al. Real-time dynamics of lattice gauge theories with a few-qubit quantum computer. Nature 534, 516–519 (2016).
de Jong, W. A. et al. Quantum simulation of non-equilibrium dynamics and thermalization in the Schwinger model. Phys. Rev. D 106, 054508 (2022).
Lamm, H., Lawrence, S. & Yamauchi, Y. (NuQS Collaboration) General methods for digital quantum simulation of gaugetheories. Phys. Rev 100, 034518 (2019).
Kan, A. et al. Investigating a (3+1)D topological θ-term in the Hamiltonian formulation of lattice gauge theories for quantum and classical simulations. Phys. Rev. D 104, 34504 (2021).
Kan, A. & Nam, Y. Lattice quantum chromodynamics and electrodynamics on a universal quantum computer. Preprint at http://arxiv.org/abs/2107.12769 (2021).
González-Cuadra, D., Zache, T. V., Carrasco, J., Kraus, B. & Zoller, P. Hardware efficient quantum simulation of non-abelian gauge theories with qudits on Rydberg platforms. Preprint at https://arxiv.org/abs/2203.15541 (2022).
Mathis, S. V., Mazzola, G. & Tavernelli, I. Toward scalable simulations of lattice gauge theories on quantum computers. Phys. Rev. D 102, 094501 (2020).
Tanabashi, M. et al. Review of particle physics. Phys. Rev. D 98, 030001 (2018).
Bauer, C. W. et al. Quantum simulation for high energy physics. Preprint at https://arxiv.org/abs/2204.03381 (2022).
Wack, A. et al. Quality, speed, and scale: three key attributes to measure the performance of near-term quantum computers. Preprint at https://arxiv.org/abs/2110.14108 (2021).
Bauer, B., Wecker, D., Millis, A. J., Hastings, M. B. & Troyer, M. Hybrid quantum–classical approach to correlated materials. Phys. Rev. X 6, 031045 (2016).
Rossmannek, M., Barkoutsos, P. K., Ollitrault, P. J. & Tavernelli, I. Quantum HF/DFT-embedding algorithms for electronic structure calculations: scaling up to complex molecular systems. J. Chem. Phys. 154, 114105 (2021).
Layden, D. et al. Quantum-enhanced Markov chain Monte Carlo. Preprint at https://arxiv.org/abs/2203.12497 (2022).
Flannigan, S. et al. Propagation of errors and quantitative quantum simulation with quantum advantage. Preprint at https://arxiv.org/abs/2204.13644 (2022).
Daley, A. J. et al. Practical quantum advantage in quantum simulation. Nature 607, 667–676 (2022).
Childs, A. Quantum Information Processing in Continuous Time. PhD thesis, Massachusetts Institute of Technology (2000); https://dspace.mit.edu/handle/1721.1/16663
Acknowledgements
We thank M. Motta for insightful discussions and constructive comments on the final manuscript. Furthermore, we thank T. Angelides, I. Burghardt, A. C. Vázquez, S. Carretta, A. Chiesa, D. Gerace, S. Kühn, S. Maniscalco, R. Martinazzo, N. Marzari, V. Savona and C. Zoufal for insightful and useful discussions. We acknowledge financial support from the Swiss National Science Foundation (SNF) through grant number 200021-179312. IBM, the IBM logo and ibm.com are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. The current list of IBM trademarks is available at https://www.ibm.com/legal/copytrade.
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Miessen, A., Ollitrault, P.J., Tacchino, F. et al. Quantum algorithms for quantum dynamics. Nat Comput Sci 3, 25–37 (2023). https://doi.org/10.1038/s43588-022-00374-2
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DOI: https://doi.org/10.1038/s43588-022-00374-2
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