- Open Access
Lie-algebra-assisted quantum simulation and quantum optimal control via high-order Magnus expansions
Phys. Rev. A 114, 032415 – Published 8 September, 2026
DOI: https://doi.org/10.1103/jmrn-t4jw
Abstract
The evolution of a quantum system under time-dependent driving exhibits phenomena that are absent in its stationary counterpart. However, the high dimensionality and noncommutative nature of quantum dynamics make this a challenging problem. The Magnus expansion provides an analytic framework to approximate the effective dynamics on short timescales, but computing high-order terms with existing methods is computationally expensive. We introduce a scalable approach for Hamiltonians consisting of a time-independent drift term and a controllable term, in which the high-order Magnus expansion is reduced to a polynomial expression in the duration and the parameters of the time-dependent control function. For a fixed Hamiltonian model, the demanding algebraic calculation is performed once and stored in dynamical coefficients, after which the effective Hamiltonian can be evaluated numerically several orders of magnitude faster than previous techniques. We first demonstrate the method as a proof of principle for quantum simulation, verifying the expected high-order error scaling and benchmarking the numerical efficiency of the polynomial representation. We then apply the same analytically differentiable framework to a state-of-the-art quantum optimal-control problem, designing continuous control pulses for multiqubit phase gates on a neutral-atom Rydberg platform. Our results show that this method provides a practical route toward optimal control of challenging driven quantum systems.
Physics Subject Headings (PhySH)
Article Text
References (79)
- A. Miessen, P. J. Ollitrault, and I. Tavernelli, Quantum algorithms for quantum dynamics: A performance study on the spin-boson model, Phys. Rev. Res. 3, 043212 (2021).
- X. Qiang, T. Loke, A. Montanaro, K. Aungskunsiri, X. Zhou, J. L. O'Brien, J. B. Wang, and J. C. F. Matthews, Efficient quantum walk on a quantum processor, Nat. Commun. 7, 11511 (2016).
- V. Montenegro, C. Mukhopadhyay, R. Yousefjani, S. Sarkar, U. Mishra, M. G. A. Paris, and A. Bayat, Review: Quantum metrology and sensing with many-body systems, Phys. Rep. 1134, 1 (2025).
- N. Aslam et al., Quantum sensors for biomedical applications, Nat. Rev. Phys. 5157 (2023).
- Z. Zou, J. Gong, and W. Chen, Quantum dynamics can push sensing performance to the limit, Phys. Rev. Lett. (2025).
- R. Ramya, P. Kumar, D. Dhanasekaran, R. S. Kumar, and S. A. Sharavan, A review of quantum communication and information networks with advanced cryptographic applications using machine learning, deep learning techniques, Frankl. Open 10, 100223 (2025).
- C. Bauer et al., Quantum simulation of fundamental particles and forces, Nat. Rev. Phys. 5, 420 (2023).
- R. van der Meer et al., Experimental simulation of loop quantum gravity on a photonic chip, Nat. Quantum Inf. 9, 32 (2022).
- D. Gonzalez-Cuadra, M. Hamdan, T. V. Zache, B. Braverman, M. Kornjaca, A. Lukin, S. H. Cantu, F. Liu, S.-T. Wang, A. Keesling, M. D. Lukin, P. Zoller, and A. Bylinskii, Observation of string breaking on a (2 + 1)D Rydberg quantum simulator, Nature (London) 642, 321 (2024).
- A. N. Ciavarella and C. W. Bauer, Quantum simulation of SU(3) lattice Yang-Mills theory at leading order in large- expansion, Phys. Rev. Lett. 133, 111901 (2024).
- Q. Ansel, E. Dionis, F. Arrouas, B. Peaudecerf, S. Guérin, D. Guéry-Odelin, and D. Sugny, Introduction to theoretical and experimental aspects of quantum optimal control, J. Phys. B: At. Mol. Opt. Phys. 57, 133001 (2024).
- T. S. Mahesh, P. Batra, and M. H. Ram, Quantum optimal control: Practical aspects and diverse methods, arXiv:2205.15574.
- Q.-M. Chen, H. Rabitz, and R.-B. Wu, Quantum optimal control without arbitrary waveform generators, Phys. Rev. Appl. 20, 064016 (2023).
- J. F. Kam, H. Kang, C. D. Hill, G. J. Mooney, and L. C. L. Hollenberg, Characterization of entanglement on superconducting quantum computers of up to 414 qubits, Phys. Rev. Res. 6, 033155 (2024).
- 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).
- N. Maskara et al., Programmable simulations of molecules and materials with reconfigurable quantum processors, Nat. Phys. 21 289 (2023).
- K. Wang et al., Demonstration of low-overhead quantum error correction codes, Nat. Phys. 22 308 (2025).
- R. Acharya et al., Quantum error correction below the surface code threshold, Nature (London) 638, 920 (2024).
- C. Poole, T. M. Graham, M. A. Perlin, M. Otten, and M. Saffman, Architecture for fast implementation of quantum low-density parity-check codes with optimized Rydberg gates, Phys. Rev. A 111, 022433 (2025).
- D. Bluvstein et al., Logical quantum processor based on reconfigurable atom arrays, Nature (London) 626, 58 (2024).
- J. Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018).
- S. Chen, J. Cotler, H.-Y. Huang, and J. Li, The complexity of NISQ, Nat. Commun. 14, 6001 (2023).
- M. Saffman, Quantum computing with atomic qubits and Rydberg interactions: Progress and challenges, J. Phys. B: At. Mol. Opt. Phys. 49, 202001 (2016).
- L. Henriet, L. Beguin, A. Signoles, T. Lahaye, A. Browaeys, G.-O. Reymond, and C. Jurczak, Quantum computing with neutral atoms, Quantum 4, 327 (2020).
- M. Morgado and S. Whitlock, Quantum simulation and computing with Rydberg-interacting qubits, AVS Quantum Sci. 3, 023501 (2021).
- P. Scholl, M. Schuler, H. J. Williams, A. A. Eberharter, D. Barredo, K.-N. Schymik, V. Lienhard, L.-P. Henry, T. C. Lang, T. Lahaye, A. M. Läuchli, and A. Browaeys, Quantum simulation of 2D antiferromagnets with hundreds of Rydberg atoms, Nature (London) 595, 233 (2021).
- S. Euchner and I. Lesanovsky, Rydberg atom arrays as quantum simulators for molecular dynamics, Phys. Rev. Res. 7, L042009 (2025).
- C. Evered, E. Lawrence, H. R. Bapat, A. Deller, J. Laurer, A. Paz, J. Zeiher, M. A. Norcia, and J. Bernien, High-fidelity parallel entangling gates on a neutral-atom quantum computer, Nature (London) 622, 268 (2023).
- G. Dirr and U. Helmke, Lie theory for quantum control, GAMM Mitteilungen, 31, 59 (2008).
- M. L. Goh, M. Larocca, L. Cincio, M. Cerezo, and F. Sauvage, Lie-algebraic classical simulations for quantum computing, Phys. Rev. Res. 7, 033266 (2025).
- J. Wei and E. Norman, Lie algebraic solution of linear differential equations, J. Math. Phys. 4, 575 (1963).
- B. C. Hall, Lie Groups, Lie Algebras, and Representations, Graduate Texts in Mathematics Vol. 222 (Springer International Publishing, Cham, 2015).
- R. Wiersema, E. Kökcü, A. F. Kemper, and B. N. Bakalov, Classification of dynamical Lie algebras of 2-local spin systems on linear, circular and fully connected topologies, npj Quantum Inf. 10, 110 (2024).
- S. Qvarfort and I. Pikovski, Solving quantum dynamics with a Lie-algebra decoupling method, PRX Quantum 6, 010201 (2025).
- M. Ragone, B. N. Bakalov, F. Sauvage, A. F. Kemper, C. O. Marrero, M. Larocca, and M. Cerezo, A Lie algebraic theory of barren plateaus for deep parameterized quantum circuits, Nat. Commun. 15, 7172 (2024).
- S. Blanes, F. Casas, J. A. Oteo, and J. Ros, The Magnus expansion and some of its applications, Phys. Rep. 470, 151 (2009).
- K. F. Milfeld and R. E. Wyatt, Study extension and application of Floquet theory for quantum molecular systems in an oscillating field, Phys. Rev. A 27, 72 (1983).
- N. Auer, L. Einkemmer, P. Kandolf, and A. Ostermann, Magnus integrators on multicore CPUs and GPUs, Comput. Phys. Commun. 228, 115 (2018).
- A. Chakraborty, T. L. Patti, B. Khailany, A. N. Jordan, and A. Anandkumar, GPU-accelerated effective Hamiltonian calculator, Quantum 9, 1946 (2025).
- C. J. Budd and S. P. Nørsett, On the solution of linear differential equations in Lie groups, Philos. Trans. R. Soc. London Ser. A 357, 983 (1999).
- A. Alvermann and H. Fehske, High-order commutator-free exponential time-propagation of driven quantum systems, J. Comput. Phys. 230, 5930 (2011).
- P. A. M. Casares, M. S. Zini, and J. M. Arrazola, Quantum simulation of time-dependent Hamiltonians via commutator-free quasi-Magnus operators, Quantum 8, 1567 (2024).
- A. Arnal, F. Casas, and C. Chiralt, An efficient procedure to compute the continuous Baker–Campbell–Hausdorff formula, Appl. Math. Comput. 507, 129563 (2025).
- E. B. Dynkin, Calculation of the coefficients in the Campbell-Hausdorff formula, Dokl. Akad. Nauk SSSR (N. S.) 57, 323 (1947).
- R. Achilles and A. Bonfiglioli, The early proofs of the theorem of Campbell, Baker, Hausdorff, and Dynkin, Arch. Hist. Exact Sci. 66, 295 (2012).
- V. Gritsev and A. Polkovnikov, Integrable Floquet dynamics, SciPost Phys. 2, 021 (2017).
- J. Wu, J.-L. Wu, F.-Q. Guo, B.-B. Liu, S.-L. Su, X.-K. Song, L. Ye, and D. Wang, Quantum computation via Floquet tailored Rydberg interactions, npj Quantum Inf. 11, 118 (2025).
- M. Rodriguez-Vega, M. Vogl, and G. A. Fiete, Low-frequency and Moiré–Floquet engineering: A review, Ann. Phys. 435, 168434 (2021).
- M. Kalinowski, N. Maskara, and M. D. Lukin, Non-Abelian Floquet spin liquids in a digital Rydberg simulator, Phys. Rev. X 13, 031008 (2023).
- C. Weitenberg and J. Simonet, Tailoring quantum gases by Floquet engineering, Nat. Phys. 17, 1342 (2021).
- F. Wilczek, Quantum time crystals, Phys. Rev. Lett. 109, 160401 (2012).
- H. Liu, H. Cao, and S. Meng, Floquet engineering of topological states in realistic quantum materials via light-matter interactions, Prog. Surf. Sci. 98, 100705 (2023).
- T. Eckstein, R. Mansuroglu, P. Czarnik, J.-X. Zhu, M. J. Hartmann, L. Cincio, A. T. Sornborger, and Z. Holmes, Large-scale simulations of Floquet physics on near-term quantum computers, npj Quantum Inf. 10, 84 (2024).
- D. I. Bondar, L. B. Gaggioli, G. Korpas, J. Marecek, J. Vala, and K. Jacobs, Globally optimal control of quantum dynamics, Phys. Rev. Res. 7, 043202 (2025).
- L. B. Gaggioli, D. I. Bondar, J. Vala, R. Ovsiannikov, and J. Mareček, Unitary gate synthesis via polynomial optimization, arXiv:2508.01356.
- N. Khaneja, T. Reiss, C. Kehlet, T. Schulte-Herbrüggen, and S. J. Glaser, Optimal control of coupled spin dynamics: Design of NMR pulse sequences by gradient ascent algorithms, J. Magn. Reson. 172, 296 (2005).
- In units where .
- Ordered exponential, Wikipedia (unpublished).
- J. Bernoulli, Ars Conjectandi (Thurneisen, Basel, 1713).
- P. C. Moan and J. Niesen, Convergence of the Magnus series, Found. Comput. Math. 8, 291 (2008).
- J. L. Allen, R. Kosut, J. Joo, P. Leek, and E. Ginossar, Optimal control of two qubits via a single cavity drive in circuit quantum electrodynamics, Phys. Rev. A 95, 042325 (2017).
- All of the time-sensitive benchmarking has been done using CPU multithreading, on a system with Intel(R) Xeon(R) Gold 6240.
- We use Verner's ninth-order method with 100 time steps, enough to keep the propagation error under floating-point accuracy.
- J. H. Verner, Numerically optimal Runge–Kutta pairs with interpolants, Numer. Algorithms 53, 383 (2010).
- C. Rackauckas and Q. Nie, Differentialequations.jl—A performant and feature-rich ecosystem for solving differential equations in Julia, J. Open Res. Software 5, 15 (2017).
- J. M. Dominy and D. Leermakers, et al., quantumpropagators.jl: A Julia package for quantum state propagation, GitHub, 2025, https://github.com/QuantumControl/QuantumPropagators.jl.
- E. S. Meckes, The Random Matrix Theory of the Classical Compact Groups (Cambridge University Press, Cambridge, UK, 2019).
- S. Jandura and G. Pupillo, Time-optimal two- and three-qubit gates for Rydberg atoms, Quantum, 6, 712 (2022).
- M. Mohan, R. de Keijzer, and S. Kokkelmans, Robust control and optimal Rydberg states for neutral atom two-qubit gates, Phys. Rev. Res. 5, 033052 (2023).
- C. Fromonteil, R. Tricarico, F. Cesa, and H. Pichler, Hamilton-Jacobi-Bellman equations for Rydberg-blockade processes, Phys. Rev. Res. 6, 033333 (2024).
- M. Mohan, J. de Hond, and S. Kokkelmans, Parametrized multiqubit gates for neutral-atom quantum platforms, Phys. Rev. Appl. 23, 054074 (2025).
- A. Delakouras, G. Doultsinos, and D. Petrosyan, Multi-qubit Rydberg gates between distant atoms, Quantum 10, 1990 (2025).
- R. de Keijzer, L. Visser, O. Tse, and S. Kokkelmans, Consensus-based qubit configuration optimization for variational algorithms on neutral atom quantum systems, npj Quantum Inf. 11, 186 (2025).
- J.-L. Lagrange, Méchanique Analytique (La Veuve Desaint, 1788).
- J. Fageot, S. Aziznejad, M. Unser, and V. Uhlmann, Support and approximation properties of Hermite splines, arXiv:1902.02565.
- The inclusion of the projector in the qutrit operator is, such that its eigenspectrum is the same as the qubit operator, keeping the property that .
- M. Mogensen and P. E. Farrell, optim.jl: A mathematical optimization package for Julia, [software] GitHub, 2020, https://github.com/JuliaNLSolvers/Optim.jl.
- The comparison has its limitations. We are solving for a different optimization problem. An intuition for this distinction comes from the fact that the derivative of the phase of the laser is its detuning, and as such any discontinuity of the phase leads to a Dirac function.
- R. F. dos Santos, Magnus Tensor, 2025, https://gitlab.tue.nl/20235021/magnustensor.