Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Quantum algorithm for solving generalized eigenvalue problems with application to the Schrödinger equation

Grzegorz Rajchel-Mieldzioć, Szymon Pliś, and Emil Zak*

  • BEIT sp. z o.o., ul. Wadowicka 8a, 30-415 Kraków, Poland

  • *Contact author: emil@beit.tech

Phys. Rev. Research 8, 023192 – Published 20 May, 2026

DOI: https://doi.org/10.1103/td1z-jffj

Abstract

Accurate computation of multiple eigenvalues of quantum Hamiltonians is essential in quantum chemistry, materials science, and molecular spectroscopy. Estimating excited-state energies is challenging for classical algorithms due to exponential scaling with system size, posing an even harder problem than ground-state calculations. We present a quantum algorithm for estimating eigenvalues and singular values of parametrized matrix families, including solving generalized eigenvalue problems that frequently arise in quantum simulations. Our method uses quantum amplitude amplification and phase estimation to identify matrix eigenvalues by locating minima in the singular value spectrum. We demonstrate our algorithm by proposing a quantum computing formulation of the pseudospectral collocation method for the Schrödinger equation. We estimate fault-tolerant quantum resource requirements for the quantum collocation method, showing favorable scaling in the size of the problem N [up to Õ(N)] compared to classical implementations with Õ(N), for certain well-behaved potentials. Additionally, unlike the standard collocation method, which results in a generalized eigenvalue problem requiring matrix inversion, our algorithm circumvents the associated numerical instability by scanning a parametrized matrix family and detecting eigenvalues through singular value minimization. This approach is particularly effective when multiple eigenvalues are needed or when the generalized eigenvalue problem involves a high condition number. In the fault-tolerant era, our method may thus be useful for simulating high-dimensional molecular systems with dense spectra involving highly excited states, such as those encountered in molecular photodynamics or quasicontinuum regimes in many-body and solid-state systems.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (80)

  1. M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, 10th anniversary ed. (Cambridge University Press, Cambridge, 2010), 10th anniversary ed.
  2. S. Lee, J. Lee, H. Zhai, Y. Tong, A. M. Dalzell, A. Kumar, P. Helms, J. Gray, Z.-H. Cui, W. Liu, M. Kastoryano, R. Babbush, J. Preskill, D. R. Reichman, E. T. Campbell, E. F. Valeev, L. Lin, and G. K.-L. Chan, Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry, Nat. Commun. 14, 1952 (2023).
  3. Y. Su, D. W. Berry, N. Wiebe, N. Rubin, and R. Babbush, Fault-tolerant quantum simulations of chemistry in first quantization, PRX Quantum 2, 040332 (2021).
  4. R. Babbush, C. Gidney, D. W. Berry, N. Wiebe, J. McClean, A. Paler, A. Fowler, and H. Neven, Encoding electronic spectra in quantum circuits with linear T complexity, Phys. Rev. X 8, 041015 (2018).
  5. J. Lee, D. W. Berry, C. Gidney, W. J. Huggins, J. R. McClean, N. Wiebe, and R. Babbush, Even more efficient quantum computations of chemistry through tensor hypercontraction, PRX Quantum 2, 030305 (2021).
  6. V. von Burg, G. H. Low, T. Häner, D. S. Steiger, M. Reiher, M. Roetteler, and M. Troyer, Quantum computing enhanced computational catalysis, Phys. Rev. Res. 3, 033055 (2021).
  7. K. Deka and E. Zak, Simultaneously optimizing symmetry shifts and tensor factorizations for cost-efficient fault-tolerant quantum simulations of electronic Hamiltonians, J. Chem. Theory Comput. 21, 4458 (2025).
  8. P. Richerme, M. C. Revelle, C. G. Yale, D. Lobser, A. D. Burch, S. M. Clark, D. Saha, M. A. Lopez-Ruiz, A. Dwivedi, J. M. Smith, S. A. Norrell, A. Sabry, and S. S. Iyengar, Quantum computation of hydrogen bond dynamics and vibrational spectra, J. Phys. Chem. Lett. 14, 7256 (2023).
  9. L. A. Mariano, V. H. A. Nguyen, J. B. Petersen, M. Björnsson, J. Bendix, G. R. Eaton, S. S. Eaton, and A. Lunghi, The role of electronic excited states in the spin-lattice relaxation of spin-1/2 molecules, Sci. Adv. 11, eadr0168 (2025).
  10. P. R. Bunker, P. Jensen, and C. Jungen, Molecular symmetry and spectroscopy, Phys. Today 52, 63 (1999).
  11. C. Kittel and P. B. Kahn, Quantum theory of solids, Am. J. Phys. 33, 517 (1965).
  12. B. M. Weight, X. Li, and Y. Zhang, Theory and modeling of light-matter interactions in chemistry: Current and future, Phys. Chem. Chem. Phys. 25, 31554 (2023).
  13. S. Mukherjee, M. Pinheiro, Jr., B. Demoulin, and M. Barbatti, Simulations of molecular photodynamics in long timescales, Philos. Trans. R. Soc. A 380, 20200382 (2022).
  14. G. Brassard, P. Høyer, M. Mosca, and A. Tapp, Quantum amplitude amplification and estimation, Contemporary Mathematics 305, 53 (2002).
  15. L. K. Grover, Fixed-point quantum search, Phys. Rev. Lett. 95, 150501 (2005).
  16. T. J. Yoder, G. H. Low, and I. L. Chuang, Fixed-point quantum search with an optimal number of queries, Phys. Rev. Lett. 113, 210501 (2014).
  17. D. W. Berry, A. M. Childs, and R. Kothari, Hamiltonian simulation with nearly optimal dependence on all parameters, in 2015 IEEE 56th Annual Symposium on Foundations of Computer Science (IEEE, Berkeley, 2015), pp. 792–809.
  18. J. M. Martyn, Y. Liu, Z. E. Chin, and I. L. Chuang, Efficient fully-coherent quantum signal processing algorithms for real-time dynamics simulation, J. Chem. Phys. 158, 024106 (2023).
  19. C. Shao, Computing eigenvalues of diagonalizable matrices on a quantum computer, ACM Trans. Quantum Comput. 3, 1 (2022).
  20. A. Kerzner, V. Gheorghiu, M. Mosca, T. Guilbaud, F. Carminati, F. Fracas, and L. Dellantonio, A square-root speedup for finding the smallest eigenvalue, Quantum Sci. Technol. 9, 045025 (2024).
  21. G. H. Low and Y. Su, Quantum eigenvalue processing, in 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS) (IEEE, Philadelphia, PA, 2024), pp. 1051–1062.
  22. Z. Ding, H. Li, L. Lin, H. Ni, L. Ying, and R. Zhang, Quantum multiple eigenvalue Gaussian filtered search: An efficient and versatile quantum phase estimation method, Quantum 8, 1487 (2024).
  23. S. Jin, S. Wu, G. Zhou, Y. Li, L. Li, B. Li, and X. Wang, A query‐based quantum eigensolver, Quantum Eng. 2, e49 (2020).
  24. Y. Chen, A. Gilyén, and R. de Wolf, A quantum speed-up for approximating the top eigenvectors of a matrix, in Proceedings of the 2025 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA, Philadelphia, PA, 2025), pp. 994–1036.
  25. Z. Zimborás et al., Myths around quantum computation before full fault tolerance: What no-go theorems rule out and what they don’t, arXiv:2501.05694.
  26. J. Tilly, H. Chen, S. Cao, D. Picozzi, K. Setia, Y. Li, E. Grant, L. Wossnig, I. Rungger, G. H. Booth, and J. Tennyson, The variational quantum eigensolver: A review of methods and best practices, Phys. Rep. 986, 1 (2022).
  27. A. C. Peet and W. Yang, The collocation method for calculating vibrational bound states of molecular systems—With application to Ar–HCI, J. Chem. Phys. 90, 1746 (1989).
  28. T. Helgaker, P. Jørgensen, and J. Olsen, Molecular Electronic-Structure Theory (John Wiley & Sons, Ltd, New York, NY, 2000).
  29. T. Carrington, Jr., Perspective: Computing (ro-)vibrational spectra of molecules with more than four atoms, J. Chem. Phys. 146, 120902 (2017).
  30. S. Manzhos, M. Ihara, and T. Carrington, Using collocation to solve the Schrödinger equation, J. Chem. Theory Comput. 19, 1641 (2023).
  31. X.-G. Hu, T.-S. Ho, and H. Rabitz, The collocation method based on a generalized inverse multiquadric basis for bound-state problems, Comput. Phys. Commun. 113, 168 (1998).
  32. J. Brown and T. Carrington, Jr., Using an iterative eigensolver to compute vibrational energies with phase-spaced localized basis functions, J. Chem. Phys. 143, 044104 (2015).
  33. B. Hashemi, Y. Nakatsukasa, and L. N. Trefethen, Rectangular eigenvalue problems, Adv. Comput. Math. 48, 80 (2022).
  34. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes 3rd Edition: The Art of Scientific Computing, 3rd ed. (Cambridge University Press, USA, 2007).
  35. B. Ford and G. Hall, The generalized eigenvalue problem in quantum chemistry, Comput. Phys. Commun. 8, 337 (1974).
  36. G. Dusson, M.-S. Dupuy, and I.-M. Lygatsika, A posteriori error estimates for Schrödinger operators discretized with linear combinations of atomic orbitals, SIAM J. Numer. Anal. 63, 2395 (2025).
  37. V. Szalay, T. Szidarovszky, G. Czakó, and A. G. Császár, Jr., A paradox of grid-based representation techniques: Accurate eigenvalues from inaccurate matrix elements, J. Math. Chem. 50, 636 (2012).
  38. G. Avila and T. Carrington, Nonproduct quadrature grids for solving the vibrational Schrödinger equation, J. Chem. Phys. 131, 174103 (2009).
  39. B. Poirier and J. C. Light, Efficient distributed Gaussian basis for rovibrational spectroscopy calculations, J. Chem. Phys. 113, 211 (2000).
  40. G. Richings, I. Polyak, K. Spinlove, G. Worth, I. Burghardt, and B. Lasorne, Quantum dynamics simulations using Gaussian wavepackets: The vMCG method, Int. Rev. Phys. Chem. 34, 269 (2015).
  41. I. Burghardt, K. Giri, and G. A. Worth, Multimode quantum dynamics using Gaussian wavepackets: The Gaussian-based multiconfiguration time-dependent Hartree (G-MCTDH) method applied to the absorption spectrum of pyrazine, J. Chem. Phys. 129, 174104 (2008).
  42. E. J. Zak and T. Carrington, Using collocation and a hierarchical basis to solve the vibrational Schrödinger equation, J. Chem. Phys. 150, 204108 (2019).
  43. T. Carrington, Jr., Using collocation to study the vibrational dynamics of molecules, Spectrochim. Acta, Part A 248, 119158 (2021).
  44. J. Simmons, Jr. and T. Carrington, Computing vibrational spectra using a new collocation method with a pruned basis and more points than basis functions: Avoiding quadrature, J. Chem. Phys. 158, 144115 (2023).
  45. L. Hogben, Handbook of Linear Algebra (Chapman and Hall/CRC, Boca Raton, FL, 2013).
  46. In this case, N is the larger of the two dimensions.
  47. Later on, we generalize this problem also to singular values.
  48. For normal matrices, ɛ pseudospectrum is the region ±ɛ around their spectrum; therefore, this notion is aligned with our goal of finding numbers close to the exact eigenvalues.
  49. More specifically, for each register, with the highest probability, the outcome will be the true eigenvalue λ. However, there will also be a small irrelevant part |g〉, which will eventually not contribute much to the final estimation of the eigenvalue |η〉=1−γ|λ〉+γ∑i|gi〉, as the coefficient γ≈0.
  50. G. H. Low and I. L. Chuang, Optimal Hamiltonian simulation by quantum signal processing, Phys. Rev. Lett. 118, 010501 (2017).
  51. D. Camps, L. Lin, R. Van Beeumen, and C. Yang, Explicit quantum circuits for block encodings of certain sparse matrices, SIAM J. Matrix. Anal. Appl. 45, 801 (2024).
  52. In the above, we disregard the registers related to the QPE, as these do not change the value of the characteristic function qubit.
  53. In principle, due to the inexact accuracy of QPE, the median qubit will also contain contributions from other states, but we drop this for clarity.
  54. M. Cardona and M. L. W. Thewalt, Isotope effects on the optical spectra of semiconductors, Rev. Mod. Phys. 77, 1173 (2005).
  55. C. Kittel, Introduction to Solid State Physics (Wiley, Hoboken, NJ, 2004), 8th ed.
  56. R. M. Nieminen, Electronic structure calculations for materials design, J. Phys.: Condens. Matter 14, 2859 (2002).
  57. S. Boys, Some bilinear convergence characteristics of the solutions of dissymmetric secular equations, Proc. R. Soc. London A 309, 195 (1969).
  58. G. Avila, Jr. and T. Carrington, A multi-dimensional Smolyak collocation method in curvilinear coordinates for computing vibrational spectra, J. Chem. Phys. 143, 214108 (2015).
  59. R. B. Lehoucq, D. C. Sorensen, and C. Yang, ARPACK Users’ Guide: Solution of Large-Scale Eigenvalue Problems with Implicitly Restarted Arnoldi Methods (Society for Industrial and Applied Mathematics, Philadelphia, PA, 1998).
  60. M. Myllykoski and C. C. Kjelgaard Mikkelsen, Task‐based, GPU‐accelerated and robust library for solving dense nonsymmetric eigenvalue problems, Concurrency Comput.: Pract. Exper. 33, e5915 (2021).
  61. A. W. Harrow, A. Hassidim, and S. Lloyd, Quantum algorithm for linear systems of equations, Phys. Rev. Lett. 103, 150502 (2009).
  62. D. Trenev, P. J. Ollitrault, S. M. Harwood, T. P. Gujarati, S. Raman, A. Mezzacapo, and S. Mostame, Refining resource estimation for the quantum computation of vibrational molecular spectra through Trotter error analysis, Quantum 9, 1630 (2025).
  63. To avoid obfuscation by too many parameters, we also assume the number of grid points in the spatial coordinate M is proportional to N, i.e., M=O(N), making the ratio of the dimensions of the initial matrices B and B′′ constant.
  64. This assumption is valid for localized basis sets, such as the Gaussian basis.
  65. V. V. Williams, Y. Xu, Z. Xu, and R. Zhou, New bounds for matrix multiplication from alpha to omega, in Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA, Philadelphia, PA, 2024), pp. 3792–3835.
  66. J. Alman and V. V. Williams, A refined laser method and faster matrix multiplication, TheoretiCS 3, 11261 (2024).
  67. Y. Saad and M. H. Schultz, GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems, SIAM J. Sci. Stat. Comput. 7, 856 (1986).
  68. C. Musco, C. Musco, and A. Sidford, Stability of the Lanczos method for matrix function approximation, in Proceedings of the 2018 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA, Philadelphia, PA, 2018), pp. 1605–1624.
  69. Note that we neglect the dependence of the Krylov space size on the number of eigenvalues requested, which is often logarithmic [67].
  70. G. Golub and C. van Loan, Matrix Computations (Johns Hopkins University Press, Baltimore, MD, 1996).
  71. For higher-order truncations, the complexities of the classical and quantum algorithms both grow linearly in J. Thus, quantum advantage can be searched already for low J’s.
  72. In the case of vanishing derivative of the singular value dependence on the parameter α, K might be proportional to roots of 1/ɛ, making this dependence better from the quantum computing perspective.
  73. C. Sünderhauf, E. Campbell, and J. Camps, Block-encoding structured matrices for data input in quantum computing, Quantum 8, 1226 (2024).
  74. P. Mukhopadhyay, T. F. Stetina, and N. Wiebe, Quantum simulation of the first-quantized Pauli-Fierz Hamiltonian, PRX Quantum 5, 010345 (2024).
  75. L. Ruiz-Perez and J. C. Garcia-Escartin, Quantum arithmetic with the quantum Fourier transform, Quantum Inf. Process. 16, 152 (2017).
  76. D. Gosset, R. Kothari, and K. Wu, Quantum state preparation with optimal T-count, in Proceedings of the 2026 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA, Philadelphia, PA, 2026), pp. 3378–3406.
  77. It is straightforward to observe that by inspecting the mapping f:CN×N→CN×N, given by f(A)=A−1, with the domain restricted to invertible matrices. Its directional derivative in direction H is given by f′(A)(H)=−A−1HA−1, we choose unit vectors e⃗ and f⃗ so that Ae⃗=σf⃗, where σ is the smallest singular value of A. Then, for H=f⃗∧e⃗ we have f′(A)(H)f⃗=σ−2e⃗, where ∧ signifies outer product between vectors.
  78. L. Lin and Y. Tong, Optimal polynomial based quantum eigenstate filtering with application to solving quantum linear systems, Quantum 4, 361 (2020).
  79. J. M. Martyn, Z. M. Rossi, A. K. Tan, and I. L. Chuang, Grand unification of quantum algorithms, PRX Quantum 2, 040203 (2021).
  80. G. Rajchel-Mieldzioć, S. Pliś, and E. Zak, Github repository Schrödinger equation dataset (2026), https://github.com/anwlwp/Schroedinger-equation.

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation