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Variational simulation of higher-spin systems on qubit-based quantum simulators

Chufan Lyu1,2,*, Zuoheng Zou3, Xusheng Xu3, Man-Hong Yung3,4,5,6,7, and Abolfazl Bayat1,2,†

  • *Contact author: chufan.lyu1@std.uestc.edu.cn
  • Contact author: abolfazl.bayat@uestc.edu.cn

Phys. Rev. Research 8, 033119 – Published 28 July, 2026

DOI: https://doi.org/10.1103/lfc9-cjqd

Abstract

Qubit-based quantum simulators naturally target two-level systems, whereas many quantum many-body problems are intrinsically d level. Encodings from qudits to qubits then enlarge the Hilbert space and can introduce unphysical states that interfere with variational optimization. We formulate a variational framework for encoded d-level models that suppresses these illegitimate states with penalty terms and benchmark it for spin-1 and spin-3/2 bilinear-biquadratic Heisenberg chains. We compare binary encoding, which minimizes the qubit overhead, with symmetry encoding, which preserves the relevant spin symmetries and enables symmetry-conserving ansätze. Although binary encoding is more qubit efficient, its hardware-efficient ansatz is harder to train and less effective at exploiting conserved quantities. In contrast, symmetry encoding requires more qubits but reaches substantially higher fidelities, converges faster, and exhibits better trainability than the binary hardware-efficient ansatz. These results identify symmetry-preserving encodings as a practical route to simulating higher-spin models on existing qubit platforms.

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References (129)

  1. J. Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018).
  2. R. P. Feynman, Simulating physics with computers, Int. J. Theor. Phys. 21, 467 (1982).
  3. S. Lloyd, Quantum approximate optimization is computationally universal, arXiv:1812.11075.
  4. P. Bordia, H. Lüschen, S. Scherg, S. Gopalakrishnan, M. Knap, U. Schneider, and I. Bloch, Probing slow relaxation and many-body localization in two-dimensional quasiperiodic systems, Phys. Rev. X 7, 041047 (2017).
  5. M. Schreiber, S. S. Hodgman, P. Bordia, H. P. Lüschen, M. H. Fischer, R. Vosk, E. Altman, U. Schneider, and I. Bloch, Observation of many-body localization of interacting fermions in a quasirandom optical lattice, Science 349, 842 (2015).
  6. C. Gross and I. Bloch, Quantum simulations with ultracold atoms in optical lattices, Science 357, 995 (2017).
  7. A. Omran, H. Levine, A. Keesling, G. Semeghini, T. T. Wang, S. Ebadi, H. Bernien, A. S. Zibrov, H. Pichler, S. Choi, J. Cui, M. Rossignolo, P. Rembold, S. Montangero, T. Calarco, M. Endres, M. Greiner, V. Vuletić, and M. D. Lukin, Generation and manipulation of Schrödinger cat states in Rydberg atom arrays, Science 365, 570 (2019).
  8. A. Keesling, A. Omran, H. Levine, H. Bernien, H. Pichler, S. Choi, R. Samajdar, S. Schwartz, P. Silvi, S. Sachdev, P. Zoller, M. Endres, M. Greiner, V. Vuletić, and M. D. Lukin, Quantum Kibble–Zurek mechanism and critical dynamics on a programmable Rydberg simulator, Nature (London) 568, 207 (2019).
  9. J. Wang, S. Paesani, R. Santagati, S. Knauer, A. A. Gentile, N. Wiebe, M. Petruzzella, J. L. O’Brien, J. G. Rarity, A. Laing, and M. G. Thompson, Experimental quantum Hamiltonian learning, Nat. Phys. 13, 551 (2017).
  10. H.-S. Zhong, H. Wang, Y.-H. Deng, M.-C. Chen, L.-C. Peng, Y.-H. Luo, J. Qin, D. Wu, X. Ding, Y. Hu, et al., Quantum computational advantage using photons, Science 370, 1460 (2020).
  11. J. Carolan, M. Mohseni, J. P. Olson, M. Prabhu, C. Chen, D. Bunandar, M. Y. Niu, N. C. Harris, F. N. C. Wong, M. Hochberg, S. Lloyd, and D. Englund, Variational quantum unsampling on a quantum photonic processor, Nat. Phys. 16, 322 (2020).
  12. J. Li, R. Fan, H. Wang, B. Ye, B. Zeng, H. Zhai, X. Peng, and J. Du, Measuring out-of-time-order correlators on a nuclear magnetic resonance quantum simulator, Phys. Rev. X 7, 031011 (2017).
  13. B. P. Lanyon, C. Hempel, D. Nigg, M. Müller, R. Gerritsma, F. Zähringer, P. Schindler, J. T. Barreiro, M. Rambach, G. Kirchmair, M. Hennrich, P. Zoller, R. Blatt, and C. F. Roos, Universal digital quantum simulation with trapped ions, Science 334, 57 (2011).
  14. J. Zhang, G. Pagano, P. W. Hess, A. Kyprianidis, P. Becker, H. Kaplan, A. V. Gorshkov, Z.-X. Gong, and C. Monroe, Observation of a many-body dynamical phase transition with a 53-qubit quantum simulator, Nature (London) 551, 601 (2017).
  15. C. Hempel, C. Maier, J. Romero, J. McClean, T. Monz, H. Shen, P. Jurcevic, B. P. Lanyon, P. Love, R. Babbush, A. Aspuru-Guzik, R. Blatt, and C. F. Roos, Quantum chemistry calculations on a trapped-ion quantum simulator, Phys. Rev. X 8, 031022 (2018).
  16. C. Kokail, C. Maier, R. van Bijnen, T. Brydges, M. K. Joshi, P. Jurcevic, C. A. Muschik, P. Silvi, R. Blatt, C. F. Roos, et al., Self-verifying variational quantum simulation of lattice models, Nature (London) 569, 355 (2019).
  17. Z. Han, C. Lyu, Y. Zhou, J. Yuan, J. Chu, W. Nuerbolati, H. Jia, L. Nie, W. Wei, Z. Yang, L. Zhang, Z. Zhang, C.-K. Hu, L. Hu, J. Li, D. Tan, A. Bayat, S. Liu, F. Yan, and D. Yu, Multilevel variational spectroscopy using a programmable quantum simulator, Phys. Rev. Res. 6, 013015 (2024).
  18. Y. Salathé, M. Mondal, M. Oppliger, J. Heinsoo, P. Kurpiers, A. Potočnik, A. Mezzacapo, U. L. Heras, L. Lamata, E. Solano, S. Filipp, and A. Wallraff, Digital quantum simulation of spin models with circuit quantum electrodynamics, Phys. Rev. X 5, 021027 (2015).
  19. C. S. Wang, J. C. Curtis, B. J. Lester, Y. Zhang, Y. Y. Gao, J. Freeze, V. S. Batista, P. H. Vaccaro, I. L. Chuang, L. Frunzio, L. Jiang, S. M. Girvin, and R. J. Schoelkopf, Efficient multiphoton sampling of molecular vibronic spectra on a superconducting bosonic processor, Phys. Rev. X 10, 021060 (2020).
  20. A. H. Karamlou, W. A. Simon, A. Katabarwa, T. L. Scholten, B. Peropadre, and Y. Cao, Analyzing the performance of variational quantum factoring on a superconducting quantum processor, npj Quantum Inf. 7, 156 (2021).
  21. C. Neill, T. McCourt, X. Mi, Z. Jiang, M. Y. Niu, W. Mruczkiewicz, I. Aleiner, F. Arute, K. Arya, J. Atalaya, et al., Accurately computing the electronic properties of a quantum ring, Nature (London) 594, 508 (2021).
  22. J. Han, W. Cai, L. Hu, X. Mu, Y. Ma, Y. Xu, W. Wang, H. Wang, Y. P. Song, C.-L. Zou, and L. Sun, Experimental simulation of open quantum system dynamics via Trotterization, Phys. Rev. Lett. 127, 020504 (2021).
  23. J. Braumüller, A. H. Karamlou, Y. Yanay, B. Kannan, D. Kim, M. Kjaergaard, A. Melville, B. M. Niedzielski, Y. Sung, A. Vepsäläinen, R. Winik, J. L. Yoder, T. P. Orlando, S. Gustavsson, C. Tahan, and W. D. Oliver, Probing quantum information propagation with out-of-time-ordered correlators, Nat. Phys. 18, 172 (2022).
  24. X. Zhang, W. Jiang, J. Deng, K. Wang, J. Chen, P. Zhang, W. Ren, H. Dong, S. Xu, Y. Gao, et al., Digital quantum simulation of Floquet symmetry-protected topological phases, Nature (London) 607, 468 (2022).
  25. Y.-H. Shi, Y. Liu, Y.-R. Zhang, Z. Xiang, K. Huang, T. Liu, Y.-Y. Wang, J.-C. Zhang, C.-L. Deng, G.-H. Liang, et al., Quantum simulation of topological zero modes on a 41-qubit superconducting processor, Phys. Rev. Lett. 131, 080401 (2023).
  26. M. H. Levitt, Spin Dynamics: Basics of Nuclear Magnetic Resonance (John Wiley & Sons, Chichester, UK, 2013).
  27. M. P. A. Fisher, P. B. Weichman, G. Grinstein, and D. S. Fisher, Boson localization and the superfluid-insulator transition, Phys. Rev. B 40, 546 (1989).
  28. E. B. Wilson, J. C. Decius, P. C. Cross, and B. R. Sundheim, Molecular vibrations: The theory of infrared and Raman vibrational spectra, J. Electrochem. Soc. 102, 235C (1955).
  29. M. Shimizu, Itinerant electron magnetism, Rep. Prog. Phys. 44, 329 (1981).
  30. A. Glos, A. Krawiec, and Z. Zimborás, Space-efficient binary optimization for variational quantum computing, npj Quantum Inf. 8, 39 (2022).
  31. Z. Tabi, K. H. El-Safty, Z. Kallus, P. Hága, T. Kozsik, A. Glos, and Z. Zimborás, Quantum optimization for the graph coloring problem with space-efficient embedding, in 2020 IEEE International Conference on Quantum Computing and Engineering (QCE), Denver, CO, USA (IEEE, Piscataway, NJ, 2020), pp. 56–62.
  32. P. Liu, R. Wang, J.-N. Zhang, Y. Zhang, X. Cai, H. Xu, Z. Li, J. Han, X. Li, G. Xue, W. Liu, L. You, Y. Jin, and H. Yu, Performing SU(d) operations and rudimentary algorithms in a superconducting transmon qudit for d=3 and d=4, Phys. Rev. X 13, 021028 (2023).
  33. M. Meth, J. F. Haase, J. Zhang, C. Edmunds, L. Postler, A. Steiner, A. J. Jena, L. Dellantonio, R. Blatt, P. Zoller, T. Monz, P. Schindler, C. Muschik, and M. Ringbauer, Simulating 2D lattice gauge theories on a qudit quantum computer, Nat. Phys. 21, 570 (2025).
  34. T. V. Zache, D. González-Cuadra, and P. Zoller, Fermion-qudit quantum processors for simulating lattice gauge theories with matter, Quantum 7, 1140 (2023).
  35. D. González-Cuadra, T. V. Zache, J. Carrasco, B. Kraus, and P. Zoller, Hardware efficient quantum simulation of non-Abelian gauge theories with qudits on Rydberg platforms, Phys. Rev. Lett. 129, 160501 (2022).
  36. P. Hrmo, B. Wilhelm, L. Gerster, M. W. van Mourik, M. Huber, R. Blatt, P. Schindler, T. Monz, and M. Ringbauer, Native qudit entanglement in a trapped ion quantum processor, Nat. Commun. 14, 2242 (2023).
  37. M. Ringbauer, M. Meth, L. Postler, R. Stricker, R. Blatt, P. Schindler, and T. Monz, A universal qudit quantum processor with trapped ions, Nat. Phys. 18, 1053 (2022).
  38. M. X. Luo and X. J. Wang, Universal quantum computation with qudits, Sci. China Phys. Mech. Astron. 57, 1712 (2014).
  39. L.-A. Wu and D. A. Lidar, Qubits as parafermions, J. Math. Phys. 43, 4506 (2002).
  40. C. D. Batista and G. Ortiz, Algebraic approach to interacting quantum systems, Adv. Phys. 53, 1 (2004).
  41. N. P. D. Sawaya, T. Menke, T. H. Kyaw, S. Johri, A. Aspuru-Guzik, and G. G. Guerreschi, Resource-efficient digital quantum simulation of d-level systems for photonic, vibrational, and spin-s Hamiltonians, npj Quantum Inf. 6, 49 (2020).
  42. R. H. Dicke, Coherence in spontaneous radiation processes, Phys. Rev. 93, 99 (1954).
  43. K. Yang, Y. Zhu, X. Zeng, Z. Zou, M.-H. Yung, and Z. Wang, Cost of locally approximating high-dimensional ground states of contextual quantum models, Commun. Phys. 8, 204 (2025).
  44. J. Chen, T. Stollenwerk, and N. Chancellor, Performance of domain-wall encoding for quantum annealing, IEEE Trans. Quantum Eng. 2, 1 (2021).
  45. N. P. D. Sawaya, A. T. Schmitz, and S. Hadfield, Encoding trade-offs and design toolkits in quantum algorithms for discrete optimization: Coloring, routing, scheduling, and other problems, Quantum 7, 1111 (2023).
  46. J. Berwald, N. Chancellor, and R. Dridi, Understanding domain-wall encoding theoretically and experimentally, Philos. Trans. R. Soc. A 381, 20210410 (2023).
  47. S. Karimi and P. Ronagh, Practical integer-to-binary mapping for quantum annealers, Quantum Inf. Proc. 18, 94 (2019).
  48. N. Chancellor, Domain wall encoding of discrete variables for quantum annealing and QAOA, Quantum Sci. Technol. 4, 045004 (2019).
  49. O. Di Matteo, A. McCoy, P. Gysbers, T. Miyagi, R. M. Woloshyn, and P. Navrátil, Improving Hamiltonian encodings with the Gray code, Phys. Rev. A 103, 042405 (2021).
  50. J. Tilly, H. Chen, S. Cao, D. Picozzi, K. Setia, Y. Li, E. Grant, L. Wossnig, I. Ramo, J. Biamonte, et al., The variational quantum eigensolver: A review of methods and best practices, Phys. Rep. 986, 1 (2022).
  51. H.-S. Zhong, Y.-H. Deng, J. Qin, H. Wang, M.-C. Chen, L.-C. Peng, Y.-H. Luo, D. Wu, S.-Q. Gong, H. Su, et al., Phase-programmable Gaussian boson sampling using stimulated squeezed light, Phys. Rev. Lett. 127, 180502 (2021).
  52. Y.-H. Deng, Y.-C. Gu, H.-L. Liu, S.-Q. Gong, H. Su, Z.-J. Zhang, H.-Y. Tang, M.-H. Jia, J.-M. Xu, M.-C. Chen, et al., Gaussian boson sampling with pseudo-photon-number-resolving detectors and quantum computational advantage, Phys. Rev. Lett. 131, 150601 (2023).
  53. F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, S. Boixo, M. Broughton, B. B. Buckley, D. A. Buell, et al., Hartree-Fock on a superconducting qubit quantum computer Science 369, 1084 (2020).
  54. P. J. J. O’Malley, R. Babbush, I. D. Kivlichan, J. Romero, J. R. McClean, R. Barends, J. Kelly, P. Roushan, A. Tranter, N. Ding, et al., Scalable quantum simulation of molecular energies, Phys. Rev. X 6, 031007 (2016).
  55. A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, J. M. Chow, and J. M. Gambetta, Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets, Nature (London) 549, 242 (2017).
  56. J. I. Colless, V. V. Ramasesh, D. Dahlen, M. S. Blok, M. E. Kimchi-Schwartz, J. R. McClean, J. Carter, W. A. de Jong, and I. Siddiqi, Computation of molecular spectra on a quantum processor with an error-resilient algorithm, Phys. Rev. X 8, 011021 (2018).
  57. Y. Nam, J.-S. Chen, N. C. Pisenti, K. Wright, C. Delaney, D. Maslov, K. R. Brown, S. Allen, J. M. Amini, J. Apisdorf, et al., Ground-state energy estimation of the water molecule on a trapped-ion quantum computer, npj Quantum Inf. 6, 33 (2020).
  58. E. Farhi, J. Goldstone, and S. Gutmann, A quantum approximate optimization algorithm, arXiv:1411.4028.
  59. S. Bravyi, A. Kliesch, R. Koenig, and E. Tang, Obstacles to variational quantum optimization from symmetry protection, Phys. Rev. Lett. 125, 260505 (2020).
  60. M. Karácsony, L. Oroszlány, and Z. Zimboras, Efficient qudit based scheme for photonic quantum computing, SciPost Phys. Core 7, 032 (2024).
  61. J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Quantum machine learning, Nature (London) 549, 195 (2017).
  62. S. Arunachalam and R. de Wolf, Guest column: A survey of quantum learning theory, ACM SIGACT News 48, 41 (2017).
  63. C. Ciliberto, M. Herbster, A. D. Ialongo, M. Pontil, A. Rocchetto, S. Severini, and L. Wossnig, Quantum machine learning: A classical perspective, Proc. R. Soc. A 474, 20170551 (2018).
  64. V. Dunjko and H. J. Briegel, Machine learning & artificial intelligence in the quantum domain: A review of recent progress, Rep. Prog. Phys. 81, 074001 (2018).
  65. E. Farhi and H. Neven, Classification with quantum neural networks on near term processors, arXiv:1802.06002.
  66. M. Schuld and N. Killoran, Quantum machine learning in feature Hilbert spaces, Phys. Rev. Lett. 122, 040504 (2019).
  67. S. A. Wilkinson and M. J. Hartmann, Evaluating the performance of sigmoid quantum perceptrons in quantum neural networks, arXiv:2208.06198.
  68. P. Zapletal, N. A. McMahon, and M. J. Hartmann, Error-tolerant quantum convolutional neural networks for symmetry-protected topological phases, Phys. Rev. Res. 6, 033111 (2024).
  69. C. Cirstoiu, Z. Holmes, J. Iosue, L. Cincio, P. J. Coles, and A. Sornborger, Variational fast forwarding for quantum simulation beyond the coherence time, npj Quantum Inf. 6, 82 (2020).
  70. J. Gibbs, K. Gili, Z. Holmes, B. Commeau, A. Arrasmith, L. Cincio, P. J. Coles, and A. Sornborger, Long-time simulations with high fidelity on quantum hardware, arXiv:2102.04313.
  71. X. Yuan, S. Endo, Q. Zhao, Y. Li, and S. C. Benjamin, Theory of variational quantum simulation, Quantum 3, 191 (2019).
  72. S. McArdle, T. Jones, S. Endo, Y. Li, S. C. Benjamin, and X. Yuan, Variational ansatz-based quantum simulation of imaginary time evolution, npj Quantum Inf. 5, 75 (2019).
  73. K. Heya, K. M. Nakanishi, K. Mitarai, Z. Yan, K. Zuo, Y. Suzuki, T. Sugiyama, S. Tamate, Y. Tabuchi, K. Fujii, and Y. Nakamura, Subspace variational quantum simulator, Phys. Rev. Res. 5, 023078 (2023).
  74. J. Huh, S. Mostame, T. Fujita, M.-H. Yung, and A. Aspuru-Guzik, Linear-algebraic bath transformation for simulating complex open quantum systems, New J. Phys. 16, 123008 (2014).
  75. Z. Hu, R. Xia, and S. Kais, A quantum algorithm for evolving open quantum dynamics on quantum computing devices, Sci. Rep. 10, 3301 (2020).
  76. S. Endo, J. Sun, Y. Li, S. C. Benjamin, and X. Yuan, Variational quantum simulation of general processes, Phys. Rev. Lett. 125, 010501 (2020).
  77. T. Haug and K. Bharti, Generalized quantum assisted simulator, Quantum Sci. Technol. 7, 045019 (2022).
  78. J. J. Meyer, J. Borregaard, and J. Eisert, A variational toolbox for quantum multi-parameter estimation, npj Quantum Inf. 7, 89 (2021).
  79. J. J. Meyer, Fisher information in noisy intermediate-scale quantum applications, Quantum 5, 539 (2021).
  80. J. L. Beckey, M. Cerezo, A. Sone, and P. J. Coles, Variational quantum algorithm for estimating the quantum Fisher information, Phys. Rev. Res. 4, 013083 (2022).
  81. R. Kaubruegger, P. Silvi, C. Kokail, R. van Bijnen, A. M. Rey, J. Ye, A. M. Kaufman, and P. Zoller, Variational spin-squeezing algorithms on programmable quantum sensors, Phys. Rev. Lett. 123, 260505 (2019).
  82. B. Koczor, S. Endo, T. Jones, Y. Matsuzaki, and S. C. Benjamin, Variational-state quantum metrology, New J. Phys. 22, 083038 (2020).
  83. Z. Ma, P. Gokhale, T.-X. Zheng, S. Zhou, X. Yu, L. Jiang, P. Maurer, and F. T. Chong, Adaptive circuit learning for quantum metrology, in 2021 IEEE International Conference on Quantum Computing and Engineering (QCE), Broomfield, CO, USA (IEEE, Piscataway, NJ, 2021), pp. 419–430.
  84. T. Haug and M. S. Kim, Natural parametrized quantum circuit, Phys. Rev. A 106, 052611 (2022).
  85. R. Sagastizabal, S. P. Premaratne, B. A. Klaver, M. A. Rol, V. Negîrneac, M. S. Moreira, X. Zou, S. Johri, N. Muthusubramanian, M. Beekman, C. Zachariadis, V. P. Ostroukh, N. Haider, A. Bruno, A. Y. Matsuura, and L. DiCarlo, Variational preparation of finite-temperature states on a quantum computer, npj Quantum Inf. 7, 130 (2021).
  86. C. Lyu, V. Montenegro, and A. Bayat, Accelerated variational algorithms for digital quantum simulation of many-body ground states, Quantum 4, 324 (2020).
  87. K. M. Nakanishi, K. Mitarai, and K. Fujii, Subspace-search variational quantum eigensolver for excited states, Phys. Rev. Res. 1, 033062 (2019).
  88. O. Higgott, D. Wang, and S. Brierley, Variational quantum computation of excited states, Quantum 3, 156 (2019).
  89. J. R. McClean, M. E. Kimchi-Schwartz, J. Carter, and W. A. De Jong, Hybrid quantum-classical hierarchy for mitigation of decoherence and determination of excited states, Phys. Rev. A 95, 042308 (2017).
  90. R. Santagati, J. Wang, A. A. Gentile, S. Paesani, N. Wiebe, J. R. McClean, S. Morley-Short, P. J. Shadbolt, D. Bonneau, J. W. Silverstone, et al., Witnessing eigenstates for quantum simulation of Hamiltonian spectra, Sci. Adv. 4, eaap9646 (2018).
  91. C.-L. Hong, L. Colmenarez, L. Ding, C. L. Benavides-Riveros, and C. Schilling, Refining the weighted subspace-search variational quantum eigensolver: Compression of ansätze into a single pure state and optimization of weights, arXiv:2306.11844 [quant-ph].
  92. V. Feulner and M. J. Hartmann, Variational quantum eigensolver ansatz for the J1J2-model, Phys. Rev. B 106, 144426 (2022).
  93. P. K. Barkoutsos, J. F. Gonthier, I. Sokolov, N. Moll, G. Salis, A. Fuhrer, M. Ganzhorn, D. J. Egger, M. Troyer, A. Mezzacapo, et al., Quantum algorithms for electronic structure calculations: Particle-hole Hamiltonian and optimized wave-function expansions, Phys. Rev. A 98, 022322 (2018).
  94. H. Wang, S. Ashhab, and F. Nori, Efficient quantum algorithm for preparing molecular-system-like states on a quantum computer, Phys. Rev. A 79, 042335 (2009).
  95. K. Seki, T. Shirakawa, and S. Yunoki, Symmetry-adapted variational quantum eigensolver, Phys. Rev. A 101, 052340 (2020).
  96. B. T. Gard, L. Zhu, G. S. Barron, N. J. Mayhall, S. E. Economou, and E. Barnes, Efficient symmetry-preserving state preparation circuits for the variational quantum eigensolver algorithm, npj Quantum Inf. 6, 10 (2020).
  97. G. S. Barron, B. T. Gard, O. J. Altman, N. J. Mayhall, E. Barnes, and S. E. Economou, Preserving symmetries for variational quantum eigensolvers in the presence of noise, Phys. Rev. Appl. 16, 034003 (2021).
  98. F. Zhang, N. Gomes, N. F. Berthusen, P. P. Orth, C.-Z. Wang, K.-M. Ho, and Y.-X. Yao, Shallow-circuit variational quantum eigensolver based on symmetry-inspired Hilbert space partitioning for quantum chemical calculations, Phys. Rev. Res. 3, 013039 (2021).
  99. H. Zheng, Z. Li, J. Liu, S. Strelchuk, and R. Kondor, Speeding up learning quantum states through group equivariant convolutional quantum ansätze, PRX Quantum 4, 020327 (2023).
  100. G.-L. R. Anselmetti, D. Wierichs, C. Gogolin, and R. M. Parrish, Local, expressive, quantum-number-preserving VQE ansätze for fermionic systems, New J. Phys. 23, 113010 (2021).
  101. J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, The theory of variational hybrid quantum-classical algorithms, New J. Phys. 18, 023023 (2016).
  102. I. G. Ryabinkin, S. N. Genin, and A. F. Izmaylov, Constrained variational quantum eigensolver: Quantum computer search engine in the Fock space, J. Chem. Theory Comput. 15, 249 (2019).
  103. R. Dutta, N. P. Vu, C. Xu, D. G. A. Cabral, N. Lyu, A. V. Soudackov, X. Dan, H. Li, C. Wang, and V. S. Batista, Simulating electronic structure on bosonic quantum computers, J. Chem. Theory Comput. 21, 2281 (2025).
  104. R. Nigmatullin, K. Hémery, K. Ghanem, S. Moses, D. Gresh, P. Siegfried, M. Mills, T. Gatterman, N. Hewitt, E. Granet, and H. Dreyer, Experimental demonstration of breakeven for a compact fermionic encoding, Nat. Phys. 21, 1319 (2025).
  105. M. Ilyas, S. Cui, and M. Perkowski, Ternary logic design in topological quantum computing, J. Phys. A: Math. Theor. 55, 305302 (2022).
  106. Q. Li, C. Mukhopadhyay, and A. Bayat, Fermionic simulators for enhanced scalability of variational quantum simulation, Phys. Rev. Res. 5, 043175 (2023).
  107. C. Lyu, X. Xu, M.-H. Yung, and A. Bayat, Symmetry enhanced variational quantum spin eigensolver, Quantum 7, 899 (2023).
  108. R. Wiersema, C. Zhou, Y. de Sereville, J. F. Carrasquilla, Y. B. Kim, and H. Yuen, Exploring entanglement and optimization within the Hamiltonian variational ansatz, PRX Quantum 1, 020319 (2020).
  109. J. Haegeman, B. Pirvu, D. J. Weir, J. I. Cirac, T. J. Osborne, H. Verschelde, and F. Verstraete, Variational matrix product ansatz for dispersion relations, Phys. Rev. B 85, 100408 (2012).
  110. F. Vicentini, A. Biella, N. Regnault, and C. Ciuti, Variational neural-network ansatz for steady states in open quantum systems, Phys. Rev. Lett. 122, 250503 (2019).
  111. Y. Huang, Q. Li, X. Hou, R. Wu, M.-H. Yung, A. Bayat, and X. Wang, Robust resource-efficient quantum variational ansatz through an evolutionary algorithm, Phys. Rev. A 105, 052414 (2022).
  112. D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv:1412.6980 [cs.LG].
  113. J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, Barren plateaus in quantum neural network training landscapes, Nat. Commun. 9, 4812 (2018).
  114. Z. Holmes, K. Sharma, M. Cerezo, and P. J. Coles, Connecting ansatz expressibility to gradient magnitudes and barren plateaus, PRX Quantum 3, 010313 (2022).
  115. F. Vatan and C. Williams, Optimal quantum circuits for general two-qubit gates, Phys. Rev. A 69, 032315 (2004).
  116. F. D. M. Haldane, Nonlinear field theory of large-spin Heisenberg antiferromagnets: Semiclassically quantized solitons of the one-dimensional easy-axis Néel state, Phys. Rev. Lett. 50, 1153 (1983).
  117. I. Affleck, T. Kennedy, E. H. Lieb, and H. Tasaki, Rigorous results on valence-bond ground states in antiferromagnets, Phys. Rev. Lett. 59, 799 (1987).
  118. A. Imambekov, M. Lukin, and E. Demler, Spin-exchange interactions of spin-one bosons in optical lattices: Singlet, nematic, and dimerized phases, Phys. Rev. A 68, 063602 (2003).
  119. G. De Chiara, M. Lewenstein, and A. Sanpera, Bilinear-biquadratic spin-1 chain undergoing quadratic Zeeman effect, Phys. Rev. B 84, 054451 (2011).
  120. C. Wu, Hidden symmetry and quantum phases in spin-3/2 cold atomic systems, Mod. Phys. Lett. B 20, 1707 (2006).
  121. Y. Yamashita, N. Shibata, and K. Ueda, SU(4) spin-orbit critical state in one dimension, Phys. Rev. B 58, 9114 (1998).
  122. J. Chen, S. Hu, L. Pan, and X. Wang, Nonuniform quadrupolar orders in the spin-3/2 generalized Heisenberg chain, arXiv:2311.08099.
  123. D. C. Liu and J. Nocedal, On the limited memory BFGS method for large scale optimization, Math. Program. 45, 503 (1989).
  124. J. A. Nelder and R. Mead, A simplex method for function minimization, Comput. J. 7, 308 (1965).
  125. M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, and P. J. Coles, Variational quantum algorithms, Nat. Rev. Phys. 3, 625 (2021).
  126. A. Arrasmith, M. Cerezo, P. Czarnik, L. Cincio, and P. J. Coles, Effect of barren plateaus on gradient-free optimization, Quantum 5, 558 (2021).
  127. X. Xu, J. Cui, Z. Cui, R. He, Q. Li, X. Li, Y. Lin, J. Liu, W. Liu, J. Lu, et al., Mindspore quantum: A user-friendly, high-performance, and AI-compatible quantum computing framework, arXiv:2406.17248.
  128. J. Gray, Quimb: A Python library for quantum information and many-body calculations, J. Open Source Software 3, 819 (2018).
  129. P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al., SciPy 1.0: Fundamental algorithms for scientific computing in Python, Nat. Methods 17, 261 (2020).

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