- Open Access
Quantum algorithm for one-quasiparticle excitations in the thermodynamic limit via cluster-additive block diagonalization
Phys. Rev. Research 8, 023358 – Published 30 June, 2026
DOI: https://doi.org/10.1103/sdpn-s42c
Abstract
We propose a quantum algorithm for computing one-quasiparticle excitation energies in the thermodynamic limit by combining numerical linked-cluster expansions (NLCEs) and the variational quantum eigensolver (VQE). Our approach uses VQE to block diagonalize the cluster Hamiltonian through a single-unitary transformation. This unitary is then postprocessed using the projective cluster-additive transformation (PCAT) to ensure cluster additivity, a key requirement for NLCE convergence. We benchmark our method on the transverse-field Ising model (TFIM) in one and two dimensions, and with longitudinal field, computing one-quasiparticle dispersions in the high-field polarized phase. We compare two cost function classes, trace minimization and variance based, demonstrating their effectiveness with the Hamiltonian variational ansatz (HVA). For pure TFIM, layers of HVA suffice: matches exact diagonalization. For TFIM with longitudinal field, where parity symmetry breaks and PCAT becomes essential, both and layers of HVA converge with increasing cluster size, with layers providing improved accuracy. Our results establish PCAT as a cluster-additive framework that extends variational quantum algorithms to excited-state calculations in the thermodynamic limit via NLCE. While demonstrated with VQE, the PCAT postprocessing approach, which requires only low-energy eigenspace information, applies to any quantum eigenstate preparation method.
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References (52)
- A. Peruzzo, J. McClean, P. Shadbolt, M. H. Yung, X. Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’Brien, A variational eigenvalue solver on a photonic quantum processor, Nat. Commun. 5, 4213 (2014).
- J. Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018).
- P. J. J. O’Malley et al., Scalable quantum simulation of molecular energies, Phys. Rev. X 6, 031007 (2016).
- 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).
- 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).
- 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).
- J. Kattemölle and J. van Wezel, Variational quantum eigensolver for the Heisenberg antiferromagnet on the kagome lattice, Phys. Rev. B 106, 214429 (2022).
- A. C. Y. Li, M. S. Alam, T. Iadecola, A. Jahin, J. Job, D. M. Kurkcuoglu, R. Li, P. P. Orth, A. B. Özgüler, G. N. Perdue, and N. M. Tubman, Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model, Phys. Rev. Res. 5, 033071 (2023).
- R. Watanabe, K. Fujii, and H. Ueda, Variational quantum eigensolver with embedded entanglement using a tensor-network ansatz, Phys. Rev. Res. 6, 023009 (2024).
- J. Wang and R. Jaiswal, Scalable quantum ground state preparation of the Heisenberg model: A variational quantum eigensolver approach, arXiv:2308.12020.
- Sumeet, M. Hörmann, and K. P. Schmidt, Hybrid quantum-classical algorithm for the transverse-field Ising model in the thermodynamic limit, Phys. Rev. B 110, 155128 (2024).
- 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).
- Z. Holmes, K. Sharma, M. Cerezo, and P. J. Coles, Connecting ansatz expressibility to gradient magnitudes and barren plateaus, PRX Quantum 3, 010313 (2022).
- O. Higgott, D. Wang, and S. Brierley, Variational quantum computation of excited states, Quantum 3, 156 (2019).
- P. J. Ollitrault, A. Kandala, C. F. Chen, P. K. Barkoutsos, A. Mezzacapo, M. Pistoia, S. Sheldon, S. Woerner, J. M. Gambetta, and I. Tavernelli, Quantum equation of motion for computing molecular excitation energies on a noisy quantum processor, Phys. Rev. Res. 2, 043140 (2020).
- K. M. Nakanishi, K. Mitarai, and K. Fujii, Subspace-search variational quantum eigensolver for excited states, Phys. Rev. Res. 1, 033062 (2019).
- C. L. Benavides-Riveros, L. Chen, C. Schilling, S. Mantilla, and S. Pittalis, Excitations of quantum many-body systems via purified ensembles: A unitary-coupled-cluster-based approach, Phys. Rev. Lett. 129, 066401 (2022).
- Y. Guo, T. Angelides, K. Jansen, and S. Kühn, Concurrent VQE for simulating excited states of the Schwinger model, arXiv:2407.15629.
- S. Bravyi, D. P. DiVincenzo, and D. Loss, Schrieffer-Wolff transformation for quantum many-body systems, Ann. Phys. 326, 2793 (2011).
- F. Wegner, Flow-equations for Hamiltonians, Ann. Phys. 506, 77 (1994).
- S. D. Głazek and K. G. Wilson, Renormalization of Hamiltonians, Phys. Rev. D 48, 5863 (1993).
- C. Knetter and G. S. Uhrig, Perturbation theory by flow equations: Dimerized and frustrated = 1/2 chain, Eur. Phys. J. B 13, 209 (2000).
- C. J. Morningstar and M. Weinstein, Contractor renormalization group technology and exact Hamiltonian real-space renormalization group transformations, Phys. Rev. D 54, 4131 (1996).
- J. Oitmaa, C. Hamer, and W. Zheng, Series Expansion Methods for Strongly Interacting Lattice Models (Cambridge University Press, Cambridge, England, 2006).
- M. P. Gelfand, Series expansions for excited states of quantum lattice models, Solid State Commun. 98, 11 (1996).
- A. C. Irving and C. J. Hamer, Linked cluster expansions for U(1) lattice gauge theory in 2 + 1 and 3 + 1 dimensions, Nucl. Phys. B 235, 358 (1984).
- B. Tang, E. Khatami, and M. Rigol, A short introduction to numerical linked-cluster expansions, Comput. Phys. Commun. 184, 557 (2013).
- M. Rigol, T. Bryant, and R. R. P. Singh, Numerical linked-cluster approach to quantum lattice models, Phys. Rev. Lett. 97, 187202 (2006).
- K. Coester, S. Clever, F. Herbst, S. Capponi, and K. P. Schmidt, A generalized perspective on non-perturbative linked-cluster expansions, Europhys. Lett. 110, 20006 (2015).
- R. Jaiswal, I. Lovas, and L. Balents, Simulating a quasiparticle on a quantum device, Phys. Rev. A 111, 012629 (2025).
- M. Hörmann and K. P. Schmidt, Projective cluster-additive transformation for quantum lattice models, SciPost Phys. 15, 097 (2023).
- C. Knetter, K. P. Schmidt, and G. S. Uhrig, The structure of operators in effective particle-conserving models, J. Phys. A: Math. Gen. 36, 7889 (2003).
- D. Wecker, M. B. Hastings, and M. Troyer, Progress towards practical quantum variational algorithms, Phys. Rev. A 92, 042303 (2015).
- L. S. Cederbaum, J. Schirmer, and H. D. Meyer, Block diagonalisation of Hermitian matrices, J. Phys. A: Math. Gen. 22, 2427 (1989).
- I. G. Enting, Series expansions from the finite lattice method, Nucl. Phys. B Proc. Suppl. 47, 180 (1996).
- J. des Cloizeaux, Extension d’une formule de Lagrange à des problèmes de valeurs propres, Nucl. Phys. 20, 321 (1960).
- M. Takahashi, Half-filled Hubbard model at low temperature, J. Phys. C 10, 1289 (1977).
- I. Shavitt and L. T. Redmon, Quasidegenerate perturbation theories: A canonical van Vleck formalism and its relationship to other approaches, J. Chem. Phys. 73, 5711 (1980).
- T. Kato, On the convergence of the perturbation method. I, Prog. Theor. Phys. 4, 514 (1949).
- A. Kardashin, A. Uvarov, D. Yudin, and J. Biamonte, Certified variational quantum algorithms for eigenstate preparation, Phys. Rev. A 102, 052610 (2020).
- D. B. Zhang, B. L. Chen, Z. H. Yuan, and T. Yin, Variational quantum eigensolvers by variance minimization, Chin. Phys. B 31, 120301 (2022).
- E. Farhi, J. Goldstone, and S. Gutmann, A quantum approximate optimization algorithm, arXiv:1411.4028.
- E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, Quantum computation by adiabatic evolution, arXiv:quant-ph/0001106.
- R. Wang, T. H. Hsieh, and G. Vidal, Bang-bang algorithms for quantum many-body ground states: A tensor network exploration, Phys. Rev. B 106, 195133 (2022).
- B. Anselme Martin, P. Simon, and M. J. Rančić, Simulating strongly interacting Hubbard chains with the variational Hamiltonian ansatz on a quantum computer, Phys. Rev. Res. 4, 023190 (2022).
- A. A. Mele, G. B. Mbeng, G. E. Santoro, M. Collura, and P. Torta, Avoiding barren plateaus via transferability of smooth solutions in a Hamiltonian variational ansatz, Phys. Rev. A 106, L060401 (2022).
- C. Y. Park and N. Killoran, Hamiltonian variational ansatz without barren plateaus, Quantum 8, 1239 (2024).
- M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, 10th anniversary ed. (Cambridge University Press, Cambridge, England, 2010).
- P. Pfeuty, The one-dimensional Ising model with a transverse field, Ann. Phys. 57, 79 (1970).
- H. X. He, C. J. Hamer, and J. Oitmaa, High-temperature series expansions for the (2+1)-dimensional Ising model, J. Phys. A: Math. Gen. 23, 1775 (1990).
- S. Hesselmann and S. Wessel, Thermal Ising transitions in the vicinity of two-dimensional quantum critical points, Phys. Rev. B 93, 155157 (2016).
- Sumeet, M. Hörmann, and K. P. Schmidt, Data for Quantum algorithm for one-quasiparticle excitations in the thermodynamic limit via cluster-additive block diagonalization, Zenodo, 2026, doi:10.5281/zenodo.20775920.