- Open Access
Error convergence of quantum linear system solvers
Phys. Rev. A 113, 062436 – Published 12 June, 2026
DOI: https://doi.org/10.1103/rjvs-sj7h
Abstract
The Harrow-Hassidim-Lloyd (HHL) algorithm remains a paradigmatic quantum routine for solving linear systems and a widely used benchmark in theoretical and experimental studies. Several circuit-level variants have been proposed to simplify its implementation, including constructions in which the clock register is factorized via Hadamard gates to reduce circuit depth and entanglement. In this work, we show that this commonly deployed modification can fundamentally alter the asymptotic behavior of the algorithm and may fail to converge in the limit of large clock size due to persistent phase interference effects. This phenomenon is not a finite-size artifact and compromises the correctness guarantees of the factorized-clock implementation. We provide an analytic characterization of the underlying mechanism and demonstrate that a minimal and experimentally feasible modification suffices to restore convergence while preserving the practical advantages of the simplified circuit. Our results clarify an overlooked correctness issue in a widely used HHL implementation pattern and delineate the conditions under which simplified realizations faithfully reproduce the intended algorithmic behavior.
Physics Subject Headings (PhySH)
Article Text
References (86)
- M. Nielsen and I. Chuang, Quantum Computation and Quantum Information: 10th Anniversary Edition (Cambridge University, New York, 2010).
- G. Benenti, G. Casati, D. Rossini, and G. Strini, Principles of Quantum Computation and Information: A Comprehensive Textbook (World Scientific, Singapore, 2019).
- P. W. Shor, in Proceedings of the 35th Annual Symposium on Foundations of Computer Science (SFCS '94) (IEEE Computer Society, Los Alamitos, CA, 1994), pp. 124–134.
- S. Aaronson and A. Arkhipov, in STOC '11: Proceedings of the 43rd Annual ACM Symposium on Theory of Computing (Association for Computing Machinery, New York, 2011), pp. 333–342.
- S. Aaronson and A. Arkhipov, The computational complexity of linear optics, Theory Comput. 9, 143 (2013).
- J. Watrous, in Encyclopedia of Complexity and Systems Science, edited by R. A. Meyers (Springer, New York, 2009), pp. 7174–7201.
- P. Arnault, P. Arrighi, S. Herbert, E. Kasnetsi, and T. Li, A typology of quantum algorithms, arXiv:2407.05178.
- S. Sagiroglu and D. Sinanc, in Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS) (IEEE, 2013), p. 42.
- J. Fan, F. Han, and H. Liu, Challenges of big data analysis, Natl. Sci. Rev. 1, 293 (2014).
- A. W. Harrow, A. Hassidim, and S. Lloyd, Quantum algorithm for linear systems of equations, Phys. Rev. Lett. 103, 150502 (2009).
- M. Morales, L. Pira, P. Schleich, K. Koor, P. Costa, D. An, A. Aspuru-Guzik, L. Lin, P. Rebentrost, and D. Berry, Quantum linear system solvers: A survey of algorithms and applications, arXiv:2411.02522.
- P. C. S. Costa, D. An, R. Babbush, and D. Berry, The discrete adiabatic quantum linear system solver has lower constant factors than the randomized adiabatic solver, Quantum 9, 1887 (2025).
- P. C. S. Costa, D. An, Y. R. Sanders, Y. Su, R. Babbush, and D. W. Berry, Optimal scaling quantum linear-systems solver via discrete adiabatic theorem, PRX Quantum 3, 040303 (2022).
- A. M. Dalzell, A shortcut to an optimal quantum linear system solver, arXiv:2406.12086.
- A. Gilyén, Y. Su, G. H. Low, and N. Wiebe, in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, STOC 2019 (Association for Computing Machinery, New York, 2019), p. 193.
- J. M. Martyn, Z. M. Rossi, A. K. Tan, and I. L. Chuang, Grand unification of quantum algorithms, PRX Quantum 2, 040203 (2021).
- 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).
- A. Setty, Block encoding of sparse matrices via coherent permutation, arXiv:2508.21667.
- J. Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018).
- K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, W.-K. Mok, S. Sim, L.-C. Kwek, and A. Aspuru-Guzik, Noisy intermediate-scale quantum algorithms, Rev. Mod. Phys. 94, 015004 (2022).
- A. Y. Kitaev, Quantum measurements and the Abelian stabilizer problem, arXiv:quant-ph/9511026.
- R. Cleve, A. Ekert, C. Macchiavello, and M. Mosca, Quantum algorithms revisited, Proc. R. Soc. A 454, 339 (1998).
- D. S. Abrams and S. Lloyd, Quantum algorithm providing exponential speed increase for finding eigenvalues and eigenvectors, Phys. Rev. Lett. 83, 5162 (1999).
- G. Brassard, P. Hoyer, M. Mosca, and A. Tapp, Quantum amplitude amplification and estimation Contemp. Math. 305, 53 (2002).
- L. K. Grover, in Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing - STOC '96 (Association for Computing Machinery, New York, 1993), p. 212.
- L. K. Grover, Quantum mechanics helps in searching for a needle in a haystack, Phys. Rev. Lett. 79, 325 (1997).
- M. Boyer, G. Brassard, P. Høyer, and A. Tapp, Tight bounds on quantum searching, Fortschr. Phys. 46, 493 (1998).
- C. Cade, M. Folkertsma, S. Gharibian, R. Hayakawa, F. Le Gall, T. Morimae, and J. Weggemans, in Proceedings of the 50th EATCS International Colloquium on Automata, Languages and Programming (ICALP 2023) (Schloss Dagstuhl, Leibniz-Zentrum für Informatik, 2023), Vol. 9, pp. 32:1–32:19.
- Y. Cao, A. Daskin, S. Frankel, and S. Kais, Quantum circuit design for solving linear systems of equations, Mol. Phys. 110, 1675 (2012).
- S. Barz, I. Kassal, M. Ringbauer, Y. O. Lipp, B. Dakic, A. Aspuru-Guzik, and P. Walther, A two-qubit photonic quantum processor and its application to solving systems of linear equations, Sci. Rep. 4, 6115 (2014).
- D. Dervovic, M. Herbster, P. Mountney, S. Severini, N. Usher, and L. Wossnig, Quantum linear systems algorithms: A primer, arXiv:1802.08227.
- B. Duan, J. Yuan, C.-H. Yu, J. Huang, and C.-Y. Hsieh, A survey on HHL algorithm: From theory to application in quantum machine learning, Phys. Lett. A 384, 126595 (2020).
- J. Pan, Y. Cao, X. Yao, Z. Li, C. Ju, H. Chen, X. Peng, S. Kais, and J. Du, Experimental realization of quantum algorithm for solving linear systems of equations, Phys. Rev. A 89, 022313 (2014).
- C. Lanczos, An iteration method for the solution of the eigenvalue problem of linear differential and integral operators, J. Res. Natl. Bur. Stand. 45, 255 (1950).
- N. J. Higham, FORTRAN codes for estimating the one-norm of a real or complex matrix, with applications to condition estimation, ACM Trans. Math. Softw. 14, 381 (1988).
- S.-C. T. Choi, C. C. Paige, and M. A. Saunders, MINRES-QLP: A Krylov subspace method for indefinite or singular symmetric systems, SIAM J. Sci. Comput. 33, 1810 (2011).
- H. Avron, A. Druinsky, and S. Toledo, Spectral condition-number estimation of large sparse matrices, Numer. Linear Algebra Appl. 26, e2235 (2019).
- D. W. Berry, G. Ahokas, R. Cleve, and B. C. Sanders, Efficient quantum algorithms for simulating sparse Hamiltonians, Commun. Math. Phys. 270, 359 (2007).
- B. D. W. and C. A. M., Quantum information and computation Quantum Inf. Comput. 12, 29 (2012).
- D. W. Berry, R. Cleve, and S. Gharibian, Gate-efficient discrete simulations of continuous-time quantum query algorithms, arXiv:1211.4637.
- D. W. Berry, A. M. Childs, R. Cleve, R. Kothari, and R. D. Somma, in STOC '14: Proceedings of the 46th ACM Symposium on Theory of Computing (Association for Computing Machinery, New York, 2014), p. 283.
- D. W. Berry, A. M. Childs, R. Cleve, R. Kothari, and R. D. Somma, Simulating Hamiltonian dynamics with a truncated Taylor series, Phys. Rev. Lett. 114, 090502 (2015).
- D. W. Berry, A. M. Childs, and R. Kothari, in Proceedings of the 56th IEEE Symposium on Foundations of Computer Science (FOCS 2015) (IEEE, New York, 2015), p. 792.
- D. Berry and L. Novo, Corrected quantum walk for optimal Hamiltonian simulation, Quant. Inf. Comp. 16, 1295 (2016).
- D. Berry and L. Novo, Improved Hamiltonian simulation via a truncated Taylor series and corrections, Quant. Inf. Comp. 17, 623 (2017).
- G. H. Low and I. L. Chuang, Optimal Hamiltonian simulation by quantum signal processing, Phys. Rev. Lett. 118, 010501 (2017).
- G. H. Low and I. L. Chuang, Hamiltonian simulation by qubitization, Quantum 3, 163 (2019).
- S. Lloyd, Universal quantum simulators, Science 273, 1073 (1996).
- D. Porras and J. I. Cirac, Effective quantum spin systems with trapped ions, Phys. Rev. Lett. 92, 207901 (2004).
- I. M. Georgescu, S. Ashhab, and F. Nori, Quantum simulation, Rev. Mod. Phys. 86, 153 (2014).
- C. Monroe, W. C. Campbell, L.-M. Duan, Z.-X. Gong, A. V. Gorshkov, P. W. Hess, R. Islam, K. Kim, N. M. Linke, G. Pagano, P. Richerme, C. Senko, and N. Y. Yao, Programmable quantum simulations of spin systems with trapped ions, Rev. Mod. Phys. 93, 025001 (2021).
- in Quantum State Estimation, edited by M. Paris and J. Řeháček, Lecture Notes in Physics Vol. 649 (Springer-Verlag, Berlin, 2004).
- D. Gross, Y.-K. Liu, S. T. Flammia, S. Becker, and J. Eisert, Quantum state tomography via compressed sensing, Phys. Rev. Lett. 105, 150401 (2010).
- D. Gross, Recovering low-rank matrices from few coefficients in any basis, IEEE Trans. Inf. Theory 57, 1548 (2011).
- S. T. Flammia, D. Gross, Y.-K. Liu, and J. Eisert, Quantum tomography via compressed sensing: Error bounds, sample complexity and efficient estimators, New J. Phys. 14, 095022 (2012).
- H. J. J. Morrell, A. Zaman, and H. Y. Wong, A step-by-step HHL algorithm walkthrough to enhance understanding of critical quantum computing concepts, IEEE Access 11, 77117 (2023).
- P. Kaye and M. Mosca, International Conference on Quantum Information (Optica, Washington, DC, 2001), p. PB28.
- L. Grover and T. Rudolph, Creating superpositions that correspond to efficiently integrable probability distributions, arXiv:quant-ph/0208112.
- L. K. Grover, Synthesis of quantum superpositions by quantum computation, Phys. Rev. Lett. 85, 1334 (2000).
- M. Möttönen, J. J. Vartiainen, V. Bergholm, and M. M. Salomaa, Transformation of quantum states using uniformly controlled rotations, Quantum Info. Comput. 5, 467 (2005).
- A. N. Soklakov and R. Schack, Efficient state preparation for a register of quantum bits, Phys. Rev. A 73, 012307 (2006).
- M. Plesch and Č. Brukner, Quantum-state preparation with universal gate decompositions, Phys. Rev. A 83, 032302 (2011).
- I. F. Araujo, D. K. Park, F. Petruccione, and A. J. da Silva, A divide-and-conquer algorithm for quantum state preparation, Sci. Rep. 11, 6329 (2021).
- X.-M. Zhang, M.-H. Yung, and X. Yuan, Low-depth quantum state preparation, Phys. Rev. Res. 3, 043200 (2021).
- S. Wang, Z. Wang, G. Cui, S. Shi, R. Shang, L. Fan, W. Li, Z. Wei, and Y. Gu, Fast black-box quantum state preparation based on linear combination of unitaries, Quantum Info. Proc. 20, 270 (2021).
- J. Bausch, Fast black-box quantum state preparation, Quantum 6, 773 (2022).
- B. D. Clader, A. M. Dalzell, N. Stamatopoulos, G. Salton, M. Berta, and W. J. Zeng, Quantum resources required to block-encode a matrix of classical data, IEEE Trans. Quantum Eng. 3, 1 (2022).
- G. H. Low, V. Kliuchnikov, and L. Schaeffer, Trading T gates for dirty qubits in state preparation and unitary synthesis, Quantum 8, 1375 (2024).
- A. Luis and J. Peřina, Optimum phase-shift estimation and the quantum description of the phase difference, Phys. Rev. A 54, 4564 (1996).
- V. Bužek, R. Derka, and S. Massar, Optimal quantum clocks, Phys. Rev. Lett. 82, 2207 (1999).
- D. W. Berry and H. M. Wiseman, Optimal states and almost optimal adaptive measurements for quantum interferometry, Phys. Rev. Lett. 85, 5098 (2000).
- A. E. Rastegin, Relative error of state-dependent cloning, Phys. Rev. A 66, 042304 (2002).
- A. E. Rastegin, Sine distance for quantum states, arXiv:quant-ph/0602112.
- T. W. Anderson, Asymptotic theory of certain “goodness of fit” criteria based on stochastic processes, Ann. Math. Stat. 23, 193 (1952).
- W. Hoeffding, Probability inequalities for sums of bounded random variables, J. Am. Stat. Assoc. 58, 13 (1963).
- The spectral decomposition of is , and, using the Cauchy-Schwarz inequality, .
- NIST Handbook of Mathematical Functions, edited by D. M. Lozier, R. F. Boisvert, and C. W. Clark (Cambridge University, New York, 2010).
- V. Giovannetti, S. Lloyd, and L. Maccone, Quantum random access memory, Phys. Rev. Lett. 100, 160501 (2008).
- X.-M. Zhang, T. Li, and X. Yuan, Quantum state preparation with optimal circuit depth: Implementations and applications, Phys. Rev. Lett. 129, 230504 (2022).
- X.-M. Zhang and X. Yuan, Circuit complexity of quantum access models for encoding classical data, npj Quantum Inf. 10, 42 (2024).
- X.-Q. Zhou, T. C. Ralph, P. Kalasuwan, M. Zhang, A. Peruzzo, B. P. Lanyon, and J. L. O'Brien, Adding control to arbitrary unknown quantum operations, Nat. Commun. 2, 413 (2011).
- X.-Q. Zhou, P. Kalasuwan, T. C. Ralph, and J. L. O'Brien, Calculating unknown eigenvalues with a quantum algorithm, Nat. Photon. 7, 223 (2013).
- N. Friis, V. Dunjko, W. Dür, and H. J. Briegel, Implementing quantum control for unknown subroutines, Phys. Rev. A 89, 030303(R) (2014).
- https://github.com/MatiasGinzburg/Improvement-on-the-Convergence-in-Quantum-Linear-System-Solvers.
- F. Mezzadri, How to generate random matrices from the classical compact groups, arXiv:math-ph/0609050.
- A. Barenco, C. H. Bennett, R. Cleve, D. P. DiVincenzo, N. Margolus, P. Shor, T. Sleator, J. A. Smolin, and H. Weinfurter, Elementary gates for quantum computation, Phys. Rev. A 52, 3457 (1995).