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Quantum Algorithm for Linear Systems of Equations
Open accessAbstract
Solving linear systems of equations is a common problem that arises both on its own and as a subroutine in more complex problems: given a matrix $A$ and a vector $\stackrel{\ensuremath{\rightarrow}}{b}$, find a vector $\stackrel{\ensuremath{\rightarrow}}{x}$ such that $A\stackrel{\ensuremath{\rightarrow}}{x}=\stackrel{\ensuremath{\rightarrow}}{b}$. We consider the case where one does not need to know the solution $\stackrel{\ensuremath{\rightarrow}}{x}$ itself, but rather an approximation of the expectation value of some operator associated with $\stackrel{\ensuremath{\rightarrow}}{x}$, e.g., ${\stackrel{\ensuremath{\rightarrow}}{x}}^{\ifmmode\dagger\else\textdagger\fi{}}M\stackrel{\ensuremath{\rightarrow}}{x}$ for some matrix $M$. In this case, when $A$ is sparse, $N\ifmmode\times\else\texttimes\fi{}N$ and has condition number $\ensuremath{\kappa}$, the fastest known classical algorithms can find $\stackrel{\ensuremath{\rightarrow}}{x}$ and estimate ${\stackrel{\ensuremath{\rightarrow}}{x}}^{\ifmmode\dagger\else\textdagger\fi{}}M\stackrel{\ensuremath{\rightarrow}}{x}$ in time scaling roughly as $N\sqrt{\ensuremath{\kappa}}$. Here, we exhibit a quantum algorithm for estimating ${\stackrel{\ensuremath{\rightarrow}}{x}}^{\ifmmode\dagger\else\textdagger\fi{}}M\stackrel{\ensuremath{\rightarrow}}{x}$ whose runtime is a polynomial of $\mathrm{log}(N)$ and $\ensuremath{\kappa}$. Indeed, for small values of $\ensuremath{\kappa}$ [i.e., $\mathrm{poly}\mathrm{log}(N)$], we prove (using some common complexity-theoretic assumptions) that any classical algorithm for this problem generically requires exponentially more time than our quantum algorithm.
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“On the specific applications of this algorithm [75], its use is possible in computational biology, where numeric models can be solved, and also in the domain of machine learning techniques.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [75]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“As proposed by Harrow etal. [75], the quantum algorithm is capable enough to give solutions to definite linear systems at exponentially rapid speed compared to any existing algorithm.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [75]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The classical algorithmic complexity for this task scales polynomially in the dimension N , whereas the now-famous Harrow-Hassidim-Lloyd (HHL) quantum algorithm [3] has a complexity that scales logarithmically in N , with some scaling improvements having been proposed [134-137].”
From Variational quantum algorithms (2021) · cites this paper as [3]
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“In particular, the task of matrix inversion admits an exponential quantum speedup, which could have many applications.”
From Quantum Computing in the NISQ era and beyond (2018) · cites this paper as [40]
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“This quantum algorithm runs in time O (log N ) , an exponential speedup relative to classical matrix inversion (for fixed error and for fixed sparsity and condition number of A ).”
From Quantum Computing in the NISQ era and beyond (2018) · cites this paper as [40]
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“Available pieces of literature reveal the application of quantum algorithms to solve linear [75] and differential equations [76-78], where these algorithms play the role of 'subroutines' in algorithms on data processing.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [75]
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“They proved that even though these traditional sampling ones are quick, any traditional algorithm, in general, needs much more time than their [75] algorithm to complete the identical matrix inversion job.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [75]
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“This group [75] reported that their algorithm was related to the traditional Monte Carlo ones, which are pretty quick and operate with samples of a statistical distribution involving N objects instead of using all the N constituents of distribution.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [75]
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“Recent research trends indicate 'data sets' which are used to define these equations, which can be enormous depending on the specific application [75], where data of the order petabytes could be required to process to get a solution.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [75]
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“However, aside from the caveat that the matrix A must be sparse and well conditioned, note that both the input vector | b 〉 and the output vector | A -1 b 〉 are quantum states.”
From Quantum Computing in the NISQ era and beyond (2018) · cites this paper as [40]
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“Assuming these constraints, x j i can be approximately produced in time polynomial in log N , d and κ . 58,59”
From Quantum algorithms: an overview (2016) · cites this paper as [58]
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“Indeed, one can even show that achieving a similar runtime classically would imply that classical computers could ef fi ciently simulate any polynomial-time quantum computation. 58”
From Quantum algorithms: an overview (2016) · cites this paper as [58]
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“The quantum algorithm of Harrow, Hassidim and Lloyd 58 (HHL) for solving systems of linear equations sidesteps this issue by ' solving ' the equations in a peculiarly quantum sense: given the ability to create the quantum state b j i ¼ P N i ¼ 1 bi i j i , and access to A , the algorithm outputs a state approximately proportional to x j i ¼ P N i ¼ 1 xi i j i .”
From Quantum algorithms: an overview (2016) · cites this paper as [58]
In the citing paper
“Second, by considering more favorable schemes for Hamiltonian decomposition 256,257 , most modern-day simulation algorithms scale in terms of τ max = t ‖ H ‖ max instead of τ , where ‖ H ‖ max is the absolute value of the largest entry of H as a matrix in the computational basis.”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [257]
In the citing paper
“Childs 257 showed that given oracle access to a Hamiltonian with eigenvalues λ , one can construct a unitary quantum walk operator with eigenvalues e ± i arcsin λ .”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [257]
In the citing paper
“Inserting this conditional operation in the quantum phase algorithm and using the improved phaseestimation techniques of ref. 15 yields the eigenvectors and eigenvalues to accuracy by applying the quantum phase algorithm for time t = O( -1 ), and so requires n = O(1/ 3 ) copies of the state ρ.”
From Quantum principal component analysis (2014) · cites this paper as [15]
In the citing paper
“Note that density matrix exponentiation is most effective when some of the eigenvalues of ρ are large.If all of the eigenvalues are of size O(1/d) then we require time t = O(d) to resolve the eigenvalues 15 .”
From Quantum principal component analysis (2014) · cites this paper as [15]
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“The Harrow-Hassidim-Lloyd (HHL) quantum algorithm [16] can solve certain linear systems exponentially faster than any known classical algorithm”
From The prospects of quantum computing in computational molecular biology (2021) · cites this paper as [16]
In the citing paper
“However, the algorithm proposed by Harrow, Hassidim and Lloyd [16] allows the inversion of a matrix in O log N ð Þ within some conditions.”
From The prospects of quantum computing in computational molecular biology (2021) · cites this paper as [16]
In the citing paper
“Reading out the function values from the quantum state |u in general requires exponential sampling, representing a so-called data”
From Solving nonlinear differential equations with differentiable quantum circuits (2021) · cites this paper as [9]
In the citing paper
“The power of HHL-based algorithms relies on amplitude encoding, where a function is represented as a quantum state |u = 2 N k=1 u k |k encoded in complex u k amplitudes of computational basis states |k for N qubits.This allows for compressing large grids into a small qubit register, providing exponential memory advantage.”
From Solving nonlinear differential equations with differentiable quantum circuits (2021) · cites this paper as [9]
In the citing paper
“When two particles for example are sent into a multiport, e.g.multimode waveguide [153,154], multimode fiber [155] or integrated waveguide structures [156], the transmission can be described as a complex bi-partite quantum walk”
From Two-photon interference: the Hong–Ou–Mandel effect (2021) · cites this paper as [154]
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Citations
Cites
Algorithms for quantum computation: discrete logarithms and factoring
Shor, P. W. (2002) · Proceedings 35th Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1994.365700 · Cited by 8,713
A scheme for efficient quantum computation with linear optics
Knill, E., Laflamme, R., & Milburn, G. J. (2001) · Nature · doi:10.1038/35051009 · Cited by 5,880
The Mathematical Theory of Finite Element Methods
Brenner, S. C., & Scott, L. R. (2007) · Texts in applied mathematics · doi:10.1007/978-0-387-75934-0 · Cited by 4,009
Lloyd, S. (1996) · Science · doi:10.1126/science.273.5278.1073 · Cited by 3,088 · cited 2×
“[2] S.”
BackgroundSelf-citationIntroduction to the Theory of Computation
Sipser, M. (1996) · ACM SIGACT News · doi:10.1145/230514.571645 · Cited by 2,886
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
Shewchuk, J. R. (1994) · Cited by 2,193
Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion
Hansen, P. C. (1987) · Medical Entomology and Zoology · Cited by 2,142
A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
Farhi, E., Goldstone, J., Gutmann, S., Lapan, J., Lundgren, A., & Preda, D. (2001) · Science · doi:10.1126/science.1057726 · Cited by 2,056
Quantum amplitude amplification and estimation
Brassard, G., Høyer, P., Mosca, M., & Tapp, A. (2002) · Contemporary Mathematics · doi:10.1090/conm/305/05215 · Cited by 1,680
Cleve, R., EKERT, A. K., Macchiavello, C., & Mosca, M. (1998) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.1998.0164 · Cited by 1,399
Research works printed but not linked: 3
- P. W. Shor, 1994, Proc. of the 35th FOCS
- P. Valiant, 2008, Proc. of the 40th Annual STOC
- L. Sheridan, 2009, J. Math Phys. A, 42, 185302
Cited by
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BackgroundInfluentialMachine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
Self-citationA variational eigenvalue solver on a photonic quantum processor
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., & O’Brien, J. L. (2014) · Nature Communications · doi:10.1038/ncomms5213 · Cited by 4,552
Variational quantum algorithms
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“The classical algorithmic complexity for this task scales polynomially in the dimension N , whereas the now-famous HHL quantum algorithm [4] has a complexity that scales logarithmically in N , with some scaling improvements having been proposed [113– 116].”
BackgroundMethodNoisy intermediate-scale quantum algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 2,231
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Lloyd, S., Mohseni, M., & Rebentrost, P. (2014) · Nature Physics · doi:10.1038/nphys3029 · Cited by 1,385
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Cao, Y., Romero, J., Olson, J., Degroote, M., Johnson, P., Kieferová, M., Kivlichan, I., Menke, T., Peropadre, B., Sawaya, N., et al. (n.d.) · OPUS - Open Publications of UTS Scholars (University of Technology Sydney) · Cited by 1,265
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Quantum algorithms: an overview
Montanaro, A. (2016) · npj Quantum Information · doi:10.1038/npjqi.2015.23 · Cited by 1,071
“e condition number = kA 1kkAk, a parameter measuring the numerical instability of A, should be small. Assuming these constraints, jxican be approximately produced in time polynomial in logN, d, and [47,4]. 9 If dand are small, this is an exponential improvement on standard classical algorithms. Indeed, one can even sh…”
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Quantum information processing with superconducting circuits: a review
Wendin, G. P. (2017) · Reports on Progress in Physics · doi:10.1088/1361-6633/aa7e1a · Cited by 1,022
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Low, G. H., & Chuang, I. L. (2019) · Quantum · doi:10.22331/q-2019-07-12-163 · Cited by 854
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Moll, N., Barkoutsos, P., Bishop, L. S., Chow, J. M., Cross, A., Egger, D. J., Filipp, S., Fuhrer, A., Gambetta, J. M., Ganzhorn, M., et al. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab822 · Cited by 744
Simulating Hamiltonian Dynamics with a Truncated Taylor Series
Berry, D. W., Childs, A. M., Cleve, R., Kothari, R., & Somma, R. D. (2015) · Physical Review Letters · doi:10.1103/physrevlett.114.090502 · Cited by 742
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Low, G. H., & Chuang, I. L. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 714
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Orús, R., Mugel, S., & Lizaso, E. (2019) · Reviews in Physics · doi:10.1016/j.revip.2019.100028 · Cited by 792
CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences
Slotnick, J. P., Khodadoust, A., Alonso, J. J., Darmofal, D., Gropp, W., Lurie, E. A., & Mavriplis, D. J. (2014) · NASA Technical Reports Server (NASA) · Cited by 673
Gilyén, A., Su, Y., Low, G. H., & Wiebe, N. (2019) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3313276.3316366 · Cited by 1,070
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Gyongyosi, L., & Imre, S. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2018.11.002 · Cited by 646
Childs, A. M., Kothari, R., & Somma, R. D. (2017) · SIAM Journal on Computing · doi:10.1137/16m1087072 · Cited by 646
A rigorous and robust quantum speed-up in supervised machine learning
Liu, Y., Arunachalam, S., & Temme, K. (2021) · Nature Physics · doi:10.1038/s41567-021-01287-z · Cited by 649
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Lloyd, S., & Weedbrook, C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.121.040502 · Cited by 576
Self-citationAaronson, S. (2015) · Nature Physics · doi:10.1038/nphys3272 · Cited by 567
Dynamical memristors for higher-complexity neuromorphic computing
Kumar, S., Wang, X., Strachan, J. P., Yang, Y., & Lu, W. D. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00434-z · Cited by 584
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Wiebe, N., Braun, D., & Lloyd, S. (2012) · Physical Review Letters · doi:10.1103/physrevlett.109.050505 · Cited by 528
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Wang, H., Qin, J., Ding, X., Chen, M.-C., Chen, S., You, X., He, Y., Jiang, X. Q., You, L., Wang, Z., et al. (2019) · Physical Review Letters · doi:10.1103/physrevlett.123.250503 · Cited by 524
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Magesan, E., Gambetta, J. M., & Emerson, J. (2012) · Physical Review A · doi:10.1103/physreva.85.042311 · Cited by 464
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Blekos, K., Brand, D., Ceschini, A., Chou, C.-H., Li, R.-H., Pandya, K., & Summer, A. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.03.002 · Cited by 563
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Metadata
- Authors
- Harrow, A. W., Hassidim, A., & Lloyd, S.
- Affiliations
- University of Bristol (GB)
- Massachusetts Institute of Technology (US)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2009-10-07
- Volume
- 103
- Issue
- 15
- Pages
- 150502-150502
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W1492999010
- Semantic Scholar
- ed562f0c86c80f75a8b9ac7344567e8b44c8d643
- Cited by
- 3,325
- References
- 34
- Citations by year
- 2026: 355 · 2025: 553 · 2024: 478 · 2023: 429 · 2022: 332 · 2021: 337 · 2020: 231 · 2019: 181 · 2018: 98 · 2017: 102 · 2016: 53 · 2015: 35 · 2014: 39 · 2013: 33 · 2012: 40
- FWCI
- 23.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physical Review Letters (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- arXiv (Cornell University)
- PIRSA
- Bulletin of the American Physical Society
- Funders
- W. M. Keck Foundation
- Engineering and Physical Sciences Research Council (GR/S82169/01)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum algorithms, systems of linear equations, sparse matrices, condition number, expectation value estimation, quantum advantage
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