2013
DOI: 10.1103/physrevlett.110.250504
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Preconditioned Quantum Linear System Algorithm

Abstract: We describe a quantum algorithm that generalizes the quantum linear system algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] to arbitrary problem specifications. We develop a state preparation routine that can initialize generic states, show how simple ancilla measurements can be used to calculate many quantities of interest, and integrate a quantum-compatible preconditioner that greatly expands the number of problems that can achieve exponential speedup over classical linear systems solvers. To … Show more

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Cited by 224 publications
(257 citation statements)
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“…Thus, we have an inequality recurrence for bounding ∆ h : Therefore 16) which shows that the solution error decreases exponentially with n. In other words, the quantum spectral method approximates the solution with error ǫ using n = poly(log(1/ǫ)).…”
Section: Solution Errormentioning
confidence: 95%
See 1 more Smart Citation
“…Thus, we have an inequality recurrence for bounding ∆ h : Therefore 16) which shows that the solution error decreases exponentially with n. In other words, the quantum spectral method approximates the solution with error ǫ using n = poly(log(1/ǫ)).…”
Section: Solution Errormentioning
confidence: 95%
“…Other work has developed quantum algorithms for partial differential equations (PDEs). Reference [16] described a quantum algorithm that applies the QLSA to implement a finite element method for Maxwell's equations. Reference [17] applied Hamiltonian simulation to a finite difference approximation of the wave equation.…”
Section: Introductionmentioning
confidence: 99%
“…In addition, a magnificent combination of machine learning and quantum mechanics has opened a new window for information processing [12][13][14][15][16][17][18][19]. A class of quantum machine learning [19][20][21][22] is based on the Harrow-Hassidim -Lloyd (HHL) algorithm that aims to obtain the inverse of a matrix with exponential speed-up under reasonable conditions [20]. Normally, quantum linear regression has been regarded as a representative task in quantum machine learning and investigated by various all-qubit approaches [13,19,23].…”
Section: Introductionmentioning
confidence: 99%
“…There exists many preconditioning techniques for the classical methods [15,16,29], including AMG, DDM, etc. To the best of our knowledge, there was only one work related to the quantum preconditioning [13]. To improve the efficiency of quantum linear solver, Clader et al [13] chose a sparse approximate inverse (SPAI) preconditioner M .…”
Section: Introductionmentioning
confidence: 99%