Quantum algorithm for linear systems of equations

The quantum algorithm for linear systems of equations, also called HHL algorithm, designed by Aram Harrow, Avinatan Hassidim, and Seth Lloyd, is a quantum algorithm published in 2008 for solving linear systems. The algorithm estimates the result of a scalar measurement on the solution vector to a given linear system of equations. Due to the prevalence of linear systems in virtually all areas of science and engineering, the quantum algorithm for linear systems of equations has the potential for widespread applicability.

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enThe quantum algorithm for linear systems of equations, also called HHL algorithm, designed by Aram Harrow, Avinatan Hassidim, and Seth Lloyd, is a quantum algorithm published in 2008 for solving linear systems. The algorithm estimates the result of a scalar measurement on the solution vector to a given linear system of equations. Due to the prevalence of linear systems in virtually all areas of science and engineering, the quantum algorithm for linear systems of equations has the potential for widespread applicability.
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enThe quantum algorithm for linear systems of equations, also called HHL algorithm, designed by Aram Harrow, Avinatan Hassidim, and Seth Lloyd, is a quantum algorithm published in 2008 for solving linear systems. The algorithm estimates the result of a scalar measurement on the solution vector to a given linear system of equations. The algorithm is one of the main fundamental algorithms expected to provide a speedup over their classical counterparts, along with Shor's factoring algorithm, Grover's search algorithm, and the quantum fourier transform. Provided the linear system is sparse and has a low condition number , and that the user is interested in the result of a scalar measurement on the solution vector, instead of the values of the solution vector itself, then the algorithm has a runtime of , where is the number of variables in the linear system. This offers an exponential speedup over the fastest classical algorithm, which runs in (or for positive semidefinite matrices). An implementation of the quantum algorithm for linear systems of equations was first demonstrated in 2013 by Cai et al., Barz et al. and Pan et al. in parallel. The demonstrations consisted of simple linear equations on specially designed quantum devices. The first demonstration of a general-purpose version of the algorithm appeared in 2018 in the work of Zhao et al. Due to the prevalence of linear systems in virtually all areas of science and engineering, the quantum algorithm for linear systems of equations has the potential for widespread applicability.
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Quantum algorithm for linear systems of equations
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Amplitude amplification
Aram Harrow
Big data
Black-Scholes
Born rule
BQP
Category:Articles containing proofs
Category:Integer factorization algorithms
Category:Quantum algorithms
Cloud-based quantum computing
Computational finance
Condition number
Conjugate gradient method
Conjugate Gradient method
Differentiable programming
Finite Element Method
Gaussian elimination
Grover's algorithm
Hamiltonian simulation
Hermitian matrix
Least-squares fit
Machine learning
Phase estimation
Preconditioner
PSPACE
Quantum algorithm
Quantum fourier transform
Quantum phase estimation
Quantum state
Radar cross-section
Seth Lloyd
Shor's Algorithm
Singular values
Sparse matrix
Stefanie Barz
System of linear equations
Unitary operator
Unit vector
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Algoritmo cuántico para sistemas de ecuaciones lineales
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m.010hlhnp
Q17083575
Quantum algorithm for linear systems of equations
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Category:Articles containing proofs
Category:Integer factorization algorithms
Category:Quantum algorithms
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