ToQ.jl: A high-level programming language for D-Wave machines based on Julia

O’Malley, Daniel; Vesselinov, Velimir V.

doi:10.1109/hpec.2016.7761616

Cited by 31 publications

(38 citation statements)

References 5 publications

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“…Finally, just like O'Malley and Vesselinov mentioned in their papers [15,16], quantum annealing also has potential in specific areas like the Binary [20] and box-constrained integer least squares [5] where classical methods struggle.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…The technique of using quantum annealing for solving linear least squares problems for real numbers was created by O'Malley and Vesselinov [15]. In that work, they discovered that because the time complexity is in the order of O(mn 2 ), which is the same time complexity class as the methods mentioned above, the approach might be best suited for binary and sparse least squares problem.…”

Section: Related Workmentioning

confidence: 99%

“…The values of o and p are the user defined lower and upper limits of the interval. In the work by O'Malley and Vesselinov [15], the radix 2 representation of x j is given by…”

Section: Quantum Annealing For Linear Least Squaresmentioning

confidence: 99%

“…In 2016, O'Malley and Vesselinov [15] briefly explored using the D-wave Quantum Annealer for the linear least squares problem for binary and real numbers. In this paper, we shall study their approach in more detail.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing the Quantum Annealing Approach for Solving Linear Least Squares Problems

Borle

Lomonaco

2018

WALCOM: Algorithms and Computation

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With the advent of quantum computers, researchers are exploring if quantum mechanics can be leveraged to solve important problems in ways that may provide advantages not possible with conventional or classical methods. A previous work by O'Malley and Vesselinov in 2016 briefly explored using a quantum annealing machine for solving linear least squares problems for real numbers. They suggested that it is best suited for binary and sparse versions of the problem. In our work, we propose a more compact way to represent variables using two's and one's complement on a quantum annealer. We then do an in-depth theoretical analysis of this approach, showing the conditions for which this method may be able to outperform the traditional classical methods for solving general linear least squares problems. Finally, based on our analysis and observations, we discuss potentially promising areas of further research where quantum annealing can be especially beneficial.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The values of o and p are the user defined lower and upper limits of the interval. In the work by O'Malley and Vesselinov [15], the radix 2 representation of x j is given by…”

Section: Quantum Annealing For Linear Least Squaresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyzing the Quantum Annealing Approach for Solving Linear Least Squares Problems

Borle

Lomonaco

2018

WALCOM: Algorithms and Computation

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“…Deriving the QUBO formulation of the unfolding problem To rewrite the unfolding equation, we follow the approach described in Ref. [21]. First, we replace the Poisson term with a sum of squares of differences, i.e.…”

Section: Appendixmentioning

confidence: 99%

Unfolding measurement distributions via quantum annealing

Cormier

Sipio

Wittek

2019

J. High Energ. Phys.

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High-energy physics is replete with hard computational problems and it is one of the areas where quantum computing could be used to speed up calculations. We present an implementation of likelihood-based regularized unfolding on a quantum computer. The inverse problem is recast in terms of quadratic unconstrained binary optimization (QUBO), which has the same form of the Ising hamiltonian and hence it is solvable on a programmable quantum annealer. We tested the method using a model that captures the essence of the problem, and compared the results with a baseline method commonly used in precision measurements at the Large Hadron Collider (LHC) at CERN. The unfolded distribution is in very good agreement with the original one. We also show how the method can be extended to include the effect of nuisance parameters representing sources of systematic uncertainties affecting the measurement.

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The $$\langle $$Im|Possibility$$\rangle $$ of Quantum Annealing for Maximum Likelihood Estimation

Yoon

2022

Credible Asset Allocation, Optimal Transport Methods, and Related Topics

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ToQ.jl: A high-level programming language for D-Wave machines based on Julia

Cited by 31 publications

References 5 publications

Analyzing the Quantum Annealing Approach for Solving Linear Least Squares Problems

Analyzing the Quantum Annealing Approach for Solving Linear Least Squares Problems

Unfolding measurement distributions via quantum annealing

The $$\langle $$Im|Possibility$$\rangle $$ of Quantum Annealing for Maximum Likelihood Estimation

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