Boosting quantum annealing performance through direct polynomial unconstrained binary optimization | Paper Citation Record

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This paper cites Thus, one needs to find an equiva- lent encoding as a QUBO problem to solve 3-SAT on such hardware.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Thus, one needs to find an equiva- lent encoding as a QUBO problem to solve 3-SAT on such hardware

Reference 2

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This paper cites In principle, it is desirable forJ to be as large as possible in order to speed up the absolute computing time.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization In principle, it is desirable forJ to be as large as possible in order to speed up the absolute computing time

Reference 3

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This paper cites Typical gap profiles of PUBO and QUBO formulations of randomly generated 3-SAT instances.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Typical gap profiles of PUBO and QUBO formulations of randomly generated 3-SAT instances

Reference 5

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Observation 35dd532c-829f-4a2a-a393-fc7a18a5e541 · outbound

This paper cites Finding an optimal QUBO reduction for a given PUBO problem is in generalNP-hard.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Finding an optimal QUBO reduction for a given PUBO problem is in generalNP-hard

Reference 6

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This paper cites Figure 2 investigates the correlations between these two quantities for10 000random instances generated via toughSAT with N = 6 and M = round(4.24N) = 25.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Figure 2 investigates the correlations between these two quantities for10 000random instances generated via toughSAT with N = 6 and M = round(4.24N) = 25

Reference 8

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Reference 9

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This paper cites In this section, we in- vestigate how varying this strength affects the minimum energy gap.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization In this section, we in- vestigate how varying this strength affects the minimum energy gap

Reference 10

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This paper cites However, hx is actually a free parameter that can be tuned to optimize the annealing schedule.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization However, hx is actually a free parameter that can be tuned to optimize the annealing schedule

Reference 11

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This paper cites (b) Each CNOT gate can be further decomposed into one RZZ gate of fixed angle and multiple single-qubit rotations.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization (b) Each CNOT gate can be further decomposed into one RZZ gate of fixed angle and multiple single-qubit rotations

Reference 12

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Reference 13

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Reference 14

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This paper cites Martoňák, G.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Martoňák, G

Reference 15

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This paper cites Hauke, H.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Hauke, H

Reference 16

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This paper cites Rajak, S.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Rajak, S

Reference 17

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This paper cites Yarkoni, E.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Yarkoni, E

Reference 18

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Reference 20

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This paper cites Micheletti, P.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Micheletti, P

Reference 21

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This paper cites Slongo, P.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Slongo, P

Reference 22

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This paper cites Outeiral, G.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Outeiral, G

Reference 23

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This paper cites Polynomial unconstrained binary optimisation inspired by optical simulation.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Polynomial unconstrained binary optimisation inspired by optical simulation

Reference 24

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This paper cites Polynomial Reduction Methods and their Impact on QAOA Circuits.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Polynomial Reduction Methods and their Impact on QAOA Circuits

Reference 25

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This paper cites Babbush, B.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Babbush, B

Reference 27

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This paper cites Stein, F.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Stein, F

Reference 28

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This paper cites Hsieh, R.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Hsieh, R

Reference 29

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Reference 30

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This paper cites Glover, G.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Glover, G

Reference 31

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Reference 32

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This paper cites Jansen, M.-B.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Jansen, M.-B

Reference 33

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Reference 34

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Reference 35

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This paper cites Cheung, P.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Cheung, P

Reference 36

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This paper cites Čepait˙ e, A.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Čepait˙ e, A

Reference 37

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This paper cites Tighter Lower Bounds on Quantum Annealing Times.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Tighter Lower Bounds on Quantum Annealing Times

Reference 38

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This paper cites Bottarelli, M.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Bottarelli, M

Reference 39

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This paper cites Altshuler, H.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Altshuler, H

Reference 40

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This paper cites A Quantum Approximate Optimization Algorithm.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization A Quantum Approximate Optimization Algorithm

Reference 41

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Reference 42

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This paper cites Blanes, F.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Blanes, F

Reference 43

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This paper cites Boros and P.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Boros and P

Reference 44

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This paper cites Alleviating the quantum Big-$M$ problem.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Alleviating the quantum Big-$M$ problem

Reference 45

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This paper cites Aspvall, M.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Aspvall, M

Reference 46

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Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Unresolved cited work

Reference 47

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Reference 48

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Reference 49

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Reference 50

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This paper cites Albash and D.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Albash and D

Reference 51

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This paper cites Willsch, M.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Willsch, M

Reference 52

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This paper cites Bernaschi, I.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Bernaschi, I

Reference 53

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This paper cites Mézard, G.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Mézard, G

Reference 54

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This paper cites Žnidarič, Scaling of the running time of the quantum adiabatic algorithm for propositional satisfiability, Phys.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Žnidarič, Scaling of the running time of the quantum adiabatic algorithm for propositional satisfiability, Phys

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Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Unresolved cited work

Reference 56

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This paper cites Efficient QUBO transformation for Higher Degree Pseudo Boolean Functions.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Efficient QUBO transformation for Higher Degree Pseudo Boolean Functions

Reference 57

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Reference 58

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Observation 14d7a344-dde4-4066-86fa-3046fdc852e8 · outbound

This paper cites Bose, Exponential speed-up of quantum annealing via n-local catalysts (2024), arXiv:2409.13029 [quant-ph].

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Bose, Exponential speed-up of quantum annealing via n-local catalysts (2024), arXiv:2409.13029 [quant-ph]

Reference 59

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This paper cites Bermudez, D.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Bermudez, D

Reference 60

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Reference 61

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This paper cites Andrade, Z.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Andrade, Z

Reference 62

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This paper cites Nagies, K.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Nagies, K

Reference 63

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Reference 64

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This paper cites Decker, C.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Decker, C

Reference 65

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This paper cites Chancellor, S.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Chancellor, S

Reference 66

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Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Rakyta and Z

Reference 67

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This paper cites Madden and A.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Madden and A

Reference 68

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This paper cites Orús and J.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Orús and J

Reference 69

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This paper cites Lanting, A.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Lanting, A

Reference 70

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This paper cites Hauke, L.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Hauke, L

Reference 71

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This paper cites Genuine Multipartite Entanglement in Quantum Optimization.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Genuine Multipartite Entanglement in Quantum Optimization

Reference 72

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This paper cites Lipkin, N.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Lipkin, N

Reference 73

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This paper cites Meshkov, A.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Meshkov, A

Reference 74

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This paper cites Glick, H.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Glick, H

Reference 75

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Reference 76

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This paper cites Ohzeki, Quantum Monte Carlo simulation of a par- ticular class of non-stoquastic Hamiltonians in quantum annealing, Sci.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Ohzeki, Quantum Monte Carlo simulation of a par- ticular class of non-stoquastic Hamiltonians in quantum annealing, Sci

Reference 77

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This paper cites Seki and H.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Seki and H

Reference 78

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Reference 79

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Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Unresolved cited work

Reference 80

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This paper cites Lucas, Ising formulations of many NP problems, Front.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Lucas, Ising formulations of many NP problems, Front

Reference 81

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This paper cites Dominguez, J.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Dominguez, J

Reference 82

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Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Unresolved cited work

Reference 83

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This paper cites Glos, and J.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Glos, and J

Reference 84

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This paper cites Adiabatic Quantum Algorithms for the NP-Complete Maximum-Weight Independent Set, Exact Cover and 3SAT Problems.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Adiabatic Quantum Algorithms for the NP-Complete Maximum-Weight Independent Set, Exact Cover and 3SAT Problems

Reference 85

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This paper cites Herrman, L.

Boosting quantum annealing performance through direct polynomial unconstrained binary optimization Herrman, L

Reference 86

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