Learning-Driven Annealing with Adaptive Hamiltonian Modification for Solving Large-Scale Problems on Quantum Devices [PDF]
QuantumWe present Learning-Driven Annealing (LDA), a framework that links individual quantum annealing evolutions into a global solution strategy to mitigate hardware constraints such as short annealing times and integrated control errors.
Sebastian Schulz +2 more
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Quantum annealing for jet clustering with thrust [PDF]
Physical Review D, 2022 Quantum computing holds the promise of substantially speeding up computationally expensive tasks, such as solving optimization problems over a large number of elements.
Andrea Delgado, J. Thaler
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Travel time optimization on multi-AGV routing by reverse annealing
Scientific Reports, 2022 Quantum annealing has been actively researched since D-Wave Systems produced the first commercial machine in 2011. Controlling a large fleet of automated guided vehicles is one of the real-world applications utilizing quantum annealing. In this study, we
Renichiro Haba +2 more
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Quantum annealing of a frustrated magnet. [PDF]
Nat CommunQuantum annealing, which involves quantum tunnelling among possible solutions, has state-of-the-art applications not only in quickly finding the lowest-energy configuration of a complex system, but also in quantum computing.
Zhao Y +6 more
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Optimal Protocols in Quantum Annealing and Quantum Approximate Optimization Algorithm Problems.
Physical Review Letters, 2021 Quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA) are two special cases of the following control problem: apply a combination of two Hamiltonians to minimize the energy of a quantum state.
L. Brady +4 more
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Deep learning optimal quantum annealing schedules for random Ising models
New Journal of Physics, 2023 A crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory neural networks to automate the search for ...
Pratibha Raghupati Hegde +3 more
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Integrating Machine Learning Algorithms With Quantum Annealing Solvers for Online Fraud Detection
IEEE Access, 2022 Machine learning has been increasingly applied in identification of fraudulent transactions. However, most application systems detect duplicitous activities after they have already occurred, not at or near real time.
Haibo Wang +3 more
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Towards optimization of photonic-crystal surface-emitting lasers via quantum annealing. [PDF]
Optics Express, 2022 Photonic-crystal surface-emitting lasers (PCSELs), which utilize a two-dimensional (2D) optical resonance inside a photonic crystal for lasing, feature various outstanding functionalities such as single-mode high-power operation and arbitrary control of ...
Takuya Inoue +5 more
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Quantum annealing for vehicle routing problem with weighted segment [PDF]
PROCEEDINGS OF THE 1ST CONFERENCE ON QUANTUM SCIENCES AND TECHNOLOGY (CONQUEST 2022), 2022 Quantum annealing technologies aim to solve computational optimization and sampling problems. QPU (Quantum Processing Unit) machines such as the D-Wave system use the QUBO (Quadratic Unconstrained Binary Optimization) formula to define model optimization
Toufan D. Tambunan +3 more
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Expanding the scope of quantum annealing applicability
AIP Advances, 2023 With the emergence of D-wave’s quantum annealing machine, the power of quantum computing has become tangible, and the development of quantum computers is accelerating rapidly.
Hiroshi Isshiki, Koki Asari
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