Perspectives of quantum annealing: methods and implementations
By Philipp Hauke, Helmut G. Katzgraber, W. Lechner, Hidetoshi Nishimori, William D. Oliver. Reports on progress in physics. Physical Society, 2019
552 citations recorded by the source corpus.
Quantum annealing is a computing paradigm that has the ambitious goal of efficiently solving large-scale combinatorial optimization problems of practical importance. However, many challenges have yet to be overcome before this goal can be reached. This perspectives article first gives a brief introduction to the concept of quantum annealing, and then highlights new pathways that may clear the way towards feasible and large scale quantum annealing. Moreover, since this field of research is to a strong degree driven by a synergy between experiment and theory, we discuss both in this work. An important focus in this article is on future perspectives, which complements other review articles, and which we hope will motivate further research.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/1b964e5af73ab3d6720dcfc85b53589d115cfb1d
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Citations in the corpus
- Practical quantum advantage in quantum simulation (2022, background): “This is also especially important for the encoding of broader computational challenges (including, for example, optimization) on quantum dynamics, where the resulting Hamiltonians again generally involve long-range interactions 95,96 .”
- Quantum simulation and computing with Rydberg-interacting qubits (2020, methodology): “Quantum annealing is a technique for efficiently solving complex optimization problems of widespread practical importance 268 .”