2017/08/31 by Helmut G. Katzgraber
Computer Science · Physics and Astronomy · #Algorithm #Computer engineering #Computer science #Heuristics #Neural Networks and Reservoir Computing #Physics #Quadratic unconstrained binary optimization #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum annealing #Quantum computer #Quantum mechanics #Qubit #Scope (computer science) #Simulated annealing #Spin glass #cond-mat.dis-nn #quant-ph
paper · pdf · doi:10.1088/2058-9565/aab6ba
published as Quantum Sci. Technol. 3, 030505 (2018) · Perspective article submitted to QST on the subject "What would you do with 1000 qubits?"
arxiv created 2018/06/19 · openalex publication_date 2018/06/19 · arxiv updated 2018/06/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Abstract Quantum annealing promises to solve complex combinatorial optimization problems faster than current transistor-based computer technologies. Although to date only one commercially-available quantum annealer is procurable, one can already start to map out the application scope of these novel optimization machines. These mid-scale programmable analog special-purpose devices could, potentially, revolutionize optimization. However, their disruptive application domain remains to be found. While the commercial analog quantum optimization machine by D-Wave Systems Inc. already exceeds 1000 qubits, here it is argued that maybe smaller devices with better quality qubits, higher connectivity, and more tunability might be better suited to answer if quantum annealing will ever truly outperform specialized silicon technology combined with efficient heuristics for optimization and sampling applications.