Experimental investigation of performance differences between coherent Ising machines and a quantum annealer

By Ryan Hamerly, T. Inagaki, Peter Leonard McMahon, Davide Venturelli, Alireza Marandi, Tatsuhiro Onodera, Edwin Ng, C. Langrock, K. Inaba, Toshimori Honjo and 13 more. Science Advances, 2018

374 citations recorded by the source corpus.

Benchmarking the coherent Ising machine and the D-Wave quantum annealer sheds light on the importance of connectivity. Physical annealing systems provide heuristic approaches to solving combinatorial optimization problems. Here, we benchmark two types of annealing machines—a quantum annealer built by D-Wave Systems and measurement-feedback coherent Ising machines (CIMs) based on optical parametric oscillators—on two problem classes, the Sherrington-Kirkpatrick (SK) model and MAX-CUT. The D-Wave quantum annealer outperforms the CIMs on MAX-CUT on cubic graphs. On denser problems, however, we observe an exponential penalty for the quantum annealer [exp(–αDWN2)] relative to CIMs [exp(–αCIMN)] for fixed anneal times, both on the SK model and on 50% edge density MAX-CUT. This leads to a several orders of magnitude time-to-solution difference for instances with over 50 vertices. An optimal–annealing time analysis is also consistent with a substantial projected performance difference. The difference in performance between the sparsely connected D-Wave machine and the fully-connected CIMs provides strong experimental support for efforts to increase the connectivity of quantum annealers.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/79ab8cd207fdfd8c2db39ec6b1189df6420868ed

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In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

Citations in the corpus

Ising machines as hardware solvers of combinatorial optimization problems (2022, background): “The clearest indication of this is that similar performance to demonstrated coherent Ising machines may be achieved by simulating the mean-ﬁeld dynamics 119,178,179,211 .”
Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices (2018, neutral): “[66] Ryan Hamerly, Takahiro Inagaki, Peter L.”