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Honjo T, Sonobe T, Inaba K, Inagaki T, Ikuta T, Yamada Y, Kazama T, Enbutsu K, Umeki T, Kasahara R, Kawarabayashi KI, Takesue H. 100,000-spin coherent Ising machine. Sci Adv 2021; 7:eabh0952. [PMID: 34586855 PMCID: PMC8480917 DOI: 10.1126/sciadv.abh0952] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 08/04/2021] [Indexed: 05/20/2023]
Abstract
Computers based on physical systems are increasingly anticipated to overcome the impending limitations on digital computer performance. One such computer is a coherent Ising machine (CIM) for solving combinatorial optimization problems. Here, we report a CIM with 100,512 degenerate optical parametric oscillator pulses working as the Ising spins. We show that the CIM delivers fine solutions to maximum cut problems of 100,000-node graphs drastically faster than standard simulated annealing. Moreover, the CIM, when operated near the phase transition point, provides some extremely good solutions and a very broad distribution. This characteristic will be useful for applications that require fast random sampling such as machine learning.
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Affiliation(s)
- Toshimori Honjo
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
- Corresponding author. (T.H.); (H.T.)
| | - Tomohiro Sonobe
- National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
| | - Kensuke Inaba
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Takahiro Inagaki
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Takuya Ikuta
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Yasuhiro Yamada
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Takushi Kazama
- NTT Device Technology Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Koji Enbutsu
- NTT Device Technology Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Takeshi Umeki
- NTT Device Technology Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Ryoichi Kasahara
- NTT Device Technology Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Ken-ichi Kawarabayashi
- National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
| | - Hiroki Takesue
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
- Corresponding author. (T.H.); (H.T.)
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Hamerly R, Inagaki T, McMahon PL, Venturelli D, Marandi A, Onodera T, Ng E, Langrock C, Inaba K, Honjo T, Enbutsu K, Umeki T, Kasahara R, Utsunomiya S, Kako S, Kawarabayashi KI, Byer RL, Fejer MM, Mabuchi H, Englund D, Rieffel E, Takesue H, Yamamoto Y. Experimental investigation of performance differences between coherent Ising machines and a quantum annealer. Sci Adv 2019; 5:eaau0823. [PMID: 31139743 PMCID: PMC6534389 DOI: 10.1126/sciadv.aau0823] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 04/17/2019] [Indexed: 05/05/2023]
Abstract
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(-αDW N 2)] relative to CIMs [exp(-αCIM N)] 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.
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Affiliation(s)
- Ryan Hamerly
- Research Laboratory of Electronics, Massachusetts Institute of Technology, 50 Vassar Street, Cambridge, MA 02139, USA
- National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo 101-8403, Japan
- Corresponding author. (R.H.); (T.I.); (P.L.M.)
| | - Takahiro Inagaki
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
- Corresponding author. (R.H.); (T.I.); (P.L.M.)
| | - Peter L. McMahon
- National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo 101-8403, Japan
- E. L. Ginzton Laboratory, Stanford University, Stanford, CA 94305, USA
- School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA
- Corresponding author. (R.H.); (T.I.); (P.L.M.)
| | - Davide Venturelli
- NASA Ames Research Center Quantum Artificial Intelligence Laboratory (QuAIL), Mail Stop 269-1, Moffett Field, CA 94035, USA
- USRA Research Institute for Advanced Computer Science (RIACS), 615 National Avenue, Mountain View, CA 94035, USA
| | - Alireza Marandi
- E. L. Ginzton Laboratory, Stanford University, Stanford, CA 94305, USA
- California Institute of Technology, Pasadena, CA 91125, USA
| | - Tatsuhiro Onodera
- E. L. Ginzton Laboratory, Stanford University, Stanford, CA 94305, USA
| | - Edwin Ng
- E. L. Ginzton Laboratory, Stanford University, Stanford, CA 94305, USA
| | - Carsten Langrock
- E. L. Ginzton Laboratory, Stanford University, Stanford, CA 94305, USA
| | - Kensuke Inaba
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Toshimori Honjo
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Koji Enbutsu
- NTT Device Technology Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Takeshi Umeki
- NTT Device Technology Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Ryoichi Kasahara
- NTT Device Technology Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Shoko Utsunomiya
- National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo 101-8403, Japan
| | - Satoshi Kako
- National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo 101-8403, Japan
| | - Ken-ichi Kawarabayashi
- National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo 101-8403, Japan
| | - Robert L. Byer
- E. L. Ginzton Laboratory, Stanford University, Stanford, CA 94305, USA
| | - Martin M. Fejer
- E. L. Ginzton Laboratory, Stanford University, Stanford, CA 94305, USA
| | - Hideo Mabuchi
- E. L. Ginzton Laboratory, Stanford University, Stanford, CA 94305, USA
| | - Dirk Englund
- Research Laboratory of Electronics, Massachusetts Institute of Technology, 50 Vassar Street, Cambridge, MA 02139, USA
| | - Eleanor Rieffel
- NASA Ames Research Center Quantum Artificial Intelligence Laboratory (QuAIL), Mail Stop 269-1, Moffett Field, CA 94035, USA
| | - Hiroki Takesue
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Yoshihisa Yamamoto
- E. L. Ginzton Laboratory, Stanford University, Stanford, CA 94305, USA
- ImPACT Program, Japan Science and Technology Agency, Gobancho 7, Chiyoda-ku, Tokyo 102-0076, Japan
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Abstract
Methods for representing the meaning of words in vector spaces purely using the information distributed in text corpora have proved to be very valuable in various text mining and natural language processing (NLP) tasks. However, these methods still disregard the valuable semantic relational structure between words in co-occurring contexts. These beneficial semantic relational structures are contained in manually-created knowledge bases (KBs) such as ontologies and semantic lexicons, where the meanings of words are represented by defining the various relationships that exist among those words. We combine the knowledge in both a corpus and a KB to learn better word embeddings. Specifically, we propose a joint word representation learning method that uses the knowledge in the KBs, and simultaneously predicts the co-occurrences of two words in a corpus context. In particular, we use the corpus to define our objective function subject to the relational constrains derived from the KB. We further utilise the corpus co-occurrence statistics to propose two novel approaches, Nearest Neighbour Expansion (NNE) and Hedged Nearest Neighbour Expansion (HNE), that dynamically expand the KB and therefore derive more constraints that guide the optimisation process. Our experimental results over a wide-range of benchmark tasks demonstrate that the proposed method statistically significantly improves the accuracy of the word embeddings learnt. It outperforms a corpus-only baseline and reports an improvement of a number of previously proposed methods that incorporate corpora and KBs in both semantic similarity prediction and word analogy detection tasks.
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Affiliation(s)
- Mohammed Alsuhaibani
- Department of Computer Science, University of Liverpool, Liverpool, United Kingdom
- * E-mail:
| | - Danushka Bollegala
- Department of Computer Science, University of Liverpool, Liverpool, United Kingdom
- Kawarabayashi ERATO Large Graph Project, Tokyo, Japan
| | - Takanori Maehara
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Kawarabayashi ERATO Large Graph Project, Tokyo, Japan
| | - Ken-ichi Kawarabayashi
- National Institute of Informatics, Tokyo, Japan
- Kawarabayashi ERATO Large Graph Project, Tokyo, Japan
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Kajiwara T, Bollegala D, Yoshida Y, Kawarabayashi KI. An iterative approach for the global estimation of sentence similarity. PLoS One 2017; 12:e0180885. [PMID: 28898242 PMCID: PMC5595307 DOI: 10.1371/journal.pone.0180885] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Accepted: 06/22/2017] [Indexed: 11/21/2022] Open
Abstract
Measuring the similarity between two sentences is often difficult due to their small lexical overlap. Instead of focusing on the sets of features in two given sentences between which we must measure similarity, we propose a sentence similarity method that considers two types of constraints that must be satisfied by all pairs of sentences in a given corpus. Namely, (a) if two sentences share many features in common, then it is likely that the remaining features in each sentence are also related, and (b) if two sentences contain many related features, then those two sentences are themselves similar. The two constraints are utilized in an iterative bootstrapping procedure that simultaneously updates both word and sentence similarity scores. Experimental results on SemEval 2015 Task 2 dataset show that the proposed iterative approach for measuring sentence semantic similarity is significantly better than the non-iterative counterparts.
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