Digital Annealer Research

Moore's law, an observation that roughly states that the number of transistors on a classical computer doubles every two years, has been long used to predict future performance of computer systems. However, we are reaching the physical limits of this observation whereas the problems faced by society grow ever more computationally challenging and data intensive.

Hence new computer architectures must be developed to address this emerging challenge. Furthermore, novel algorithms must also be developed that utilize these new technologies efficiently and effectively. At Fujitsu, we are leveraging our expertise in building and designing digital circuits. As such, we have developed the novel technology known as the Digital Annealer for solving computationally challenging combinatorial optimization problems. At Fujitsu Research of America, our main focus has been on the design and developments of novel algorithms within combinatorial optimization and machine learning that leverage the Digital Annealer and other Post Moore's law technologies.

Our Digital Annealer research projects include, but are not limited to:

  • Quadratization: Transformation of Higher Order Binary Optimization (HOBO) problems into Quadratic Unconstrained Binary Optimization (QUBO) problems
  • Development of novel data clustering and graph clustering algorithms
  • Development of novel algorithms for well-known combinatorial optimization problems such graph partitioning
  • Development of novel algorithms for scheduling and resource allocation

Publications


  1. 1. Mandal, Avradip, Arnab Roy, Sarvagya Upadhyay, and Hayato Ushijima-Mwesigwa. "Compressed Quadratization of Higher Order Binary Optimization Problems." ACM Computer Frontiers (2020).
  2. 2. Cohen, Eldan, Avradip Mandal, Hayato Ushijima-Mwesigwa, and Arnab Roy. "Ising-Based Consensus Clustering on Specialized Hardware." In International Symposium on Intelligent Data Analysis, pp. 106-118. Springer, Cham, 2020.
  3. 3. Liu, Xiaoyuan, Hayato Ushijima-Mwesigwa, Avradip Mandal, Sarvagya Upadhyay, Ilya Safro, and Arnab Roy. "On Modeling Local Search with Special-Purpose Combinatorial Optimization Hardware." (2019).
  4. 4. Ushijima-Mwesigwa, Hayato, Ruslan Shaydulin, Christian FA Negre, Susan M. Mniszewski, Yuri Alexeev, and Ilya Safro. "Multilevel combinatorial optimization across quantum architectures." (2019).
  5. 5. Shaydulin, Ruslan, Hayato Ushijima-Mwesigwa, Ilya Safro, Susan Mniszewski, and Yuri Alexeev. "Network community detection on small quantum computers." Advanced Quantum Technologies 2, no. 9 (2019): 1900029.
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