Fujitsu and University of Toronto optimise network transformation with Digital Annealer to help customers significantly cut network operations costs

Fujitsu today confirmed the effectiveness of Fujitsu's Quantum-inspired Digital Annealer (1) technology (2) for optimising the transformation process of legacy telecommunications networks. The project, the result of joint research conducted with the University of Toronto (3) between April 2021 and February 2023, marks the first time in the world that quantum inspired technology has been applied to network modernisation.

Fujitsu has derived a combinatorial optimisation formula and developed a solution using the Digital Annealer to find the best circuit migration plan, which plays a crucial role in the rapid and cost efficient transformation of legacy networks. This approach allows Fujitsu to help network operator customers reduce the operating costs of legacy facilities by up to 30% and the transportation costs of engineers by up to 80% during the migration period compared with conventional commercial optimisation software.

Fujitsu, through its network business, will deliver this technology to network operator customers in North America, which leads the way in network modernisation. Further research and development will be conducted on new applications of this technology. Fujitsu aims to leverage the Digital Annealer to create new value and enhance its customers’ competitiveness across a number of industries, and ultimately contribute to greater sustainability for industry and society as a whole.

Details of the technology will be presented at the international conference The Optical Fiber Communication Conference and Exposition (OFC) 2023 to be held March 5 – March 9 in San Diego, United States.

Quantum-inspired optimisation significantly streamlines network migration

Traditionally, network equipment has been regularly updated and replaced to maintain the integrity of networks. In many cases, however, this work has been for small networks, and network operators have conducted migration planning manually. For today's wide-area networks in countries and regions where the need to migrate is urgent, large numbers of circuits and network elements are complexly interrelated, and the combination of possible circuit migration sequences is 10 to the power of 150 or more even for a case of small network. This essentially renders it impossible to manually derive the optimum combination from these variables within a practical timeframe.

As part of joint research with Professor Shahrokh Valaee and his team at the University of Toronto, Fujitsu has derived combinatorial optimisation formulation for searching the circuit migration sequences that minimise costs using the Digital Annealer and analysed performance of multiple large-scale networks examples with dozens of network elements and hundreds of circuits. Digital Annealer significantly reduces the search space of variable combinations, making it possible to rapidly generate an optimal migration sequence. For the trial, Fujitsu has developed an algorithm that represents the optimisation problem with two-way, one-hot constraints (4). This made it possible to carry out searches that take advantage of the strengths of the Digital Annealer, achieving significant improvement in speed to conduct the network modernisation.

Figure 1: Results of cost reductionFigure 1: Results of cost reduction

  • [1]

    Digital Annealer :
    Domain specific computing technology that specialises in solving combinatorial optimisation problems that are difficult to solve with current general-purpose computers.

  • [2]

    Quantum-inspired technology :
    A number of arithmetic acceleration technologies inspired by quantum technology, but not quantum effects.

  • [3]

    The University of Toronto :
    Founded in 1827, the University of Toronto has evolved into Canada’s leading institution of learning, discovery and knowledge creation. One of the world's top research-intensive universities, the university offers 700 programs of study at its three campuses in the Greater Toronto Area and has full affiliations with nine teaching and research hospitals. For more information, visit

  • [4]

    The optimisation problem has a rule called "two-way one-hot constraint" where the variables to be searched are arranged in a matrix by 0 or 1 and there is only one variable that takes 1 in each row and column.

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