Today Fujitsu launched of the Digital Platform Experimentation Project in Singapore. In cooperation with A*STAR and SMU, the Project marks the world’s 1st on-premises installation of the Fujitsu Quantum-Inspired Computing Digital Annealer.
The Digital Annealer provides an alternative to quantum computing technology, which is at present both very expensive and difficult to run. Using a digital circuit design inspired by quantum phenomena, the Digital Annealer focuses on rapidly solving complex combinatorial optimization problems without the added complications and costs typically associated with quantum computing methods. The Digital Annealer will play an important role in this initiative by allowing the partners to explore novel problem-solving approaches and methodologies for a wide variety of potential real-world applications. Use cases to date include portfolio optimization, drug discovery, factory optimization, inventory management, and digital marketing.
We expect to see quantum-inspired computing exceed the limits of conventional computing in this modern age of digitalization,” said Dr Lim Keng Hui, Executive Director of IHPC. “Through our collaboration with Fujitsu and SMU, A*STAR will develop algorithms and methodologies for resource-efficient machine learning. This will reduce memory footprint, complexity and demonstrate real world use cases for industry applications. In the longer term, we aim to deploy these technologies to address complex challenges faced in experimental and computational science.”
The project will nurture local talent and capabilities at the intersection of AI, deep learning, and quantum-inspired computing. A*STAR’s Institute of High Performance Computing (IHPC) will play a key role in the deep learning and related AI capabilities for the project. SMU’s School of Information Systems will strengthen the quantum-inspired computing and related AI optimization capabilities. Combining deep learning, AI, and quantum-inspired computing technologies into a single computational service-delivery platform will help solve very complex, large-scale, real-world problems —particularly combinatorial optimization problems at the core of planning and scheduling scenarios.
The project also taps on the CoE’s research and development capabilities to implement Fujitsu technologies and accelerate the development of commercial applications using high performance optimization. This will further establish Fujitsu’s global quantum-inspired and artificial intelligence (AI) eco-system, with the SMU installation marking the first-in-the-world on-premises deployment of the Fujitsu Digital Annealer platform.
As the project progresses, Fujitsu, A*STAR, and SMU will work with key institutions and stakeholders in Singapore’s quantum computing community.
The DigiPlex Project will make use of Fujitsu’s next generation Digital Platform which is strategically very important for Fujitsu, from both research and development and business perspectives,” said Jo Oda, Corporate Executive Officer EVP, Head of Co-Creation Business Group, Japan Sales at Fujitsu. “Fujitsu’s technological contributions to the project will serve as a key enabler to realizing high performance solutions with quantum-inspired computing and AI, beyond what is currently available in the market. Upon successful implementation, Fujitsu will commercialize these solutions for the global market.”
Another important aspect of the Digital Platform Experimentation Project is the demonstration of machine learning technology through Fujitsu’s Digital Transformation (DX) Services and Platforms, which include technologies that accelerate deep learning for new applications and solutions in a variety of industries. These deep learning capabilities will prove increasingly important with the growth of edge computing and IoT devices.
In this project, SMU will benchmark Digital Annealer with exact commercial solvers (such as CPLEX and Gurobi) as well as other heuristic methods to solve complex combinatorial optimization problems. Classical methods will be combined with quantum-inspired methods to discover new hybrid algorithms that run on conventional computers to tackle practical use cases in resource planning and scheduling, such as designing daily schedules for ambulance and police cars to respond to crimes and emergencies in a congested city. This research will help to optimize resources toward a smart, safe and sustainable city.
A*STAR’s Institute of High Performance Computing (IHPC) will contribute capabilities in developing deep learning models on real-life use cases with video data analysis for security applications, such as video anomaly detection, video action classification and real-time crowd analysis. The research focus aims to shorten video training time and significantly reduce memory footprint requirements.
