David Worral, Head of Quantum Technologies at Cambridge Quantum Computing, indicated that Fujitsu has a 1 million qubit quantum annealing system in a lab at the University of Toronto.
Fujitsu has been a leader in creating lithography machines and supercomputers and FPGAs. Fujitsu chose to leverage this technology and their massive capabilities to develop quantum annealing far beyond the capabilities and scale of D-Wave Systems.
Digital Annealer is a new technology that is used to solve large-scale combinatorial optimization problems instantly. Digital Annealer uses a digital circuit design inspired by quantum phenomena and can solve problems which are difficult and time consuming for classical computers.
Applications in Decryption, Optimization and More
If the 1 million qubit quantum annealing system works then many levels (ECC) of financial and corporate encryption could be cracked within a year.
Other possibilities for digital annealing in financial services include calculating the optimum amount of cash and the most efficient route for ATM replenishment. Cash replenishment
accounts for up to 60 percent of ATM network operating costs and optimization would improve profitability significantly at a time when ATM network operations are under pressure.
And using Fujitsu’s Quantum-Inspired Digital Annealer, NatWest bank has completed a highly complex portfolio risk optimization calculation that needs to be undertaken regularly by the bank, at 300 times the speed of a traditional computer while providing an even higher degree of accuracy.
Fujitsu Laboratories has shown the capability of the Digital Annealer, Fujitsu’s computational architecture inspired by quantum phenomena that rapidly solves combinatorial optimization problems, to maximize the performance of magnetic devices essential for renewable energy harvesting and other uses. The application of Fujitsu’s next-generation architecture allows for the nearly instantaneous calculation of the optimal arrangement of multiple planar (2D) magnets to maximize the strength of the magnetic field in a device.
Many magnetic devices used for environmental power generation create magnetic flux through the arrangement of a large number of small magnets. The optimal planar (2D) arrangement for maximizing power generation efficiency remains difficult to calculate due to the enormous number of potential combinations of magnet arrangements, however. To overcome this challenge, Fujitsu has developed a technology that utilizes its Digital Annealer to calculate in a matter of seconds how to arrange each individual magnet to achieve maximum magnetic flux density, delivering an efficiency gain of 16%.
This technological breakthrough now makes it possible to quickly calculate the optimal design for magnetic devices with significantly higher power generation efficiency, and will ultimately contribute to the spread of power generation devices that utilize renewable energy such as energy harvesting devices.
Features and Benefits
* Stable operation at normal room temperature and small form factor
* Fully coupled 8,192-bit connectivity that allows for large-scale problem solving
* 64-bit (264) gradations allow high accuracy in expressing combinatorial optimization problems
Fujitsu’s Digital Annealer can solve combinatorial optimization problems instantly using a digital circuit design inspired by quantum phenomena.
The advancement of ICT technology and the realization of Artificial Intelligence (AI) today means that there is a necessity for computers to be able to carry out complex calculations in an instant, with the expectation that the knowledge gained will then be applied to various fields of business such as manufacturing, distribution, retail, automotive and finance, to name a few.
This case study introduces how Fujitsu IT Products Limited deployed Digital Annealer and succeeded in dramatically improving the efficiency of in-warehouse operations.
What is combinatorial optimization?
There is a widespread real-world demand for the ability to choose the optimal solution from a finite set of possibilities, where the scale is typically measured in quintillions. These challenges are classified as combinatorial optimization problems – essentially finding the best combination from an enormous set of potential elements. Example use cases include portfolio optimization and credit risk assessment in financial services; job shop scheduling, car design optimization, robot positioning optimization and many more in manufacturing; drug and materials discovery in life sciences and asset allocation for utilities networks. These problems are difficult to solve optimally in real-time with existing processors, even with the fastest computers, as the number of combinations increases exponentially as the number of factors taken into consideration is increased to obtain precision.
Traffic route optimization, for example, is a particularly difficult arena. Optimizing five pairs of start and destination points has to consider 10^100 possible routes, avoiding overlaps between vehicles and avoiding traffic jams. This use case has been investigated by several global automotive vendors for their autonomous cars and mobility platforms, and by governments as a means to reduce transport’s carbon footprint and for the betterment of the society.
Conventional computing has challenges solving combinatorial optimization challenges optimally in a practical amount of time and relies on approximations. Quantum computing computes all possible solutions simultaneously. When it is eventually ready to move out of the laboratory and solve practical real-world problems, quantum computing may be able to solve such challenges. But it’s not yet usable in the real world. On the other hand, quantum-inspired computing with the Fujitsu Digital Annealer is available today and delivers optimization calculations with the speed, precision and at a scale that true quantum computing is not able to achieve. For example, the Digital Annealer solution can solve the five pair traffic optimization challenge dealing with 10^100 possibilities in one second.
Annealing is a probabilistic technique for approximating the overall optimum result of a given function. Until now, in tackling any combinatorial optimization process with annealing, there has been a trade-off between precision and risk. Seeking high precision used to imply the need for more time to calculate the answer – often more time than was available – while accepting a ‘good enough’ answer introduced an increasing amount of risk and the need for a security buffer. The more precise the calculation you can achieve, the more cost-efficient the final process will be, leading to less waste.
Quantum annealing solves the speed side of this equation, but it is unlikely to be available for solving real-world scenarios or ready for practical enterprise use for at least 10 to 15 years, if at all. Fujitsu’s scientists were keen on finding how to solve these critical problems today and realized that the software being developed for quantum computers could be applied to digital architectures.