New scalable quantum processor can solve optimisation problems

A research group at the Tokyo University of Science (TUS) have made an important breakthrough in solving optimisation problems, with the development of a fully scalable quantum processor.

Optimisation problems are common in everyday life, springing up across several fields, such as network routing, logistics, machine learning, and material science.

These problems are extremely complex and difficult to solve using standard computers, so researchers have had to turn to other methods. The team used a process known as annealing, that can be used to model optimisation problems.

Mimicking the behaviour of ‘spins’

The researchers have attempted to create annealing processors that mimic the magnetic orientation of atoms, known as ‘spins’. The spins orientate randomly at high temperatures, but as the temperature decreases, the spins line up to reach the minimum energy state.

The team presented the first fully-coupled, large-scale quantum processor, comprising 512 fully connected spins. These systems are notoriously hard to implement and upscale, due to the sheer number of connections between spins that need to be considered.

While using multiple fully connected chips in parallel was a potential solution to the scalability problem, this meant that too many wires needed to be implemented in between the chips.

New solutions to scalability problems

To overcome issues relating to scalability, developing a new method in which the calculation of the quantum processor’s energy state is divided along multiple fully coupled chips. This process forms an ‘array calculator’, then a control chip collects the results from the rest of the chips and computes the total energy.

Professor Takayuki Kawahara, who led the study, explained: “The advantage of our approach is that the amount of data transmitted between the chips is extremely small. Although its principle is simple, this method allows us to realise a scalable, fully connected LSI system for solving combinatorial optimisation problems through simulated annealing.”

The researchers were able to develop a fully scalable quantum processor by using commercial FPGA chips. A widely used semiconductor device, these chips enabled the team to build a processor with 384 spins.

The machine has been successfully used to solve a number of proposed optimisation problems, including a 92-node graph colouring problem, and a 384-node maximum cut problem. These proposed experiments showed that the proposed device has true performance benefits.

Compared to other quantum processors with the same annealing systems, the FPGA system was 584 times faster and 46 times more energy efficient when solving the maximum cut problem.

With the successful demonstration of the FGPA quantum processor, the team want to further develop the device. Kawahara commented: “We wish to produce a custom-designed LSI chip to increase the capacity and greatly improve the performance and power efficiency of our method. This will enable us to realise the performance required in the fields of material development and drug discovery, which involve very complex optimisation problems.”

The team plan to promote the implementation of their results to solve real life problems. They wish to engage with other companies, and bring their approach to the core of semiconductor design technology.

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