Innovations in Ising machine technology

Computers are essential for solving complex problems in fields such as planning, logistics, and route planning, but traditional computers struggle with large-scale combinatorial optimization because they cannot efficiently process a vast number of possibilities. To address this, researchers have explored specialized systems.

One such system is the Hopfield network, a significant innovation of artificial intelligence since 1982, tested in 1985 to solve combinatorial optimization, representing solutions as energy levels and naturally finding the lowest energy, or excellent solution.

Based on similar ideas, Ising machines use the principles of magnetic spin to find efficient solutions by minimizing the energy of the system through a process similar to annealing. However, a major challenge with Ising machines is their large circuit footprint, especially in fully connected systems where each spin interacts with the others, complicating their scalability.

A research team from the University of Science in Tokyo, Japan, has been working to find solutions to this problem in relation to Ising machines. In a recent study led by Professor Takayuki Kawahara, they reported an innovative method that can halve the number of interactions that must be physically implemented. His findings were published in the newspaper Access IEEE on October 1, 2024.

The proposed method focuses on visualizing the interaction between spins as a two-dimensional matrix, where each element represents the interaction between two specific spins. Since these interactions are “symmetric” (that is, the interaction between Spin 1 and Spin 2 is the same as that between Spin 2 and Spin 1), half of the interaction matrix is ​​redundant and can be omitted – this concept has been around for many years. years

In 2020, Professor Kawahara and colleagues presented a method to fold and rearrange the remaining half of the interaction matrix in a rectangular shape to minimize the footprint of the circuit. While this led to efficient parallel computations, the wiring required to read interactions and update spin values ​​became more complex and harder to scale.

In this study, the researchers proposed a different way to halve the interaction matrix that leads to a better scalability in the circuits. I split the matrix into four sections and split each of these sections in half individually, alternatively preserving the “top” or “bottom” half of each submatrix. Then, they folded and rearranged the remaining elements in a rectangular shape, unlike the previous approach, which maintains the regularity of its arrangement.

Exploiting this crucial detail, the researchers implemented a fully coupled Ising machine based on this technique on their previously developed custom circuit containing 16 field-programmable gate arrays (FPGAs).

“Using the proposed approach, we can implement 384 spins in only eight FPGA chips. In other words, two independent and fully connected Ising machines could be implemented on the same board,” says Professor Kawahara, “Using these machines, two I classic combinatorial optimization problems were solved simultaneously, that is, the max-cut problem and the four-color problem”.

The performance of the circuit developed for this demo was amazing, especially when compared to how slow a conventional computer would be in the same situation. “We found that the performance ratio of two independent 384-spin fully coupled Ising machines was about 400 times better than simulating an Ising machine on a regular Core i7-4790 CPU for solving both problems in sequence,” reports Kawahara.

In the future, these cutting-edge developments will pave the way to scalable Ising machines suitable for real world applications such as faster molecular simulations to accelerate drug and materials discovery.

In addition, improving the efficiency of data centers and the electric power network is also a feasible use case, which aligns well with the global sustainability goals of reducing the carbon footprint of emerging technologies such as and electric vehicles and 5G / 6G telecommunications.

As innovations continue to develop, scalable Ising machines could soon become invaluable tools in industry, transforming how we tackle some of the world’s most complex optimization challenges.