• Researchers from Japan’s Tohoku University have developed a mathematical model to describe what happens within small magnets as they fluctuate between states when an electric current and magnetic field are applied.
• Their findings, published in the Nature Communications journal, could be used to develop more advanced computers that can calculate uncertainty while interpreting complex data.
• There may be various ways to build such a computer, but some scientists are investigating the utilization of segments called magnetic tunnel junctions.
• This provides us with the mathematical framework needed to incorporate magnetic tunnel junctions into the p-bit and create complex probabilistic computers.

Scientists from Japan’s Tohoku University have created a mathematical model to explain what occurs within tiny magnets as they oscillate between states when an electric current and magnetic field are applied. Their research, published in Nature Communications, may serve as the basis for creating more sophisticated computers that can estimate uncertainty while deciphering complicated data.

Researchers are focusing more on probabilistic computing

Even while traditional computers have helped us get this far, there are still issues that they cannot effectively solve. To overcome this, scientists have started developing computers that can use the principles of quantum physics to spot patterns in challenging situations. However, these so-called quantum computers are still in the early phases of research and require extremely low temperatures to operate because they are highly sensitive to their environment.

Last month, we discussed how probabilistic computers (p-computers) could be vital for developing efficient AI and ML systems. Researchers are searching for new computing paradigms because the classical computers that are now in use cannot complete that task in an energy-efficient manner. While still in the research stages and very environment-sensitive, qubit-based quantum computers may help address these challenges.

Qudit computers open endless possibilities by exceeding the binary system

P-bits, or probabilistic bits, operate P-computers by interacting with other p-bits in the same system. P-bits work at normal temperature and oscillate between positions, unlike qubits, which can be in multiple states simultaneously and are similar to the bits in traditional computers, which are either in a 0 or a 1 state.

Now, researchers are focusing more on probabilistic computing. A computer of this kind, able to run at room temperature, would be able to deduce viable solutions from complex input. To infer information about a person by looking at their purchase behavior is a simple example of this type of issue. Instead of giving a single, definitive answer, the computer looks for patterns and makes a good guess as to what the answer might be.

There may be several ways to construct such a computer. However, some researchers are looking into using components known as magnetic tunnel junctions. These are constructed from two magnetic metal layers spaced apart by a very thin insulator. Electrons tunnel through the insulating layer when these nanomagnetic devices are thermally activated in the presence of an electric current and magnetic field.

Quantum computing turns into accessible services with the cloud-based quantum computers

They can alter or produce variations inside the magnets depending on their rotation. Probabilistic computing may be based on these fluctuations, also known as p-bits, which are an alternative to the on/off or 0/1 bits that we are all familiar with from classical computers. However, researchers must be able to explain the physics of magnetic tunnel junctions to design probabilistic computers.

“We have experimentally clarified the ‘switching exponent’ that governs fluctuation under the perturbations caused by magnetic field and spin-transfer torque in magnetic tunnel junctions. This gives us the mathematical foundation to implement magnetic tunnel junctions into the p-bit in order to sophisticatedly design probabilistic computers.

Our work has also shown that these devices can be used to investigate unexplored physics related to thermally activated phenomena,” explained Shun Kanai from the Research Institute of Electrical Communication at Tohoku University

P-computers might change the future of information technologies for good. There is an urgent demand for additional technology that is scalable and energy-efficient due to the advent of artificial intelligence (AI) and machine learning (ML). Making judgments based on insufficient data is a crucial step in both AI and ML, and the optimal strategy is to output a probability for each potential response.

P-computers are powered by probabilistic bits

Due of the inability of current classical computers to do that task in an energy-efficient manner, researchers are looking for new computing paradigms. Qubit-based quantum computers may be able to assist in overcoming these difficulties, but they are still in the early phases of research and are very sensitive to their environment.

It is an inevitable fact that artificial intelligence will completely change the future. Apart from scientific developments, legal regulations seem to pave the way for the use of artificial intelligence, for instance, UK eases restrictions on data mining laws to facilitate AI industry growth.

Kerem Camsari, an assistant professor of electrical and computer engineering (ECE) at UC Santa Barbara, believes that probabilistic computers (p-computers) are the solution. P-computers are powered by probabilistic bits (p-bits), which interact with other p-bits in the same system. Unlike the bits in classical computers, which are in a 0 or a 1 state, or qubits, which can be in more than one state at a time, p-bits fluctuate between positions and operate at room temperature. In an article published in Nature Electronics, Camsari and his collaborators discuss their project that demonstrated the promise of p-computers.

“We showed that inherently probabilistic computers, built out of p-bits, can outperform state-of-the-art software that has been in development for decades,” said Camsari.

Researchers from the University of Messina in Italy, vice chair of the UCSB ECE department Luke Theogarajan, and physics professor John Martinis, who oversaw the group that created the first quantum computer to attain quantum supremacy, all worked with Camsari’s team. Together, the researchers produced their encouraging results utilizing domain-specific architectures built on traditional hardware. They created a special sparse Ising machine (sIm), a cutting-edge computing system designed to address optimization issues and reduce energy usage.

According to Camsari, the sIm is a group of probabilistic bits that may be compared to individuals. Additionally, each individual only has a tiny group of close friends, or “sparse” relationships, in the system.

“The people can make decisions quickly because they each have a small set of trusted friends and they do not have to hear from everyone in an entire network. The process by which these agents reach consensus is similar to that used to solve a hard optimization problem that satisfies many different constraints. Sparse Ising machines allow us to formulate and solve a wide variety of such optimization problems using the same hardware,” explained Camsari.

Field-programmable gate arrays (FPGAs), a potent piece of hardware that offers far more flexibility than application-specific integrated circuits, were a component of the team’s prototyped design.

“Imagine a computer chip that allows you to program the connections between p-bits in a network without having to fabricate a new chip,” said Camsari.

The researchers demonstrated that their sparse design on FPGAs has boosted sampling speed five to eighteen times quicker than those attained by optimized methods employed on conventional computers, which was up to six orders of magnitude faster.

Additionally, they stated that their sIm achieves huge parallelism where the number of p-bits grows linearly with the number of flips per second, the fundamental metric used to determine how rapidly a p-computer can make an educated decision. Camsari returns to the image of two reliable friends attempting to decide.

“The key issue is that the process of reaching a consensus requires strong communication among people who continually talk with one another based on their latest thinking. If everyone makes decisions without listening, a consensus cannot be reached and the optimization problem is not solved,” added Camsari.

In other words, it is important to increase the flips per second while making sure that everyone listens to each other since the faster the p-bits communicate, the faster a consensus may be formed.

“This is exactly what we achieved in our design. By ensuring that everyone listens to each other and limiting the number of ‘people’ who could be friends with each other, we parallelized the decision-making process,” explained Camsari.

While acknowledging that their ideas are only one part of the p-computer jigsaw, Camsari finds their work incredibly promising because it demonstrated the capacity to grow p-computers up to 5,000 p-bits.

“To us, these results were the tip of the iceberg. We used existing transistor technology to emulate our probabilistic architectures, but if nanodevices with much higher levels of integration are used to build p-computers, the advantages would be enormous. This is what is making me lose sleep,” added Camsari.

The device’s potential was originally demonstrated by an 8 p-bit p-computer created by Camsari and his partners while he was a graduate student and postdoctoral researcher at Purdue University. Their article, which was published in 2019 in Nature, detailed a ten-fold decrease in the energy it used and a hundred-fold decrease in the area footprint. Camsari and Theogarajan were able to further their p-computer research thanks to seed funding from UCSB’s Institute for Energy Efficiency, which supported the study published in Nature Electronics.

“The initial findings, combined with our latest results, mean that building p-computers with millions of p-bits to solve optimization or probabilistic decision-making problems with competitive performance may just be possible,” said Camsari.

The study team anticipates that one day, p-computers will be more quicker and more effective at handling a certain class of tasks, ones that are inherently probabilistic. If you liked this article check out how the latest study showed it is possibe to improve the interpretability of ML features for end-users.