Çamsari & Chowdhury "p-computers Keep Winning"

From the Robert Mehrabian COE News article "Probabilistic Computers Keep Winning"

It remains an open question when a commercial quantum computer will be built that can deliver an advantage in speed and energy efficiency while solving real-world combinatorial optimization problems. In the meantime, the probabilistic computer, or p-computer, based on probabilistic bits, or p-bits — a specialty of UC Santa Barbara electrical and computer engineering (ECE) associate professor Kerem Çamsarı  — has proven its value as an intermediate technology for solving such hard optimization problems. 

Two recent papers reiterate that value. In one, Çamsarı collaborated with researchers at Northwestern University to explore how synchronous probabilistic computers — where p-bits largely update in parallel — compared to the asynchronous designs his group originally developed, in which each p-bit updates independently and stochastically.

“In this work, the team achieved two exciting milestones,” says Çamsarı. “First, we showed that using voltage to control magnetism can produce highly efficient probabilistic bits. Second, we demonstrated that a carefully synchronized architecture — where all bits update together, like dancers moving in lockstep — can match the performance of conventional designs, in which each bit updates independently and unpredictably.”

A Probabilistic Leap

In the most recent paper, published in the journal Nature Communications on October 16, 2025, Shuvro Chowdhury, a postdoctoral researcher in the Çamsari lab, and fourteen co-authors, including Çamsari and fellow ECE professor and department chair, Luke Theogarajan, demonstrated an ability to outperform a leading quantum annealer on the same “spin-glass” benchmarks that are representative of hard optimization problems.  

Titled, “Pushing the Boundary of Quantum Advantage in Hard Combinatorial Optimization with Probabilistic Computers,” the paper is something of a response to a claim in another recent journal paper suggesting that a privately built quantum computer solved the spin-glass problem better and faster than any other current technology. Chowdhury and the rest of the team, comprising individual experts assembled to address the numerous specific challenges facing them, showed that, with enough p-bits, a probabilistic computer might have the edge in speed and efficiency.

“This is an impressive piece of work from the Çamsari Lab,” says Theogarajan. “It’s a truly remarkable result that illustrates the power of p-bits and their future in computing.”

“To solve this problem required us to build p-computers at sizes we had never gone to before,” Çamsarı says, “We used millions of p-bits and then did simulations on CPUs to see how they will behave at much larger scales, using existing chips that we customize.” 

The idea to use so many p-bits came accidentally. Two PhD students from the University of Messina, in Italy, Andrea Grimaldi, who was visiting the Çamsari lab for several months, and Eleonora Raimondo, who was in Messina, began to see non-intuitive favorable behavior while working with very large numbers of p-bits, “It took a year or so to understand and develop a theory for why having lots of p-bits in parallel improves performance in such an unexpected way,” Chowdhury recalls. “Then, we wondered, Can we build it? So, we got more collaborators to work on the paper and their collective opinion was, ‘Yes, in principle, this chip will work.’” 

Chowdhury spent two years on the project. The result was the paper establishing that, when co-designed with hardware to implement powerful Monte Carlo algorithms, a p-computer  provides a compelling and scalable classical (i.e., non-quantum) pathway for solving hard optimization problems. 

Focusing on two key algorithms applied to 3D spin glasses — discrete-time simulated quantum annealing (DT-SQA) and adaptive parallel tempering (APT) — the researchers showed that the p-computer outperformed a leading quantum annealer on the same problems. They demonstrated further, the team writes, that “These algorithms are readily implementable using currently available hardware, suggesting that specialized chips can leverage massive parallelism to accelerate these algorithms by orders of magnitude while drastically improving energy efficiency. Our results establish a new and rigorous classical baseline, clarifying the landscape for assessing a practical quantum advantage.”

“We previously had a chip with tens of thousands of p-bits, but modern semiconductor technology makes it possible to build one that can house millions of p-bits,” Çamsari explains. “By working with chip designers as part of our team and doing simulations, we showed that superior results could be achieved with a 3 million-p-bit chip that TSMC, a chip company in Taiwan, could build today. We simulated this chip and have one-hundred-percent trust in the simulation that shows that it can be made with existing technology.” 

“We had never run a problem that large,” Çamsari notes. “Once we did, the question was whether a quantum machine solves it better than any other approach. The answer, from our research, is no.”

• Nature Communications (Oct 2025) "Pushing the Boundary of Quantum Advantage in Hard Combinatorial Optimization with Probabilistic Computers"
   
• Nature Electronics (Aug 2025) "An integrated-circuit-based probabilistic computer that uses voltage-controlled magnetic tunnel junctions as its entropy source"

Robert Mehrabian COE News  "Probabilistic Computers Keep Winning"