The pursuit of efficient computation is a driving force in modern technology, and increasingly, researchers are turning to the principles of physics to overcome the limitations of traditional computer architectures. Combinatorial optimization problems—those involving finding the best solution from a vast number of possibilities—are ubiquitous in fields ranging from logistics and finance to drug discovery and materials science. Conventional computers often struggle with these problems as their complexity grows, leading to a search for alternative computational paradigms. This has spurred significant interest in physics-inspired computing, a field that leverages physical systems and phenomena to tackle these challenging tasks.
One prominent approach involves mimicking physical systems like spin glasses using what are known as Ising machines. These machines, built with networks of oscillators—including those utilizing materials like tantalum sulfide—operate on principles analogous to quantum mechanics, even at room temperature. The advantage lies in their ability to naturally explore a vast solution space simultaneously, potentially finding optimal or near-optimal solutions much faster and with greater energy efficiency than classical algorithms. Recent advancements demonstrate the feasibility of this approach, with systems capable of solving complex scheduling and routing problems with remarkable speed. Beyond Ising machines, researchers are exploring acoustic systems designed to function as Ising machines, further diversifying the physical platforms for this type of computation.
The inspiration doesn’t stop at quantum-like systems. Bio-inspired algorithms, such as AmoebaSAT, draw from the adaptive strategies found in nature. AmoebaSAT, for example, tackles the satisfiability (SAT) problem—a notoriously difficult optimization challenge—by mimicking the foraging behavior of amoebas. Similarly, particle swarm optimization (PSO), inspired by the flocking of birds, is being used in inverse-design methods to enhance the performance and reliability of integrated circuits. These algorithms demonstrate the power of translating natural processes into effective computational strategies. Furthermore, the concept extends to utilizing the inherent properties of physical infrastructure itself. Researchers are investigating the potential of global optical fiber networks as massive, untapped computing resources, leveraging their ability to act as an “optical oracle” for solving complex problems.
The integration of physics into computer architecture also manifests in the development of physics-based ASICs (Application-Specific Integrated Circuits). These chips are designed to both train themselves and perform computations, exploiting resistive networks and other physical properties to achieve efficiency. The Fujitsu Digital Annealer is a prime example, specifically engineered to solve quadratic unconstrained binary optimization (QUBO) problems. This approach, alongside the development of photonic Ising machines—which mimic the Ising model using light—represents a significant departure from traditional von Neumann architectures. The use of photonic computing, where information is processed using photons rather than electrons, offers the potential for dramatically increased speed and reduced energy consumption. Moreover, the field is seeing the emergence of physics-informed neural networks (PINNs), which embed physical laws directly into the learning process, enhancing the accuracy and efficiency of machine learning models. These networks are proving valuable in areas like scientific computing, particularly in fluid and solid mechanics, where accurate modeling of physical phenomena is crucial. Even the architecture of these networks is being optimized, with causality-aware dynamic convolutional neural operators designed to respect the fundamental constraints of space and time.
The exploration of quantum computing also falls under this umbrella, though it represents a more radical departure from classical computation. While still in its early stages, quantum computers have demonstrated the ability to solve certain combinatorial optimization problems, like the traveling salesman problem, more efficiently than classical methods. Recent breakthroughs include the demonstration of a quantum speed-up in optimization problems using neutral-atom quantum processors, and the development of scalable, fully coupled quantum-inspired processors. Genetic algorithms are even being employed to improve the performance of quantum simulations, further bridging the gap between classical and quantum computing.
The convergence of physics and computation is not merely about building new hardware; it’s about fundamentally rethinking how we approach problem-solving. Researchers are developing novel numerical solvers inspired by quantum principles, and exploring the use of charge-density-wave quantum oscillator networks to tackle complex optimization challenges. This interdisciplinary field, highlighted by collections like the one on Physics-Inspired Computing in *Physical Review Applied*, is rapidly evolving, offering promising solutions to the escalating “compute crisis” driven by the demands of artificial intelligence and other computationally intensive applications. The future of computing may well lie in harnessing the power of the physical world to overcome the limitations of traditional silicon-based systems, paving the way for breakthroughs in a wide range of scientific and engineering disciplines.
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