A new hardware advance from researchers at UCLA and UC Riverside could redefine how industries solve large-scale optimisation challenges, with implications for digital construction, telecommunications, logistics, and artificial intelligence.
Tackling combinatorial optimisation challenges
In modern infrastructure and digital construction, decision-making often comes down to solving combinatorial optimisation problems. These are the kinds of complex challenges that determine how best to route deliveries, schedule construction projects, optimise workforce allocation, or streamline telecommunications networks.
Today’s computer hardware, even when combined with advanced AI-driven optimisation tools, faces scaling limitations. Traditional silicon chips are running up against physical density barriers, while the training of AI models requires vast amounts of processing power and energy. This bottleneck has created a need for next-generation computing architectures capable of solving real-world optimisation problems more efficiently.
A physics-inspired approach to computing
Researchers from UCLA and UC Riverside have introduced a breakthrough in physics-inspired computing, demonstrating an Ising machine that uses a network of oscillators rather than standard digital logic gates. Oscillators—components that move back and forth at specific frequencies—are particularly powerful because they can run many calculations in parallel. When synchronised, the oscillators reveal the solution to the optimisation problem.
This innovation, published in Physical Review Applied, harnesses quantum material properties without the usual requirement of cryogenic cooling. Unlike most quantum computing systems that must operate at near absolute zero, the UCLA/UC Riverside prototype runs at room temperature, making it far more practical for integration into existing computing environments.
Bridging quantum mechanics and classical physics
At the heart of the system is tantalum sulfide, a two-dimensional quantum material that allows the machine to toggle between electrical and vibrational phases. This capability effectively bridges the abstract world of quantum mechanics with the physics of everyday life, enabling the oscillators to naturally evolve into a “ground state.” In this state, synchronisation occurs, and the optimisation problem is solved.
Professor Alexander Balandin of UCLA Samueli School of Engineering explained: “Our approach is physics-inspired computing, which leverages strongly correlated electron–phonon condensates to perform computation directly through physical processes. This enables both speed and energy efficiency.”
Implications for digital construction and beyond
For the digital construction sector, the ability to solve optimisation problems at scale is game-changing. From resource scheduling in large infrastructure projects to real-time routing of autonomous construction vehicles, the demand for faster, more efficient computation is only growing.
The new Ising machine could make it possible to:
- Optimise construction project timelines with greater accuracy.
- Improve supply chain efficiency for materials delivery.
- Enhance building energy management systems through advanced simulations.
- Enable more efficient digital twin technology by processing vast amounts of sensor data in real time.
Because the technology can operate at low power and remain compatible with silicon CMOS technology, it also holds potential for deployment at scale across industries beyond construction, including logistics, aerospace, telecommunications, and smart city infrastructure.
Future integration and industry adoption
The research team emphasised that any new computing hardware must be compatible with existing silicon-based systems to achieve commercial adoption. The tantalum sulfide–based design meets this requirement, offering a potential pathway for integration into mainstream computing environments.
The oscillators were fabricated at the UCLA Nanofabrication Laboratory and tested at UCLA’s Phonon Optimized Engineered Materials Laboratory, underscoring the high level of engineering precision required for such breakthroughs.
Funded by the Office of Naval Research and the Army Research Office, this research represents not only a scientific achievement but also a step toward practical, energy-efficient computing platforms that could help address some of the world’s most complex optimisation challenges.
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