The world of computing stands at the threshold of a revolution. For decades, scientists have sought alternatives to traditional digital computing, which is constrained by transistor speed, heat generation, and energy consumption. Now, a groundbreaking discovery from the University of Technology Sydney (UTS) and the Rochester Institute of Technology (RIT) is rewriting those limits. Researchers have achieved a major milestone in light-speed analog computing, creating a programmable circuit that uses electromagnetic waves to perform complex mathematical operations at speeds previously thought impossible.
This breakthrough, published in Nature Communications, represents not just an incremental improvement but a transformative step toward a new class of processors that can operate faster, cooler, and more efficiently than any digital system today.
What Is Light-Speed Analog Computing?
At its core, light-speed analog computing leverages the natural properties of electromagnetic waves — such as light and microwaves — to process information. Unlike digital computing, which represents data in binary form (0s and 1s), analog computing uses continuous signals to perform calculations. This approach allows multiple operations to occur in parallel, drastically improving speed and efficiency.
In traditional computers, transistors act as tiny switches, turning on and off to represent digital values. However, each transistor operation consumes energy and produces heat. When billions of these transistors switch billions of times per second, performance is inevitably limited by power and thermal constraints. Analog computing bypasses these bottlenecks by processing information as continuous variations in wave properties such as amplitude, frequency, or phase.
Imagine replacing a relay race — where each digital operation must wait for the previous one to finish — with a symphony, where every instrument (or wave) plays simultaneously. That’s the potential of light-speed analog computing.
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The Science Behind the Breakthrough
This new research focuses on a programmable electronic circuit capable of matrix transformations, a mathematical process vital to machine learning, computer vision, and advanced data processing. Led by Dr. Rasool Keshavarz from UTS and Associate Professor Mohammad-Ali Miri from RIT, the team designed a microwave-integrated circuit that performs analog computations using electromagnetic waves.
Their approach bridges the gap between physics and electronics. Instead of relying on transistors, the system manipulates wave interference patterns to perform computations. By controlling how waves combine or cancel each other, the circuit executes mathematical operations with extraordinary precision — and at the speed of light.
This technology opens the door to programmable analog processors, capable of dynamic reconfiguration much like today’s FPGAs (Field-Programmable Gate Arrays) but without the digital overhead.
How It Works — The Role of Electromagnetic Waves
Electromagnetic waves can carry vast amounts of information through their oscillations. In light-speed analog computing, these waves are directed through circuits with precise geometries and materials that determine how they interact.
When two waves meet, their amplitudes either add up or cancel out, depending on their phase relationship. By designing circuits that control these interactions, scientists can perform operations like matrix multiplication — a fundamental process behind AI, neural networks, and graphics rendering.
The programmable circuit developed by the UTS-RIT team uses microwave frequencies, which offer a balance between controllability and speed. The researchers designed layers that function as power dividers and phase shifters, ensuring that signals can be routed and manipulated with fine precision.
The result is a physical circuit capable of executing hundreds of computations simultaneously, all in real time, and all traveling at the speed of light within the medium.
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Why Analog Computing Matters in 2025 and Beyond
As we approach the physical limits of silicon transistors, the computing industry faces an urgent question: what comes next? Quantum computing offers enormous promise, but scalability and stability remain major hurdles. Light-speed analog computing, on the other hand, is feasible with current technologies and can integrate directly into existing electronic systems.
Energy Efficiency
Analog circuits consume a fraction of the energy required by digital systems because they don’t need billions of transistors switching on and off. This efficiency could drastically reduce the carbon footprint of data centers and AI model training.
Parallelism and Real-Time Processing
Analog computing excels at parallel processing. Tasks such as signal analysis, radar imaging, and real-time decision-making — which require vast simultaneous computations — can benefit tremendously from this approach.
Integration with Existing Infrastructure
Unlike quantum systems that require extreme cooling and isolation, analog computing operates at standard conditions, making it easier to integrate into consumer electronics, communication systems, and industrial equipment.
Applications of Light-Speed Analog Computing
The potential use cases for light-speed analog computing span across industries, from defense to healthcare and telecommunications. Here are some of the most promising fields:
Next-Generation Wireless Networks
Analog processors could enable ultra-fast 6G and 7G networks by managing massive data transfers in real time with minimal latency.
Radar and Space Technologies
In defense and aerospace, analog signal processors can perform instant calculations for target tracking, navigation, and communication in high-frequency domains.
Environmental and Industrial Monitoring
Analog computing could power advanced sensors for real-time data analytics in mining, agriculture, and environmental research, offering faster insights into resource management.
AI and Machine Learning
Matrix computations are the backbone of artificial intelligence. By performing these operations at the speed of light, analog computing could make AI models faster and more efficient while drastically lowering their energy demands.
Healthcare Imaging and Diagnostics
In medical imaging, where large data volumes need rapid analysis, analog systems could improve both speed and clarity in MRI and CT scans.
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How It Differs from Quantum Computing
Dr. Keshavarz and his team emphasize that light-speed analog computing is fundamentally different from quantum computing. Quantum systems rely on qubits — particles that can exist in multiple states simultaneously — to achieve exponential computational power. However, they are notoriously difficult to scale and maintain due to decoherence and environmental noise.
Analog computing, in contrast, uses classical physics principles that are well understood and easier to control. It doesn’t require near-zero temperatures or isolation chambers. Most importantly, it can be commercially viable in the near term, while quantum computing remains largely experimental.
The Future of Computing — Beyond Digital Limits
The success of the UTS and RIT collaboration signals a paradigm shift in how we think about computation. Instead of forcing physical systems to behave digitally, researchers are now learning to exploit nature’s own analog mechanisms for processing information.
This could mark the dawn of hybrid systems — machines that combine the precision and logic of digital processors with the parallelism and efficiency of analog designs. Such systems would bring humanity closer to real-time AI, autonomous scientific exploration, and even onboard processing for space missions, where speed and energy efficiency are paramount.
The Road Ahead — Challenges and Next Steps
While the breakthrough is promising, challenges remain. Scaling analog circuits for mass production, minimizing interference, and integrating with existing semiconductor technologies are major engineering hurdles. However, the research team is optimistic.
Dr. Keshavarz noted that follow-up studies are already in preparation to transition this technology from laboratory prototypes to practical architectures. These next steps involve exploring new materials, photonic integrations, and mixed-signal chips that combine analog and digital logic.
Expert Reactions and Global Impact
Industry experts are calling this one of the most significant advances in computational physics in decades. The ability to compute at light speed while consuming minimal energy could reshape industries dependent on high-performance processing.
Universities and startups are now racing to explore light-speed analog processors for commercial applications, including telecommunications, AI acceleration, and defense technologies. Governments are also investing in analog research, recognizing its strategic importance in the coming data-driven age.
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Conclusion — A New Dawn for Computing
The evolution from digital to analog marks a return to the roots of computation — but this time enhanced by modern science and engineering. Light-speed analog computing could be the missing link between today’s silicon-based world and tomorrow’s ultra-efficient computational universe.
By merging the speed of light with the logic of mathematics, humanity may finally achieve computing that’s as fast and fluid as thought itself.
FAQs
1. What is light-speed analog computing?
It’s a computing method that uses electromagnetic waves to perform calculations at light speed, allowing massive parallel processing with minimal energy use.
2. How does it differ from digital computing?
Digital computing uses binary data and transistors, while analog computing uses continuous signals, enabling faster and more efficient processing.
3. Who developed the recent analog computing breakthrough?
Researchers from the University of Technology Sydney (UTS) and the Rochester Institute of Technology (RIT) led this innovation.
4. What role do electromagnetic waves play?
They carry and process data through phase and amplitude changes, executing mathematical operations at high frequencies.
5. Is light-speed analog computing related to quantum computing?
No. It relies on classical physics and is easier to scale than quantum systems, which depend on qubits and require extreme conditions.
6. What industries can benefit from this technology?
Telecommunications, defense, AI, healthcare, and space exploration are among the primary beneficiaries.
7. How energy-efficient is analog computing compared to digital?
Analog systems can use up to 90% less energy for certain computations, reducing heat generation and operational costs.
8. When can we expect commercial analog processors?
Early prototypes are already functional; widespread commercial deployment may occur within the next 5–10 years.
9. Can analog and digital systems work together?
Yes, hybrid architectures combining both approaches are under development for maximum performance and flexibility.
10. What does this mean for the future of AI and computing?
It could enable real-time AI decision-making, ultra-fast data processing, and the next generation of energy-efficient computational systems.