Photonics Research
Using light-based computing to tackle complex challenges
January 21, 2026
Share
Bhavin Shastri, professor in the Department of Physics, Engineering Physics, and Astronomy, centre, and PhD students Hugh Morison, left, and Nayem Al Kayed, have developed a powerful computer, based on the Ising model, that can perform billions of operations per second, using light, off-the-shelf components, and operates at room temperature.
A team of researchers at Queen鈥檚 University has developed a powerful new kind of computing machine that uses light to take on complex problems such as protein folding (for drug discovery) and number partitioning (for cryptography). Built from off-the-shelf components, it also operates at room temperature and remains remarkably stable while performing billions of operations per second.
This breakthrough shows that it is possible to build a practical and scalable machine that can tackle extremely difficult problems.
The project, led by , Canada Research Chair in Neuromorphic Photonic Computing and professor in the Department of Physics, Engineering Physics, and Astronomy, with a team of his graduate students including Nayem Al Kayed and Hugh Morison, uses commercially available lasers, fibre optics, and modulators 鈥 the same technology that powers today鈥檚 internet infrastructure. The team partnered with McGill University researcher David Plant and his graduate student Charles St-Arnault.
The research was 鈥 one of the world鈥檚 most prestigious scientific journals.
Since the machine operates at room temperature it consumes significantly less energy than other advanced computing systems.
Throughout testing, the Shastri Lab鈥檚 machine has proven to be stable for long periods, operating for hours at a time, which makes it well-suited for solving problems that require repeated steps.
A century-old concept, reimagined
The Queen鈥檚 processor is based on the Ising model, which represents problems as interacting magnets with 鈥渟pins鈥 that point up or down. Much like how magnets naturally align when brought closer, the Ising searches for the lowest-energy state 鈥 mathematically equivalent to finding the best solution to a difficult optimization problem. Though simple, the model is powerful for solving problems with many interconnected binary (up/down or yes/no) choices.
The Queen鈥檚 system instead uses pulses of light that act like the magnets 鈥 but instead of a binary system of up or down, there is either a light pulse, or the absence of one. The pulses move through a loop, interact, and gradually settle into a configuration that represents a good solution, much like a group reaching a consensus after many quick exchanges.
鈥淚t鈥檚 a way to turn light into a problem solver,鈥 Dr. Shastri says.
Light solves complex problems by exploring an energy landscape in search of equilibrium.
The hidden challenge behind everyday decisions
If you鈥檝e ever waited for a package to arrive, you鈥檝e experienced a tiny piece of an extremely difficult problem. Online sales and logistics companies must figure out the best route for millions of packages delivered each day. Depending on the number of stops, the total number of possible routes increases quickly. Finding the best one becomes more and more difficult.
鈥淲ith five stops, there are only 12 possible routes. With 10 stops, there are 180,000. With 20 stops, there are more than 60 million billion options. Increase the number to 50, and checking every possibility would take longer than the age of the universe,鈥 Dr. Shastri explains.
This kind of challenge 鈥 picking the best option from a huge number of choices 鈥 is called an optimization problem. Along with supply chains it also shows up in drug design, urban planning, and many other areas. At present, most advanced computers, including quantum machines, have a hard time matching the Shastri Lab鈥檚 computer in terms of optimization.
Built from familiar technology at room temperature
What differentiates the Queen鈥檚 machine most from its peers is its combination of simplicity and performance. The team used just five basic components to build a machine that achieved 256 spins, outperforming similar commercial efforts with billions of dollars in funding. The system鈥檚 incredible stability also enables it to explore more complex problems than other optical Ising machines, whose spins often collapse after milliseconds.
The system also integrates techniques traditionally used in computer systems that move internet data reliably over long distances. It runs at room temperature and remains stable long enough to tackle problems with tens of thousands of variables. Earlier attempts at similar systems often required extremely cold temperatures or specialized materials and could only operate for short periods of time. Running at ordinary temperatures matters because it uses less energy and makes the technology more practical and scalable.
A practical path forward
The Shastri Lab team continues to work on improving the system. Key next steps include scaling-up, system integration, increasing the number of spins, and enhancing energy and cost efficiency. They will also be looking to develop pilot projects with industry partners to apply this new technology in real-world applications.
