Canadians have created a “copper‑clad” prototype quantum computer for optimization tasks

New Ising photon machine from Queen’s University

Scientists at Queen’s University in Kingston (UK) have developed a programmable photonic platform that operates at room temperature and maintains stability for hours. In essence, it resembles D‑Wave’s quantum computer, which solves combinatorial optimization problems, but differs significantly in cost, reliability, and maintenance expenses.

How the system works
* Optoelectronic generator

The platform uses standard optical telecommunications components: lasers, thin‑film lithium niobate modulators, a semiconductor optical amplifier, and digital signal processing.

* Ising model

Instead of traditional miniature magnets (as in classic Ising machines), the system uses light pulses. Each “virtual spin” is an individual pulse circulating in a loop with control.

* Problem encoding

The combinatorial optimization problem is encoded as a sequence of pulses. After launch, they interact until the system reaches its minimum‑energy state – which becomes the solution to the problem (e.g., the optimal route in the traveling salesman problem).

Technical specifications
Parameter Value Notes
Number of spins 256 Possible connections 65 536 “all‑to‑all”
Performance >200 GOPS (giga‑operations per second) during spin interaction and nonlinear processing

For comparison, modern D‑Wave quantum platforms cost millions of times more and run only a few milliseconds per solution.

Why it matters
* Room temperature – no cryogenic cooling required.

* Long‑term stability – operates for hours, not milliseconds.

* Cost‑effective – far cheaper and easier to maintain than quantum counterparts.

Potential applications
* Route optimization (logistics, transportation)
* Integer factorization
* Protein synthesis and pharmaceutical design
* Cryptography and data security
* Neuromorphic computing

The university’s setup opens the prospect of creating practical, scalable, and energy‑efficient analog computers for a wide range of optimization tasks.