Researchers at VTT Technical Research Centre unlock chip-based thermionic cooling for quantum computers

Researchers from a team at the VTT Technical Research Centre of Finland designed a vacuum-tube-like device that allows for cooling to happen in a purely electronic way – a possible road toward slashing cooling costs for dilution-refrigerated quantum computers by a factor of ten. In their experiments, the researchers found their design allowed for temperatures to drop by as much as 40%. Quantum News Briefs summarizes the September 17 article by Francisco Pires in which he describes this research.
One of the fundamental limits to any high-level computation is cooling capacity – the ability to draw computationally generated heat away from operating circuits. This particular limit is seen everywhere these days. But quantum computers are even more sensitive than traditional electronics – they’re more prone to outside interference, and are more fickle as to what types of interference can collapse their useful, working qubit states. So new techniques that allow for simpler, more efficient cooling are much in need.
The Finnish scientists at VTT developed a thermionic device that sheds heat in the form of electrons (channeling electrons requires energy, which is why thermionic devices taking advantage of the Peltier effect usually introduce yet another energy-consumption step). But crucially, this device allows cooling to be taken almost to its extreme: the researchers expect to be able to cool electronics down to a range between 1.5 K and 0.1 K – more than sufficient to serve as a fundamental cooling mechanism for “absolute-zero” computing. And this technique should be much smaller, less expensive, and less prone to errors from both a logistical and operational standpoint compared to fluid-based cooling.
“Our technology could help the industry scale down overall quantum computer system size,” said Mika Prunnila at the VTT Technical Research Center of Finland, in Espoo. Click here to read the original article in-entirety in Tom’s Hardware,

Bavaria’s REPLY project to bring practical usability of quantum computing in industrial applications

Reply is part of the research project”Bench-QC, which stands for application-driven benchmarking of quantum computing. The focus of the project, which is funded by the Bavarian State Ministry of Economic Affairs, Regional Development and Energy, is the practical usability of quantum computing in industrial applications. The initiative, consisting of industrial partners and Fraunhofer institutes, specifically identifies the applications for which quantum computing can provide a benefit to companies.
The goal of the Bench-QC project is to investigate when quantum computers produce better results than classic high-performance computers. Reply has the role of consortium leader and is bringing in already implemented use cases in various industries in the field of quantum-inspired optimization.
Filippo Rizzante, CTO at Reply, commented: “We are very excited to bring quantum computing into practice. With this initiative, we want to bring academic discussions to the business world and show companies how they can use quantum computing commercially and the benefits they can derive from it. That is why we have developed quantum-inspired algorithms that allow us to describe and optimize complex situations, giving companies time and cost savings”.
In addition to Reply, the project partners include BMW, and the two Fraunhofer institutes for Integrated Circuits IIS, and for Cognitive Systems IKS. The lighthouse project, scheduled for three years, is part of the Munich Quantum Valley of the Bavarian state government, and aims to establish expertise in quantum computing and quantum technologies in Bavaria.
Reply has the role of consortium leader and is bringing in already implemented use cases in various industries in the field of quantum-inspired optimization. Reply has developed an efficient classical solver for QUBO (quadratic unconstrained binary optimization) problems: the Reply tool “MegaQUBO” finds solutions for tens of thousands of variables within seconds. Reply’s reference implementation for hybrid neural networks is also included in the benchmarking project. Click here to read complete original announcement.