Few challenges are as formidable as building a quantum computer. It’s not just about wiring its components but also making them work together to produce accurate computation results despite the presence of noise that can introduce errors in quantum computations.
While you can write down a quantum computation as an abstract mathematical model, implementing it practically still means adjusting certain parameters, such as microwave pulses or lasers, which you can only do with limited precision. Thus, there would inevitably be a gap between the model you intended to implement and the actual outcome.
Qruise leverages machine learning to narrow this gap. By building a physical model from a quantum computer’s experimental data and comparing it to the intended behavior, Qruise helps physicists and engineers to improve their quantum computers. This also works for other quantum devices, such as quantum sensors, and other fields like photonics.
Founded as a spinoff from Forschungszentrum Jülich in late 2021 by Shai Machnes, Frank Wilhelm-Mauch, Tommaso Calarco, and Simone Montangero, Qruise raised funding from Constructor Capital and went through the Creative Destruction Lab startup program.
Learn more about the future of machine learning for quantum computing and beyond from our interview with the co-founder and CEO, Shai Machnes: Why Did You Start Qruise?
Even before Qruise, my co-founders and I were researching how to control quantum systems. At some point, more academic groups than we could handle as researchers wanted to collaborate with us, so we decided to found a company in late 2021.
Our initial focus was just quantum control, but we soon realized that the very same tools could be applied to quantum sensing or even other domains, like photonics.
When I think of Qruise today, we’re actually building a “machine learning physicist”—a system that can predict and control all kinds of physical processes. I already had this vision more than 15 years ago as a researcher, but I wasn’t able to realize it. With recent advances in computing power and data availability, machine learning has greatly improved, making it possible for us to pursue this vision. READ MORE...
