qruise, a Saarbrucken-based quantum startup founded in 2021, develops software that helps scientists and researchers use Machine Learning (ML) tools in everyday scientific workflows. Its first offering overcomes the growing difficulty of controlling the current generation of noisy quantum computers (NISQ devices) and delivers improvements in operational precision through the use of ML & Quantum Optimal Control.
Solid Q is a collaborative project of 25 German institutions developing a quantum computer with enhanced error correction. Consortium —the largest of its kind in Germany— aims to contribute to making Germany a world leader in quantum technology.
“This is a startup that comes from a previous project called OpenSuperQ,” said Machnes when asked about Qruise’s origins. Machnes is also one of the main people in charge of center control and firmware stack in the QSolid project. “QSolid takes care of device characterization controls, a bit of a cloud interface, things like that.”
Machnes went on to say that quantum computers are not digital but analog devices, and unfortunately the rate at which experts know how to create superconducting qubits today means they are like snowflakes.
“Each one is very slightly different,” he continues, “so you need to adjust the controls for each individual qubit. You need to measure each qubit. Every operation you want to perform has to be specifically measured and calibrated.”
Machnes explains Qruise creates machine learning physicists who essentially join a team of physicists in the lab, build devices and handle this kind of work for them.
“In our overall work package we have a control electronics manufacturer, namely Zurich Instruments, Qruise which takes care of the software, and then PGI-8, the Tommaso Calarco institute in Julich, which takes care of the theory,” said Mach.
Machnes is acutely aware that quantum computers are incredibly complex devices, and while he admits that scientists know how to build them, the real community doesn’t fully understand what’s going on, meaning that industry experts don’t fully understand the source of the limitations. and the challenges facing the sector today.
“Our operation has about a half or a quarter percent error at the moment, but we don’t fully understand why,” continued Machnes. “Understanding why is a big challenge, because once we do, we know where to focus our efforts and improve it in the next iteration of the hardware. This is how we make progress.”
Qruise’s CEO says they have an early version of the software already running.
“There is no QSolid hardware yet, but the simulator, the algorithm is there and people can start using it, learn, you know, if anything extra is needed,” said Maches. “And then when the hardware comes in, we can deploy it to the hardware right away.”
Featured image: QSolid