How far has the research advanced?
Dr. Daniel Scherer: The dichotomy mentioned earlier in relation to hardware – NISQ computers versus universal, error-tolerant and error-correcting quantum computers – can also be found at the algorithm level. While algorithms designed for the virtually ideal hardware of the future have been thoroughly researched and promise verifiable quantum advantages, far less research has been done into NISQ algorithms, and from a theoretical perspective, there is often zero or at best a very weak indication of potential quantum advantages. The past few years have yielded a better understanding – especially in the field of QML – of the properties of what are known as variational quantum algorithms, which essentially are tailored to NISQ computers and are also the focus of our research. But it’s not yet possible to make any definite statements regarding the quantum advantages they might hold. We keep a very close eye on how academic research is developing, and we’re in regular contact with our university partners. Key aspects of our own research include the coding of classical data – data indispensable for ML – within quantum algorithms, and the reading of information. This is where we see one of the best opportunities to boost the performance of QML algorithms. Going forward, we want to introduce automation and scalability to the design of coding and decoding strategies that are based on quantum information theory, as well as to other aspects of quantum algorithm design. There are also challenges to overcome when it comes to software engineering. The reproducibility of results, access models on quantum hardware, optimized job scheduling, optimized utilization of quantum resources, and many other factors play a decisive role in developing our methods.