Scalable Architectures for Quantum Computing

There are many challenges scaling up QPUs from the current 20-something qubits and 0.5% entangling-gate error rate. In superconducting QPUs, scale-up will result in a huge number of control lines running from the room-temperature waveform generators into the cryogenic fridge. One possible solution is to use in-fridge single flux quantum waveform generators, which we have explored in. In order to improve gate fidelities on large-scale QPUs, we need fast, efficient and flexible optimal control and calibration methods, which complement analytic design for better gates. And, of course, we must come up with methods of measuring the improved performance, which requires fast & accurate readout.When applicable, we design new superconducting circuits to perform operations faster and more accurately. As QPUs improve, they are capable of running more and more complex algorithms. They way there, however, requires careful understanding of how gate errors affect algorithms, and methods for benchmarking said errors. And it even makes sense to design algorithms which are robust to such noise. All this goes hand-in-hand with a deeper understanding of how complex quantum algorithms map to the lego-like structure of quantum gates and other computational paradigms. Armed with the above insights, we can also design new quantum simulation algorithms. The above breadth of knowledge gained above allows us to provide strategic overviews are the German and European levels.

Last Modified: 20.03.2023