UKRI: Integration of AI/HPC with quantum computing
Funding for collaborative projects that integrate quantum computing into workflows using high-performance computing and artificial intelligence. Projects should address practical bottlenecks and demonstrate benefits of quantum-classical approaches. Projects can last three years from a fixed start of 1 June 2027. The maximum project full economic cost is £3.75 million, with EPSRC funding 80%. Applications close at 16:00 UK time on 3 November 2026.
Superconducting qubit control on millisecond timescales: from rapid feedback to new qubit dynamics
Seminar by Morten Kjaergaard (Niels Bohr Institute, University of Copenhagen) on rapid FPGA-based feedback for superconducting qubits, including sparse-sampling techniques and on-FPGA inference enabling millisecond-timescale T1 estimation, ~100 ms readout optimization, and over 74,000 consecutive recalibrations in closed-loop operation.
Robust characterization of average gate set noise and cross-talk using gate-set shadows
Jadwiga Wilkens of Johannes Kepler University Linz presents gate-set-shadow methods for characterising noise on a 36-qubit superconducting processor. The seminar examines reconstructed noise channels, correlated errors and cross-talk, including limitations of standard noise models. The confirmed Quantum Information seminar is scheduled for 9 September 2026, from 11:00 to 12:30 Toronto time in Perimeter Institute's Bob Room.
Can machine learning learn new physics, or do we need to put it in by hand?"\
There has been a surge of publications on using machine learning (ML) on experimental data from physical systems: social, biological, statistical, and quantum. However, can these methods discover fundamentally new physics? It can be that their biggest impact is in better data preprocessing, while inferring new physics is unrealistic without specifically adapting the learning machine to find what we are looking for — that is, without the “intuition” — and hence without having a good a priori guess about what we will find. Is machine learning a useful tool for physics discovery? Which minimal knowledge should we endow the machines with to make them useful in such tasks? How do we do this? Eight speakers below will anchor the workshop, exploring these questions in contexts of diverse systems (from quantum to biological), and from general theoretical advances to specific applications. Each speaker will deliver a 10 min talk with another 10 minutes set aside for moderated questions/discussion. We expect the talks to be broad, bold, and provocative, discussing where the field is heading, and what is needed to get us there.