Seminar: Reliable quantum kernel classification using fewer circuit evaluations

​Seminar with Professor Chiranjib Bhattacharyya, chair of the Department of Computer Science and Automation at the Indian Institute of Science in Bangalore, India.

​Quantum kernel methods are a candidate for quantum speed-ups in supervised machine learning. The number of quantum measurements N required for a reasonable kernel estimate is a critical resource, both from complexity considerations and because of the constraints of near-term quantum hardware. We emphasize that for classification tasks, the aim is accurate classification and not accurate kernel evaluation, and demonstrate that the former is more resource efficient. In general, the uncertainty in the quantum kernel, arising from finite sampling, leads to misclassifications over some kernel instantiations. We introduce a suitable performance metric that characterizes the robustness or reliability of classification over a dataset, and obtain a bound for N which ensures, with high probability, that classification errors over a dataset are bounded by the margin errors of an idealized quantum kernel classifier. Using techniques of robust optimization, we then show that the number of quantum measurements can be significantly reduced by a robust formulation of the original support vector machine. We consider the SWAP test and the GATES test quantum circuits for kernel evaluations, and show that the SWAP test is always less reliable than the GATES test for any N. Our strategy is applicable to uncertainty in quantum kernels arising from {\em any} source of noise, although we only consider the statistical sampling noise in our analysis.

Bio
 
Chiranjib Bhattacharyya is currently  Professor and the chair of the Department of Computer Science and Automation, Indian Institute of Science.  His research interests are in foundations of Machine Learning, Optimisation and their applications to Industrial problems.
 
He has authored numerous papers in leading journals and conferences in Machine Learning. Some of his results have won best paper awards.
 
He joined the Department of CSA, IISc, in 2002 as an Assistant Professor. Prior to joining the Department he was a postdoctoral fellow at UC Berkeley. He holds BE and ME degrees, both in Electrical Engineering, from Jadavpur University and the Indian Institute of Science, respectively, and completed his PhD from the Department of Computer Science and Automation, Indian Institute of Science.
 
He is also a fellow of Indian Academy of Engineering.

The event is hybrid:  zoom-link, password: mondays23 ​
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Category Seminar
Location: Analysen, meeting room, EDIT trappa D, E och F, Campus Johanneberg
Starts: 05 December, 2022, 14:00
Ends: 05 December, 2022, 15:00

Page manager Published: Mon 24 Oct 2022.