Speaker
Description
High Energy Physics experiments generate increasingly large and complex datasets, ranging from detector hits and particle tracks to reconstructed particles and full collision events. In the current NISQ era, quantum computing is not yet expected to outperform classical approaches for most practical applications; however, it offers novel methods for representing and processing high-dimensional information through quantum data encodings.
This talk presents several High Energy Physics use cases to illustrate how quantum encoding techniques can capture correlations and geometric structures present in experimental data while providing compact representations of complex feature spaces.
The discussion will focus on the opportunities and challenges of quantum data encodings for scientific machine learning, highlighting their potential role in future quantum algorithms for increasingly complex High Energy Physics analyses. Particular attention will be given to how quantum representations may contribute to addressing scalability challenges as detector complexity and data volumes continue to grow.