Quantum computing can help and now that CERN openlabs current Quantum Technology Initiative comprises dozens of projects across computing, sensing, communication, and theory. Read the case study.CERN has become the newest IBM Quantum Hub, the the CERN-IBM relationship will be closer than ever before. Until now, scientists have been using classical machine learning techniques to analyze raw data captured by the particle detectors, automatically selecting the best candidate events. But we think we can greatly improve this screening process by boosting machine learning with quantum. In particular, exploiting the exponentially large qubit A Hilbert space can be thought of as the state space in which all quantum state vectors live. The main difference between a Hilbert space and any random vector space is that a Hilbert space is equipped with an inner product, which is an operation that can be performed between two vectors, returning a scalar. Read more about linear algebra in the Qiskit TextbookHilbert space, quantum computers should be able to capture quantum correlations in the particle collision datasets more efficiently and accurately than conventional, classical, machine learning algorithms. This ability should lead to a better interpretation of experiments.