DQC Seminar Series: Entanglement-enhanced learning of quantum processes

Abstract: Entanglement is a distinctive feature of quantum mechanics, yet precisely characterizing its advantages in quantum information processing can be challenging. In this talk, we demonstrate that quantum entanglement can provide exponential speedup in learning physical processes, on both discrete-variable and continuous-variable quantum systems. We show such advantages persist even under realistic imperfections, and experimentally demonstrate the advantages on two different platforms. Concretely, the first result is about learning n-qubit Pauli channels, with applications in scalable noise characterization of superconducting qubits; The second result is about learning n-mode bosonic random displacement channels, with demonstrations on a quantum photonic platform. Our work highlights a new paradigm of practical quantum advantages and provides new insights on the usefulness of quantum entanglement as a resource. Bio: Senrui Chen is an IQIM Postdoctoral Scholar at Caltech. He received his Ph.D. in Quantum Science and Engineering from the University of Chicago in 2025. He works on quantum information theory, focusing on quantum learning, quantum metrology, and quantum noise characterization, with an interest in applications to experimental platforms. --- Upcoming seminars: 30 Oct: Aziza Suleymanzade 20 Nov: Jeff Thompson 04 Dec: Felix Knollman