Abstract Machine learning (ML) has found broad applicability in quantum information
science in topics as diverse as experimental design, state classification, and even studies on …

Cybersecurity threats are continuously evolving, requiring innovative protection strategies to
build resilient cyber infrastructure. Artificial intelligence (AI) and machine learning offer …

Quantum networks exploit the unique properties of quantum mechanics to enable
communication and networking tasks unavailable to existing distributed classical systems …

In this work, we show a correspondence between linear support vector machines (SVMs)
and entanglement witnesses, and use this correspondence to generate entanglement …

We introduce an approach for performing quantum state reconstruction on systems of n
qubits using a machine learning-based reconstruction system trained exclusively on m …

We propose a support vector machine (SVM) based approach for generating an
entanglement witness that requires exponentially less training data than previously …

We demonstrate machine-learning-enhanced Bayesian quantum state tomography on near-
term intermediate-scale quantum hardware. Our approach to selecting prior distributions …

We use an artificial neural network (ANN) model to identify the entanglement class of an
experimentally generated three-qubit pure state drawn from one of the six inequivalent …

Quantum network users require knowledge of their entangled photon source performance,
the types of noise photons that are output by their source, and the regimes that their sources …

Machine learning has evolved from a niche topic to a subject that strongly influences all of
our lives in recent years. In addition to paradigm-shifting developments in machine learning …