Understand and bridge gaps between today’s technology and the theoretical limits given by quantum mechanics and information theory. Key focus on theory and proof-of-principle demonstrations working at the discreteness (“graininess”) of quantum mechanics and information theory: [Machine Learning] - Explore the "complexity frontier" of ML by new algorithms, architectures, and coherent physical systems including photonic, quantum, and mixed-signal CMOS systems with world-leading foundries; [Quantum Computing] - Development of large, programmable quantum systems combining individual-qubit control and large numbers of qubits to solve bottlenecks in large-scale quantum control (see DARPA ONISQ, DOE QSA, MITRE MOONSHOT, etc). [Quantum Networks] - Constructing the “quantum information” layer on the internet by new quantum control and noise mitigation methods for large-scale utility (viz. NSF Center for Quantum Networks).