Chemical simulation — Extract information about chemical structures and dynamics with potential applications to material science, computational chemistry, computational biology, and drug discovery.

Quantum matter simulation — Model and design high temperature superconductivity or other exotic states of matter which exhibits many-body quantum effects.

Quantum control — Hybrid quantum-classical models can be variationally trained to perform optimal open or closed-loop control, calibration, and error mitigation. This includes error detection and correction strategies for quantum devices and quantum processors.

Quantum communication networks — Use machine learning to discriminate among non-orthogonal quantum states, with application to design and construction of structured quantum repeaters, quantum receivers, and purification units.

Quantum metrology — Quantum-enhanced high precision measurements such as quantum sensing and quantum imaging are inherently done on probes that are small-scale quantum devices and could be designed or improved by variational quantum models.