Quantum Computation with Machine-Learning-Controlled Quantum Stuff
This addresses the challenge of implementing quantum computation on diverse hardware platforms, though it appears incremental as it builds on existing quantum circuit and tomography concepts.
The paper tackles the problem of performing quantum computation with arbitrary physical systems by showing how to interpret their input-output behavior as quantum circuits and using machine learning to identify control settings that implement universal quantum gates, enabling arbitrary quantum programs.
We describe how one may go about performing quantum computation with arbitrary "quantum stuff", as long as it has some basic physical properties. Imagine a long strip of stuff, equipped with regularly spaced wires to provide input settings and to read off outcomes. After showing how the corresponding map from settings to outcomes can be construed as a quantum circuit, we provide a machine learning algorithm to tomographically "learn" which settings implement the members of a universal gate set. At optimum, arbitrary quantum gates, and thus arbitrary quantum programs, can be implemented using the stuff.