Supervised quantum gate "teaching" for quantum hardware design
arXiv:1607.06146v11 citations
Originality Incremental advance
AI Analysis
This addresses the challenge of designing quantum hardware efficiently for researchers in quantum computing, though it appears incremental.
The authors tackled the problem of training a quantum network to implement a target quantum algorithm with minimal external classical control, achieving a method for physical quantum computer construction.
We show how to train a quantum network of pairwise interacting qubits such that its evolution implements a target quantum algorithm into a given network subset. Our strategy is inspired by supervised learning and is designed to help the physical construction of a quantum computer which operates with minimal external classical control.