By rewriting dissipative quantum neural networks with isometries and ancilla layers, the authors derive a parameter-efficient architecture whose building blocks are universal quantum channels, then measure how eight cost functions affect training.
Com- puting the distance between quantum chan- nels: usefulness of the Fano representation
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The Impact of Architecture and Cost Function on Dissipative Quantum Neural Networks
By rewriting dissipative quantum neural networks with isometries and ancilla layers, the authors derive a parameter-efficient architecture whose building blocks are universal quantum channels, then measure how eight cost functions affect training.