Quantum neural networks switch to isometries: half the parameters, and a measurable cost function that trains to 4.55×10⁻⁴
Dissipative quantum neural networks (DQNNs) previously used unitary perceptrons, so each building block could only realize a restricted class of quantum transformations. A new architecture rewrites the perceptrons as isometries and adds ancilla neurons, making each building block quantum-channel universal, able to implement any completely positive trace-preserving map. On a minimal three-qubit network trained with Choi training, the Hilbert-Schmidt distance reaches a mean diamond distance of 4.55×10⁻⁴ over 100 random qubit-qubit channels, while the quantum Chernoff bound and quantum relative entropy reach only 0.102 and 0.386.