# Dissipative Quantum Neural Network

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.

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.