Universal discriminative quantum neural networks

Understand

Quantum mechanics fundamentally forbids deterministic discrimination of quantum states and processes.

• However, the ability to optimally distinguish various classes of quantum data is an important primitive in quantum information science.
• In this work, we train near-term quantum circuits to classify data represented by non-orthogonal quantum probability distributions using the Adam stochastic optimization algorithm.
• This is achieved by iterative interactions of a classical device with a quantum processor to discover the parameters of an unknown non-unitary quantum circuit.