Researchers developed and experimentally validated a neural network-based protocol that can efficiently measure quantum entanglement in large systems (up to 100 qubits) using only local measurements, while also predicting future entanglement dynamics beyond the measurement window.
Researchers from Louisiana State University have introduced a smart quantum technology for the spatial mode correction of single photons. The team has exploited the self-learning and self-evolving features of artificial neural networks to correct the distorted spatial profile of single photons.
Chinese researchers have devised a flexible quantum GAN scheme which could accomplish image generation with arbitrarily high-dimensional features, and could also take advantage of quantum superposition to train multiple examples in parallel.
PennyLane, Xanadu’s software for quantum machine learning & optimization of hybrid quantum-classical computations, now has support for Google AI Cirq via the new PennyLane-Cirq plugin!