AI Breakthrough for Quantum Computing

More hints at links between Quantum and AI methods.

Intel Offers AI Breakthrough in Quantum Computing 
ZDNet  By Tiernan Ray

Researchers at Intel and Hebrew University in Israel have defined an important proof for deep learning, proposing a path forward for computing commonly intractable problems in quantum physics. The proof describes deep learning's unmatched ability to model quantum-computing computations, with the data redundancy occurring in convolutional neural networks (CNNs) and recurrent neural networks (RNNs) critical. Both networks' structure entails an essential "reuse" of information, via multilayer stacking, to achieve more efficient "representation" of things in computing terms. The researchers said, "Our work quantifies the power of deep learning for highly entangled wave function representations, theoretically motivating a shift towards the employment of state-of-the-art deep learning architectures in many-body physics research." The formal proofs of the efficiency of "convolutional arithmetic circuits" and "recurrent arithmetic circuits" form a proof that deep learning strategies can meet quantum entanglement challenges more efficiently. ... "