Challenges and opportunities for hybrid quantum-classical machine learning and optimization

We present an overview of our progress on quantum-inspired and quantum-assisted algorithms for optimization and machine learning at Quantum AI Lab at Google. We develop an end-to end quantum-inspired discrete optimization platform that uses an interplay of local and non-local thermal updates to sample from inaccessible low-energy states of spin-glass systems that encode high-quality solutions of certain hard combinatorial optimization. We introduce several new techniques for quantum circuit learning on Noisy Intermediate-Scale Quantum (NISQ) processors. We show how we can learn to learn on parameterized quantum circuits via classical recurrent neural networks. We apply this metalearning approach for efficient initialization of Quantum Approximate Optimization Algorithm for Sherrington-Kirkpatrick model and variational Quantum Eigensolver for the Hubbard model. Moreover, we introduce two different layerwise learning for quantum neural networks. In the first method we train layer-wise POVMs to perform variational quantum unsampling of unknown noisy quantum operations. In the second method, we are training varying subsets of the quantum circuit's parameters iteratively while increasing the circuit depth to have sufficient representation of classical or quantum data. In our approach the problem of vanishing gradients or barren plateaus of training landscape can be avoided to a large extent. We provide several applications of such quantum models for characterization of NISQ devices and classification of quantum data.