Learning to learn with quantum neural networks via classical neural networks. (arXiv:1907.05415v1 [quant-ph])

Quantum Neural Networks (QNNs) are a promising variational learning paradigm
with applications to near-term quantum processors, however they still face some
significant challenges. One such challenge is finding good parameter
initialization heuristics that ensure rapid and consistent convergence to local
minima of the parameterized quantum circuit landscape. In this work, we train
classical neural networks to assist in the quantum learning process, also know
as meta-learning, to rapidly find approximate optima in the parameter landscape
for several classes of quantum variational algorithms. Specifically, we train
classical recurrent neural networks to find approximately optimal parameters
within a small number of queries of the cost function for the Quantum
Approximate Optimization Algorithm (QAOA) for MaxCut, QAOA for
Sherrington-Kirkpatrick Ising model, and for a Variational Quantum Eigensolver
for the Hubbard model. By initializing other optimizers at parameter values
suggested by the classical neural network, we demonstrate a significant
improvement in the total number of optimization iterations required to reach a
given accuracy. We further demonstrate that the optimization strategies learned
by the neural network generalize well across a range of problem instance sizes.
This opens up the possibility of training on small, classically simulatable
problem instances, in order to initialize larger, classically intractably
simulatable problem instances on quantum devices, thereby significantly
reducing the number of required quantum-classical optimization iterations.

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