Deep recurrent networks predicting the gap evolution in adiabatic quantum computing

Abstract

In adiabatic quantum computing finding the dependence of the gap of the Hamiltonian as a function of the parameter varied during the adiabatic sweep is crucial in order to optimize the speed of the computation. Inspired by this challenge, in this work, we explore the potential of deep learning for discovering a mapping from the parameters that fully identify a problem Hamiltonian to the aforementioned parametric dependence of the gap applying different network architectures. Through this example, we conjecture that a limiting factor for the learnability of such problems is the size of the input, that is, how the number of parameters needed to identify the Hamiltonian scales with the system size. We show that a long short-term memory network succeeds in predicting the gap when the parameter space scales linearly with system size. Remarkably, we show that once this architecture is combined with a convolutional neural network to deal with the spatial structure of the model, the gap evolution can even be predicted for system sizes larger than the ones seen by the neural network during training. This provides a significant speedup in comparison with the existing exact and approximate algorithms in calculating the gap.

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Cited by in corpus (6)

• Challenges and Opportunities in Quantum Optimization
• Deep learning of many-body observables and quantum information scrambling
• Deep learning optimal quantum annealing schedules for random Ising models
• Synergy between noisy quantum computers and scalable classical deep learning
• Adiabatic Dynamics of Entanglement
• Challenges and opportunities in the supervised learning of quantum circuit outputs