Training and meta-training an ensemble of binary neural networks with quantum computing

Abstract

The design of NNs requires an expensive search in the space of the neural network architecture. This search performs heuristics called Neural Architecture Search (NAS), but an efficient algorithmic NAS remains an open problem. The development of quantum computing allows the use of quantum features, without a classical counterpart, as a proposal to solve some problems more efficiently. NAS through quantum algorithms is one such proposal, although none of the solutions presented so far is suitable for Noisy Intermediate Scale Quantum (NISQ) devices due to the complexity of the algorithms. This work reduces the use of quantum resources, which increases the possibility of running NAS on a NISQ device. The proposed method does not rely on random weights initialization and allows training a set of architectures simultaneously. The trained quantum model corresponds to an ensemble of classical ANNs, represented by a quantum superposition of an exponential number of ANNs.