Machine Learning in a Post Moore’s Law World: Quantum vs. Neuromorphic Substrates

Although machine learning currently relies on conventional computer architectures, the looming end of Moore's Law necessitates exploration of novel computational platforms. Neuromorphic and quantum systems are a natural path to pursue; biological neurons are incredibly efficient, and quantum mechanics provides theoretical foundations for fast solutions to optimization problems. Here, we make the first comparison of emerging hardware (D-Wave quantum annealer and Intel Loihi spiking processor) on an identically-posed machine learning problem. We implement the bioinspired Locally Competitive Algorithm (LCA) for solving sparse coding on the different substrates. To make the comparison valid, our dataset of choice (Fashion MNIST) is dimensionally-reduced via sparse principal component analysis, under the constraint that both classification performance and a graph-based clustering metric remain unchanged. This enables the problem to be mapped identically to both devices. An analysis of several metrics, including power consumption, reconstruction, and classification accuracy are presented. When given the same specifically-constructed challenge, both substrates perform similarly. Our results suggest while neuromorphic and quantum systems are still in their infancy, they present a possible route to address certain types of classically challenging problems, such as sparse coding, in a way that leverages the unique aspects of the substrates.