Beyond Moore's law: towards competitive quantum devices

Raymond G. Beausoleil, David A. Fattal, Marco Fiorentino B +12 more

Moore's Law has set great expectations that the performance/price ratio of commercially available semiconductor devices will continue to improve exponentially at least until the end of the next decade. Although the physics of nanoscale silicon transistors alone would allow these expectations to be met, the physics of the metal wires that connect these transistors will soon place stringent limits on the performance of integrated circuits. We will describe a Si-compatible global interconnect architecture - based on chip-scale optical wavelength division multiplexing - that could precipitate an "optical Moore's Law" and allow exponential performance gains until the transistors themselves become the bottleneck. Based on similar fabrication techniques and technologies, we will also present an approach to an optically-coupled quantum information processor for computation beyond Moore's Law, encouraging the development of practical applications of quantum information technology for commercial utilization. We present recent results demonstrating coherent population trapping in single N-V diamond color centers as an important first step in this direction.

K. Ismail, S Bandyopadhyay, Jean‐Pierre Leburton

James R. Powell

Moore's law has accurately predicted the growth of computing power from the increasing density of transistor devices on integrated circuit (IC) chips. Many believe this trend will continue indefinitely; however, quantum physics may define a future limit to electronic miniaturization and ever-increasing computing power. Heisenberg uncertainty in terms of the Compton wavelength of particle physics suggests such a boundary, even for anticipated advances in quantum computing. Moore's law has been a reliable predictor of the pace of electronic technology advancement; however, quantum physics may provide the ultimate limit to this mathematical model.

Kyle Henke, Garrett T. Kenyon, Ben Migliori

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.