Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on developments in AI. However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space.
Electric-field noise in proximity to metallic surfaces is a poorly understood phenomenon that appears in different areas of physics. Trapped ion quantum information processors are particularly susceptible to this noise, leading to motional decoherence, which ultimately limits the fidelity of quantum operations. On the other hand, trapped ions present an ideal tool to study this effect, opening new possibilities in surface science. In this work we analyze and measure the polarization of the noise field in a microfabricated ion trap for various noise sources. We find that technical noise sources and noise emanating directly from the surface give rise to different degrees of polarization, which allows us to differentiate between the two noise sources. Based on this we demonstrate a method to infer the magnitude of surface noise in the presence of technical noise.
Random numbers are important in diverse areas of science and technology. The requirements of randomness, however, are more stringent in cryptographic applications, where so-called true randomness is required. In particular, statistical randomness is not enough, and a degree of unpredictability stemmed in physical phenomenon is used to build a true random number generator (TRNG). A critical part in TRNG design is the entropy harvester, which extracts entropy from multiple entropy sources. Unfortunately, none of such sources provide true randomness in pure form, and the desired signal is heavily contaminated due to electrical coupling, electromagnetic interference, and other pseudo-random signals present in physical environment. Process variation and ageing affect the performance of noise sources. In this paper we propose an entropy extraction and estimation mechanism, which operates directly on raw randomness exhibited by analogue noise sources. The main strength of our design is its dynamic nature, i.e., it adapts as per the current state of source, and provides a robust mechanism to detect entropy failure. Our design can easily be implemented on commodity hardware in a cost effective manner.