Artificial intelligence (AI) advancements over the past few years have had an<br>unprecedented and revolutionary impact across everyday application areas. Its<br>significance also extends to technical challenges within science and<br>engineering, including the nascent field of quantum computing (QC). The<br>counterintuitive nature and high-dimensional mathematics of QC make it a prime<br>candidate for AI's data-driven learning capabilities, and in fact, many of QC's<br>biggest scaling challenges may ultimately rest on developments in AI. However,<br>bringing leading techniques from AI to QC requires drawing on disparate<br>expertise from arguably two of the most advanced and esoteric areas of computer<br>science. Here we aim to encourage this cross-pollination by reviewing how<br>state-of-the-art AI techniques are already advancing challenges across the<br>hardware and software stack needed to develop useful QC - from device design to<br>applications. We then close by examining its future opportunities and obstacles<br>in this space.