Artificial intelligence (AI) advancements over the past few years have had anunprecedented and revolutionary impact across everyday application areas. Itssignificance also extends to technical challenges within science andengineering, including the nascent field of quantum computing (QC). Thecounterintuitive nature and high-dimensional mathematics of QC make it a primecandidate for AI's data-driven learning capabilities, and in fact, many of QC'sbiggest scaling challenges may ultimately rest on developments in AI. However,bringing leading techniques from AI to QC requires drawing on disparateexpertise from arguably two of the most advanced and esoteric areas of computerscience. Here we aim to encourage this cross-pollination by reviewing howstate-of-the-art AI techniques are already advancing challenges across thehardware and software stack needed to develop useful QC - from device design toapplications. We then close by examining its future opportunities and obstaclesin this space.