Quantum generative adversarial learning in a superconducting quantum circuit

Machine learning (1), or more broadly artificial intelligence (2), represents an important area with general practical applications where near-term quantum devices may offer a substantial speedup over classical ones. With this vision, an intriguing interdisciplinary field of quantum machine learning/artificial intelligence has emerged and attracted tremendous attention in recent years (3, 4). A number of quantum algorithms that promise exponential speedups have been theoretically proposed (3–6), and some were demonstrated in proof-of-principle experiments (7, 8). Yet, in most of these previous scenarios, the input datasets considered are typically classical. As a result, certain costly processes or techniques, such as quantum random access memories (9), are required to first map the classical data to quantum wave functions so as to be processed by quantum devices, rendering the potential speedups nullified (10).