Demonstration of quantum advantage in machine learning

The main promise of quantum computing is to efficiently solve certain problems that are prohibitively expensive for a classical computer. Most problems with a proven quantum advantage involve the repeated use of a black box, or oracle, whose structure encodes the solution. One measure of the algorithmic performance is the query complexity, i.e., the scaling of the number of oracle calls needed to find the solution with a given probability. Few-qubit demonstrations of quantum algorithms, such as Deutsch–Jozsa and Grover, have been implemented across diverse physical systems such as nuclear magnetic resonance, trapped ions, optical systems, and superconducting circuits. However, at the small scale, these problems can already be solved classically with a few oracle queries, limiting the obtained advantage. Here we solve an oracle-based problem, known as learning parity with noise, on a five-qubit superconducting processor. Executing classical and quantum algorithms using the same oracle, we observe a large gap in query count in favor of quantum processing. We find that this gap grows by orders of magnitude as a function of the error rates and the problem size. This result demonstrates that, while complex fault-tolerant architectures will be required for universal quantum computing, a significant quantum advantage already emerges in existing noisy systems. Large advantage in small quantum computers Quantum computing promises to revolutionize all fields of science by solving problems that are too complex for conventional computers. However, the realization of a full-fledged, universal quantum computer is still far ahead, requiring millions of quantum bits and very low error rates. Despite this, D. Ristè and colleagues at Raytheon BBN Technologies, with collaborators at IBM, have demonstrated that a quantum advantage already appears with only a few quantum bits and a highly noisy system. The team solved a particular problem, known as learning parity with noise, using a five-qubit superconducting quantum processor. Counting the number of times that the processor runs, they demonstrate that the implemented quantum algorithm finds the solution much faster than by classical methods