Demonstration of quantum advantage in machine learning

TL;DR

This paper demonstrates a quantum advantage in solving a learning parity with noise problem using a five-qubit superconducting processor, showing a significant gap in query efficiency over classical algorithms, even in noisy, small-scale systems.

Contribution

It provides the first experimental demonstration of quantum advantage in an oracle-based machine learning problem on a superconducting quantum processor.

Findings

Quantum algorithms outperform classical ones in query complexity.

Quantum advantage gap increases with problem size and error rates.

Existing noisy quantum systems can already exhibit quantum advantage.

Abstract

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, and the attainable quantum advantage is modest. Here we solve an oracle-based problem, known as learning parity with noise, using a five-qubit superconducting processor. Running classical and quantum algorithms on 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 quantum advantage already emerges in existing noisy systems