IQIM Postdoctoral and Graduate Student Seminar

Note special time this week, IQIM seminar begins at 2:00 pm in 114 E. Bridge

Abstract: Broadly useful quantum advantage, particularly in classical data processing and machine learning, has remained a fundamental open question. In this work, we prove that a small quantum computer of polylogarithmic size can perform large-scale classification and dimension reduction on massive classical data by processing samples on the fly, whereas any classical machine achieving the same requires exponentially larger size or superpolynomially more samples and time. We validate these quantum advantages in real-world applications including movie review sentiment analysis and single-cell RNA sequencing, demonstrating four to six orders of magnitude reduction in size with fewer than 60 logical qubits. These quantum advantages are enabled by quantum oracle sketching, an algorithm for accessing the classical world in quantum superposition using only random classical data samples. Combined with classical shadows, our algorithm circumvents the data loading and readout bottleneck to construct succinct classical models from massive classical data, a task provably impossible for any classical machine that is not exponentially larger than the quantum machine. These quantum advantages persist even when classical machines are granted unlimited time or when BPP=BQP, and rely only on the correctness of quantum mechanics. Together, our results establish machine learning on classical data as a broad and natural domain of quantum advantage and a fundamental test of quantum mechanics at the complexity frontier.

Refreshments will be provided following the talk.