Quantum computing for ML

2024-12-17 — 2024-12-17

Wherein the prospects for quantum-accelerated machine learning are examined, and concrete conditions for provable speedups—data access models and noise limits—are delineated.

compsci
physics
quantum
statmech

Does quantum computing offer speedups to machine learning? When?

Figure 1: Instrumenting Schrödinger’s cat.

1 Incoming

2 References

Arute, Arya, Babbush, et al. 2019. “Quantum Supremacy Using a Programmable Superconducting Processor.” Nature.
Bernstein. 2009. “Introduction to Post-Quantum Cryptography.” In Post-Quantum Cryptography.
de Wolf. 2017. “The Potential Impact of Quantum Computers on Society.” arXiv:1712.05380 [Quant-Ph].
Montanaro. 2016. “Quantum Algorithms: An Overview.” Npj Quantum Information.
Preskill. 2018. “Quantum Computing in the NISQ Era and Beyond.” arXiv:1801.00862 [Cond-Mat, Physics:quant-Ph].
Wang, Qin, Ding, et al. 2019. “Boson Sampling with 20 Input Photons in 60-Mode Interferometers at \(10^{14}\) State Spaces.” arXiv:1910.09930 [Cond-Mat, Physics:physics, Physics:quant-Ph].