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

Biamonte, Wittek, Pancotti, et al. 2017. “Quantum Machine Learning.” Nature.
Cerezo, Arrasmith, Babbush, et al. 2021. “Variational Quantum Algorithms.” Nature Reviews Physics.
Cerezo, Verdon, Huang, et al. 2022. “Challenges and Opportunities in Quantum Machine Learning.” Nature Computational Science.
Lloyd, Mohseni, and Rebentrost. 2013. “Quantum Algorithms for Supervised and Unsupervised Machine Learning.”
“Quantum Advantage in Learning from Experiments | Science.” n.d.
Rebentrost, Schuld, Wossnig, et al. 2019. “Quantum Gradient Descent and Newton’s Method for Constrained Polynomial Optimization.” New Journal of Physics.