Quantum machine learning: a classical perspective
By Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig. Proceedings of the Royal Society A, 2017
544 citations recorded by the source corpus.
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/e1f9ef01ab55d53349096a58d76fd0cfa7bb051d
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Citations in the corpus
- A high-bias, low-variance introduction to Machine Learning for physicists (2018, unknown): “…can help improve state-of-the art ML algorithms (Arunachalam and de Wolf, 2017; Benedetti et al., 2016, 2017; Bromley and Rebentrost, 2018; Ciliberto et al., 2017; Daskin, 2018; Innocenti et al., 2018; Mitarai et al., 2018; Perdomo-Ortiz et al., 2017; Rebentrost et al., 2017; Schuld et…”
- ARTIFICIAL INTELLIGENCE AND PERSONALIZED MEDICINE (2019, background): “Not only will there be greater adoption of AI-based health products but such products could be developed and exploit emerging computing capabilities such as quantum computing[81, 82] to achieve increased speed and an ability to handle larger and larger data sets.”
- Bio-inspired computation: Where we stand and what's next (2019, background): “in terms of processing throughput for many other fields, including ML and large-scale optimization [451,452].”