An introduction to quantum machine learning

By Maria Schuld, Ilya Sinayskiy, Francesco Petruccione. Contemporary physics (Print), 2014

1,477 citations recorded by the source corpus.

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/85d2d4cb2ca1dcd9da2d5a765cc2aa4911b0a175

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In Atomic and Molecular Physics, and Optics

Citations in the corpus

A high-bias, low-variance introduction to Machine Learning for physicists (2018, neutral): “Another interesting area of research that is likely to grow is asking if and how quantum computers 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;”
Quantum machine learning (2016, positive): “The analysis of their practical feasibility is a central subject of this review. As will be seen throughout the review, there are quantum algorithms for machine learning that exhibit quantum speedups [4, 5, 6, 7]. For example, the quantum basic linear algebra subroutines (BLAS)|Fourier transforms, n”
Barren plateaus in variational quantum computing (2024, neutral): “It has become standard to divide variational quantum computing schemes into two main categories — VQAs 1–3 and quantum machine-learning (QML) models 4–6 .”
Challenges and opportunities in quantum machine learning (2022, background): “[5] Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione, “An introduction to quantum machine learning,” Contemporary Physics 56, 172–185 (2015).”
Connecting ansatz expressibility to gradient magnitudes and barren plateaus (2021, background): “[51] Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione, “An introduction to quantum machine learning,” Contemporary Physics 56, 172–185 (2015).”
Classification with Quantum Neural Networks on Near Term Processors (2018, background): “Other approaches to harnessing quantum resources in machine learning are reviewed here [3][4].”
Circuit-centric quantum classifiers (2018, background): “Numerous quantum algorithms for machine learning have been proposed in the past years [7, 8].”
Quantum error correction: an introductory guide (2019, methodology): “In the event where E = Z2, the recovery operation still restores the logical state as RE = Z1Z2 is in the stabiliser of C[[9,1,3]], and therefore acts on the logical state as follows Z1Z2 |ψ〉9 = |ψ〉9.”
Variational quantum algorithms (2020, methodology): “to use quantum algorithms for machine learning applications [134, 148, 256, 257].”
Quantum machine learning: a classical perspective (2017, background): “We invite the reader to consult [7,8,187] for a review that includes these works.”
Absence of Barren Plateaus in Quantum Convolutional Neural Networks (2020, background): “With the advent of noisy, intermediate-scale quantum (NISQ) computers, the ﬁeld of quantum machine learning has brought new approaches to solving computational problems dealing with quantum data [13–16].”
Noise-induced barren plateaus in variational quantum algorithms (2020, background): “[58] Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione, “An introduction to quantum machine learning,” Contemporary Physics 56, 172–185 (2015).”
Quantum computing for finance: Overview and prospects (2018, background): “This set of ideas has recently emerged with a lot of momentum [8, 52, 53].”
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives (2017, background): “For big data classification problems quite perspective and promising is a quantum computing approach [Biamonte et al., 2016, Chatterjee and Yu, 2016, Li et al., 2015, Rebentrost et al., 2014, Schuld et al., 2015] and a tensor network approach discussed in the next Chapter 3.”