PennyLane: Automatic differentiation of hybrid quantum-classical computations
- V. Bergholm, J. Izaac, M. Schuld, C. Gogolin, N. Killoran
- Computer ScienceArXiv
- 12 November 2018
PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation, and it extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations.
Quantum Machine Learning in Feature Hilbert Spaces.
- M. Schuld, N. Killoran
- Computer SciencePhysical Review Letters
- 19 March 2018
This Letter interprets the process of encoding inputs in a quantum state as a nonlinear feature map that maps data to quantum Hilbert space and shows how it opens up a new avenue for the design of quantum machine learning algorithms.
Circuit-centric quantum classifiers
- M. Schuld, A. Bocharov, K. Svore, N. Wiebe
- Computer Science, PhysicsPhysical Review A
- 3 April 2018
A machine learning design is developed to train a quantum circuit specialized in solving a classification problem and it is shown that the circuits perform reasonably well on classical benchmarks.
An introduction to quantum machine learning
- M. Schuld, I. Sinayskiy, Francesco Petruccione
- Computer ScienceContemporary physics (Print)
- 10 September 2014
This contribution gives a systematic overview of the emerging field of quantum machine learning and presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.
Machine learning and the physical sciences
- G. Carleo, I. Cirac, Lenka Zdeborov'a
- Physics, Computer ScienceReviews of Modern Physics
- 25 March 2019
This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences, including conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields.
Effect of data encoding on the expressive power of variational quantum-machine-learning models
It is shown that there exist quantum models which can realise all possible sets of Fourier coefficients, and therefore, if the accessible frequency spectrum is asymptotically rich enough, such models are universal function approximators.
The quest for a Quantum Neural Network
- M. Schuld, I. Sinayskiy, Francesco Petruccione
- Computer ScienceQuantum Information Processing
- 29 August 2014
This article presents a systematic approach to QNN research, concentrating on Hopfield-type networks and the task of associative memory, and outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quantum computing.
Evaluating analytic gradients on quantum hardware
- M. Schuld, V. Bergholm, C. Gogolin, J. Izaac, N. Killoran
- Computer SciencePhysical Review A
- 27 November 2018
This paper shows how gradients of expectation values of quantum measurements can be estimated using the same, or almost the same the architecture that executes the original circuit, and proposes recipes for the computation of gradients for continuous-variable circuits.
Supervised Learning with Quantum Computers
- M. Schuld, Francesco Petruccione
- Computer Science
- 22 September 2018
Quantum embeddings for machine learning
- S. Lloyd, M. Schuld, Aroosa Ijaz, J. Izaac, N. Killoran
- Computer Science
- 10 January 2020
This work proposes to train the first part of the circuit with the objective of maximally separating data classes in Hilbert space, a strategy it calls quantum metric learning, which provides a powerful analytic framework for quantum machine learning.
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