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Found 14 papers, 9 papers with code
PennyLane: Automatic differentiation of hybrid quantum-classical computations
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.
Strawberry Fields: A Software Platform for Photonic Quantum Computing
We introduce Strawberry Fields, an open-source quantum programming architecture for light-based quantum computers.
Quantum Physics Computational Physics
Applications of Near-Term Photonic Quantum Computers: Software and Algorithms
Gaussian Boson Sampling (GBS) is a near-term platform for photonic quantum computing.
Quantum Physics Computational Physics
Quantum Natural Gradient
A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits.
Continuous-variable quantum neural networks
The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field.
Machine learning method for state preparation and gate synthesis on photonic quantum computers
In the simplest case of a single input state, our method discovers circuits for preparing a desired quantum state.
Quantum Physics
Transfer learning in hybrid classical-quantum neural networks
We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements.
Generating and designing DNA with deep generative models
We propose generative neural network methods to generate DNA sequences and tune them to have desired properties.
Estimating the gradient and higher-order derivatives on quantum hardware
For a large class of variational quantum circuits, we show how arbitrary-order derivatives can be analytically evaluated in terms of simple parameter-shift rules, i. e., by running the same circuit with different shifts of the parameters.
Quantum Physics
Quantum generative adversarial networks
Quantum machine learning is expected to be one of the first potential general-purpose applications of near-term quantum devices.
BIG-bench Machine Learning
Generative Adversarial Network
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A Quantum Approximate Optimization Algorithm for continuous problems
We introduce a quantum approximate optimization algorithm (QAOA) for continuous optimization.
Quantum Physics
Evaluating analytic gradients on quantum hardware
An important application for near-term quantum computing lies in optimization tasks, with applications ranging from quantum chemistry and drug discovery to machine learning.
Quantum Physics
Quantum embeddings for machine learning
Quantum classifiers are trainable quantum circuits used as machine learning models.
Quantum Physics
Quantum machine learning in feature Hilbert spaces
We interpret the process of encoding inputs in a quantum state as a nonlinear feature map that maps data to quantum Hilbert space.
Quantum Physics
