Search Results for author:
Found 10 papers, 2 papers with code
Bayesian Learning of Parameterised Quantum Circuits
Currently available quantum computers suffer from constraints including hardware noise and a limited number of qubits.
F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
In particular, we consider training a quantum circuit Born machine using $f$-divergences.
Variational inference with a quantum computer
One alternative is variational inference, where a candidate probability distribution is optimized to approximate the posterior distribution over unobserved variables.
Parameterized quantum circuits as machine learning models
Hybrid quantum-classical systems make it possible to utilize existing quantum computers to their fullest extent.
Structure optimization for parameterized quantum circuits
We demonstrate the method for optimizing a variational quantum eigensolver for finding the ground states of Lithium Hydride and the Heisenberg model in simulation, and for finding the ground state of Hydrogen gas on the IBM Melbourne quantum computer.
Quantum Physics
Adversarial quantum circuit learning for pure state approximation
Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data.
Hierarchical quantum classifiers
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state.
Quantum Physics
A generative modeling approach for benchmarking and training shallow quantum circuits
Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications.
Quantum Physics
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning techniques.
Quantum Physics Emerging Technologies
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions.
