Quantum machine learning for 6G communication networks: State-of-the-art and vision for the future
The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of
intelligent networks with the provision of some isolated artificial intelligence (AI) operations …
intelligent networks with the provision of some isolated artificial intelligence (AI) operations …
Effect of data encoding on the expressive power of variational quantum-machine-learning models
Quantum computers can be used for supervised learning by treating parametrized quantum
circuits as models that map data inputs to predictions. While a lot of work has been done to …
circuits as models that map data inputs to predictions. While a lot of work has been done to …
Parameterized quantum circuits as machine learning models
Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …
Pennylane: Automatic differentiation of hybrid quantum-classical computations
PennyLane is a Python 3 software framework for differentiable programming of quantum
computers. The library provides a unified architecture for near-term quantum computing …
computers. The library provides a unified architecture for near-term quantum computing …
A leap among quantum computing and quantum neural networks: A survey
In recent years, Quantum Computing witnessed massive improvements in terms of available
resources and algorithms development. The ability to harness quantum phenomena to solve …
resources and algorithms development. The ability to harness quantum phenomena to solve …
Layerwise learning for quantum neural networks
With the increased focus on quantum circuit learning for near-term applications on quantum
devices, in conjunction with unique challenges presented by cost function landscapes of …
devices, in conjunction with unique challenges presented by cost function landscapes of …
Continuous-variable quantum neural networks
We introduce a general method for building neural networks on quantum computers. The
quantum neural network is a variational quantum circuit built in the continuous-variable (CV) …
quantum neural network is a variational quantum circuit built in the continuous-variable (CV) …
[HTML][HTML] An initialization strategy for addressing barren plateaus in parametrized quantum circuits
Parametrized quantum circuits initialized with random initial parameter values are
characterized by barren plateaus where the gradient becomes exponentially small in the …
characterized by barren plateaus where the gradient becomes exponentially small in the …
[HTML][HTML] Structure optimization for parameterized quantum circuits
We propose an efficient method for simultaneously optimizing both the structure and
parameter values of quantum circuits with only a small computational overhead. Shallow …
parameter values of quantum circuits with only a small computational overhead. Shallow …
General parameter-shift rules for quantum gradients
Variational quantum algorithms are ubiquitous in applications of noisy intermediate-scale
quantum computers. Due to the structure of conventional parametrized quantum gates, the …
quantum computers. Due to the structure of conventional parametrized quantum gates, the …