Variational quantum algorithms
Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …
algebra problems are very challenging for classical computers, owing to the extremely high …
Challenges and opportunities in quantum machine learning
At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …
has the potential of accelerating data analysis, especially for quantum data, with …
Quantum advantage in learning from experiments
Quantum technology promises to revolutionize how we learn about the physical world. An
experiment that processes quantum data with a quantum computer could have substantial …
experiment that processes quantum data with a quantum computer could have substantial …
AI for next generation computing: Emerging trends and future directions
Autonomic computing investigates how systems can achieve (user) specified “control”
outcomes on their own, without the intervention of a human operator. Autonomic computing …
outcomes on their own, without the intervention of a human operator. Autonomic computing …
Power of data in quantum machine learning
The use of quantum computing for machine learning is among the most exciting prospective
applications of quantum technologies. However, machine learning tasks where data is …
applications of quantum technologies. However, machine learning tasks where data is …
Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Parametrized quantum circuits serve as ansatze for solving variational problems and
provide a flexible paradigm for the programming of near-term quantum computers. Ideally …
provide a flexible paradigm for the programming of near-term quantum computers. Ideally …
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 …
Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …
impact on every industry and research discipline. At the core of this revolution lies the tools …
Absence of barren plateaus in quantum convolutional neural networks
Quantum neural networks (QNNs) have generated excitement around the possibility of
efficiently analyzing quantum data. But this excitement has been tempered by the existence …
efficiently analyzing quantum data. But this excitement has been tempered by the existence …
[HTML][HTML] Quantum machine learning beyond kernel methods
Abstract Machine learning algorithms based on parametrized quantum circuits are prime
candidates for near-term applications on noisy quantum computers. In this direction, various …
candidates for near-term applications on noisy quantum computers. In this direction, various …