NISQ computing: where are we and where do we go?
In this short review article, we aim to provide physicists not working within the quantum
computing community a hopefully easy-to-read introduction to the state of the art in the field …
computing community a hopefully easy-to-read introduction to the state of the art in the field …
Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision
Abstract Machine learning has become a ubiquitous and effective technique for data
processing and classification. Furthermore, due to the superiority and progress of quantum …
processing and classification. Furthermore, due to the superiority and progress of quantum …
Quantum convolutional neural network for classical data classification
With the rapid advance of quantum machine learning, several proposals for the quantum-
analogue of convolutional neural network (CNN) have emerged. In this work, we benchmark …
analogue of convolutional neural network (CNN) have emerged. In this work, we benchmark …
Solving nonlinear differential equations with differentiable quantum circuits
We propose a quantum algorithm to solve systems of nonlinear differential equations. Using
a quantum feature map encoding, we define functions as expectation values of parametrized …
a quantum feature map encoding, we define functions as expectation values of parametrized …
Federated quantum machine learning
Distributed training across several quantum computers could significantly improve the
training time and if we could share the learned model, not the data, it could potentially …
training time and if we could share the learned model, not the data, it could potentially …
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
Quantum convolutional neural networks are (effectively) classically simulable
Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising
model for Quantum Machine Learning (QML). In this work we tie their heuristic success to …
model for Quantum Machine Learning (QML). In this work we tie their heuristic success to …
Multiclass seismic damage detection of buildings using quantum convolutional neural network
The traditional visual inspection technique for damage assessment of buildings immediately
after an earthquake can be time‐consuming, labor‐intensive, and risky. Numerous studies …
after an earthquake can be time‐consuming, labor‐intensive, and risky. Numerous studies …
Quantum architecture search via deep reinforcement learning
Recent advances in quantum computing have drawn considerable attention to building
realistic application for and using quantum computers. However, designing a suitable …
realistic application for and using quantum computers. However, designing a suitable …
Variational quantum reinforcement learning via evolutionary optimization
Recent advances in classical reinforcement learning (RL) and quantum computation point to
a promising direction for performing RL on a quantum computer. However, potential …
a promising direction for performing RL on a quantum computer. However, potential …