Quantum information processing with superconducting circuits: a review
G Wendin - Reports on Progress in Physics, 2017 - iopscience.iop.org
During the last ten years, superconducting circuits have passed from being interesting
physical devices to becoming contenders for near-future useful and scalable quantum …
physical devices to becoming contenders for near-future useful and scalable quantum …
Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications
The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
Experimental quantum adversarial learning with programmable superconducting qubits
W Ren, W Li, S Xu, K Wang, W Jiang, F Jin… - Nature Computational …, 2022 - nature.com
Quantum computing promises to enhance machine learning and artificial intelligence.
However, recent theoretical works show that, similar to traditional classifiers based on deep …
However, recent theoretical works show that, similar to traditional classifiers based on deep …
Problem-dependent power of quantum neural networks on multiclass classification
Quantum neural networks (QNNs) have become an important tool for understanding the
physical world, but their advantages and limitations are not fully understood. Some QNNs …
physical world, but their advantages and limitations are not fully understood. Some QNNs …
Deep quantum neural networks on a superconducting processor
Deep learning and quantum computing have achieved dramatic progresses in recent years.
The interplay between these two fast-growing fields gives rise to a new research frontier of …
The interplay between these two fast-growing fields gives rise to a new research frontier of …
Robust resource-efficient quantum variational ansatz through an evolutionary algorithm
Variational quantum algorithms (VQAs) are promising methods to demonstrate quantum
advantage on near-term devices as the required resources are divided between a quantum …
advantage on near-term devices as the required resources are divided between a quantum …
Experimental simulation of larger quantum circuits with fewer superconducting qubits
Although near-term quantum computing devices are still limited by the quantity and quality of
qubits in the so-called NISQ era, quantum computational advantage has been …
qubits in the so-called NISQ era, quantum computational advantage has been …
Quantum neural network classifiers: A tutorial
Abstract Machine learning has achieved dramatic success over the past decade, with
applications ranging from face recognition to natural language processing. Meanwhile, rapid …
applications ranging from face recognition to natural language processing. Meanwhile, rapid …
Quantum self-supervised learning
The resurgence of self-supervised learning, whereby a deep learning model generates its
own supervisory signal from the data, promises a scalable way to tackle the dramatically …
own supervisory signal from the data, promises a scalable way to tackle the dramatically …
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification
Quantum neural networks (QNNs) have become an important tool for understanding the
physical world, but their advantages and limitations are not fully understood. Some QNNs …
physical world, but their advantages and limitations are not fully understood. Some QNNs …