Towards quantum enhanced adversarial robustness in machine learning
Abstract Machine learning algorithms are powerful tools for data-driven tasks such as image
classification and feature detection. However, their vulnerability to adversarial examples …
classification and feature detection. However, their vulnerability to adversarial examples …
Recent advances for quantum neural networks in generative learning
Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …
beyond the reach of classical computers. A leading method towards achieving this goal is …
Long-lived topological time-crystalline order on a quantum processor
Topologically ordered phases of matter elude Landau's symmetry-breaking theory, featuring
a variety of intriguing properties such as long-range entanglement and intrinsic robustness …
a variety of intriguing properties such as long-range entanglement and intrinsic robustness …
Quantum-classical separations in shallow-circuit-based learning with and without noises
An essential problem in quantum machine learning is to find quantum-classical separations
between learning models. However, rigorous and unconditional separations are lacking for …
between learning models. However, rigorous and unconditional separations are lacking for …
Mitigating barren plateaus of variational quantum eigensolvers
Variational quantum algorithms (VQAs) are expected to establish valuable applications on
near-term quantum computers. However, recent works have pointed out that the …
near-term quantum computers. However, recent works have pointed out that the …
Expressibility-induced concentration of quantum neural tangent kernels
Quantum tangent kernel methods provide an efficient approach to analyzing the
performance of quantum machine learning models in the infinite-width limit, which is of …
performance of quantum machine learning models in the infinite-width limit, which is of …
A quantum federated learning framework for classical clients
Quantum federated learning (QFL) enables collaborative training of a quantum machine
learning (QML) model among multiple clients possessing quantum computing capabilities …
learning (QML) model among multiple clients possessing quantum computing capabilities …
Reservoir computing via quantum recurrent neural networks
SYC Chen, D Fry, A Deshmukh, V Rastunkov… - arXiv preprint arXiv …, 2022 - arxiv.org
Recent developments in quantum computing and machine learning have propelled the
interdisciplinary study of quantum machine learning. Sequential modeling is an important …
interdisciplinary study of quantum machine learning. Sequential modeling is an important …
Quantum-empowered federated learning in space-air-ground integrated networks
As a key paradigm of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN)
has been envisioned to provide numerous intelligent applications that necessitate the …
has been envisioned to provide numerous intelligent applications that necessitate the …
Evaluating the computational advantages of the Variational Quantum Circuit model in Financial Fraud Detection
Home banking and digital payments diffusion has greatly increased in recent years. As a
result, fraud has also dramatically grown, resulting in the loss of billions of dollars worldwide …
result, fraud has also dramatically grown, resulting in the loss of billions of dollars worldwide …