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 …
Non-Abelian braiding of Fibonacci anyons with a superconducting processor
Quantum many-body systems with a non-Abelian topological order can host anyonic
quasiparticles. It has been proposed that anyons could be used to encode and manipulate …
quasiparticles. It has been proposed that anyons could be used to encode and manipulate …
Factoring integers with sublinear resources on a superconducting quantum processor
B Yan, Z Tan, S Wei, H Jiang, W Wang, H Wang… - arxiv preprint arxiv …, 2022 - arxiv.org
Shor's algorithm has seriously challenged information security based on public key
cryptosystems. However, to break the widely used RSA-2048 scheme, one needs millions of …
cryptosystems. However, to break the widely used RSA-2048 scheme, one needs millions of …
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 …
Simulating unsteady flows on a superconducting quantum processor
Z Meng, J Zhong, S Xu, K Wang, J Chen, F **… - Communications …, 2024 - nature.com
Recent advancements of quantum technologies have triggered tremendous interest in
exploring practical quantum advantage. The simulation of fluid dynamics, a highly …
exploring practical quantum advantage. The simulation of fluid dynamics, a highly …
Benchmarking adversarially robust quantum machine learning at scale
Machine learning (ML) methods such as artificial neural networks are rapidly becoming
ubiquitous in modern science, technology, and industry. Despite their accuracy and …
ubiquitous in modern science, technology, and industry. Despite their accuracy and …
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 …
Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art
Generative adversarial networks (GANs) have rapidly emerged as powerful tools for
generating realistic and diverse data across various domains, including computer vision and …
generating realistic and diverse data across various domains, including computer vision and …
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 …