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Found 15 papers, 2 papers with code
Expressibility-induced Concentration of Quantum Neural Tangent Kernels
In particular, for global loss functions, we rigorously prove that high expressibility of both the global and local quantum encodings can lead to exponential concentration of quantum tangent kernel values to zero.
Enhancing Quantum Adversarial Robustness by Randomized Encodings
To address this issue, we propose a general scheme to protect quantum learning systems from adversarial attacks by randomly encoding the legitimate data samples through unitary or quantum error correction encoders.
Quantum Neural Network Classifiers: A Tutorial
Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing.
Experimental quantum adversarial learning with programmable superconducting qubits
Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.
Quantum Capsule Networks
Capsule networks, which incorporate the paradigms of connectionism and symbolism, have brought fresh insights into artificial intelligence.
Weighted Quantum Channel Compiling through Proximal Policy Optimization
We propose a general and systematic strategy to compile arbitrary quantum channels without using ancillary qubits, based on proximal policy optimization -- a powerful deep reinforcement learning algorithm.
Recent advances for quantum classifiers
Then, we move on to introduce the variational quantum classifiers, which are essentially variational quantum circuits for classifications.
Quantum Continual Learning Overcoming Catastrophic Forgetting
Catastrophic forgetting describes the fact that machine learning models will likely forget the knowledge of previously learned tasks after the learning process of a new one.
Sample Complexity of Learning Parametric Quantum Circuits
Quantum computers hold unprecedented potentials for machine learning applications.
Quantum federated learning through blind quantum computing
In this paper, we introduce a quantum protocol for distributed learning that is able to utilize the computational power of the remote quantum servers while keeping the private data safe.
Universal Adversarial Examples and Perturbations for Quantum Classifiers
Through concrete examples involving classifications of real-life images and quantum phases of matter, we show that there exist universal adversarial examples that can fool a set of different quantum classifiers.
Topological Quantum Compiling with Reinforcement Learning
Quantum compiling, a process that decomposes the quantum algorithm into a series of hardware-compatible commands or elementary gates, is of fundamental importance for quantum computing.
Quantum Adversarial Machine Learning
Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations.
Quantum generative adversarial learning in a superconducting quantum circuit
Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning---a subfield of artificial intelligence that is currently driving a revolution in many aspects of modern society.
Quantum Physics Disordered Systems and Neural Networks Superconductivity
Hopf insulators and their topologically protected surface states
Three-dimensional (3D) topological insulators in general need to be protected by certain kinds of symmetries other than the presumed U(1) charge conservation.
