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Found 10 papers, 3 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.
Recent advances for quantum classifiers
Then, we move on to introduce the variational quantum classifiers, which are essentially variational quantum circuits for classifications.
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.
Exploiting WordNet Synset and Hypernym Representations for Answer Selection
Answer selection (AS) is an important subtask of document-based question answering (DQA).
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings
Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS.
Learning Universal Sentence Representations with Mean-Max Attention Autoencoder
In the encoding we propose a mean-max strategy that applies both mean and max pooling operations over the hidden vectors to capture diverse information of the input.
