Search Results for author:
Found 20 papers, 7 papers with code
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing
Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
Financial Vision Based Reinforcement Learning Trading Strategy
Hence, we need to ask about the AI "black box", including why did AI decide to do this or not?
Financial Vision Based Differential Privacy Applications
We apply two representative deep learning privacy-privacy frameworks proposed by Google to financial trading data.
Quantum Architecture Search via Continual Reinforcement Learning
To aid this endeavor, this paper proposes a machine learning-based method to construct quantum circuit architectures.
The Dawn of Quantum Natural Language Processing
In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing.
Variational Quantum Reinforcement Learning via Evolutionary Optimization
Recent advance in classical reinforcement learning (RL) and quantum computation (QC) points to a promising direction of performing RL on a quantum computer.
Quantum Architecture Search via Deep Reinforcement Learning
Recent advances in quantum computing have drawn considerable attention to building realistic application for and using quantum computers.
Federated Quantum Machine Learning
Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located.
Quantum machine learning with differential privacy
In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm.
An end-to-end trainable hybrid classical-quantum classifier
We introduce a hybrid model combining a quantum-inspired tensor network and a variational quantum circuit to perform supervised learning tasks.
Hybrid Quantum-Classical Graph Convolutional Network
This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data.
Quantum Convolutional Neural Networks for High Energy Physics Data Analysis
This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events.
Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
On the quantum hardware, the quantum variational classifier method has shown promising discrimination power, comparable to that on the quantum simulator.
Quantum Physics High Energy Physics - Experiment
Hybrid quantum-classical classifier based on tensor network and variational quantum circuit
One key step in performing quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices is the dimension reduction of the input data prior to their encoding.
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95. 12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features.
Ranked #1 on
Keyword Spotting
on Google Speech Commands
(10-keyword Speech Commands dataset metric)
Quantum Long Short-Term Memory
Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence and temporal dependency data modeling and its effectiveness has been extensively established.
Adversarial Robustness of Deep Convolutional Candlestick Learner
Deep learning (DL) has been applied extensively in a wide range of fields.
Data Augmentation for Deep Candlestick Learner
To successfully build a deep learning model, it will need a large amount of labeled data.
Explainable Deep Convolutional Candlestick Learner
Candlesticks are graphical representations of price movements for a given period.
Variational Quantum Circuits for Deep Reinforcement Learning
To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep $Q$-value function for decision-making and policy-selection reinforcement learning with experience replay and target network.
