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Found 32 papers, 8 papers with code
Learning to Program Variational Quantum Circuits with Fast Weights
This paper introduces the Quantum Fast Weight Programmers (QFWP) as a solution to the temporal or sequential learning challenge.
A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity
In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu.
Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning
The utility of machine learning has rapidly expanded in the last two decades and presents an ethical challenge.
Federated Quantum Long Short-term Memory (FedQLSTM)
The proposed federated QLSTM (FedQLSTM) framework is exploited for performing the task of function approximation.
Federated Quantum Machine Learning with Differential Privacy
The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data.
Efficient quantum recurrent reinforcement learning via quantum reservoir computing
Quantum reinforcement learning (QRL) has emerged as a framework to solve sequential decision-making tasks, showcasing empirical quantum advantages.
Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats
With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening.
Quantum deep Q learning with distributed prioritized experience replay
This paper introduces the QDQN-DPER framework to enhance the efficiency of quantum reinforcement learning (QRL) in solving sequential decision tasks.
Asynchronous training of quantum reinforcement learning
In this paper, we approach this challenge through asynchronous training QRL agents.
Decoding surface codes with deep reinforcement learning and probabilistic policy reuse
A significant amount of theoretical studies have provided various types of QEC codes; one of the notable topological codes is the surface code, and its features, such as the requirement of only nearest-neighboring two-qubit control gates and a large error threshold, make it a leading candidate for scalable quantum computation.
Reservoir Computing via Quantum Recurrent Neural Networks
In this work, we approach sequential modeling by applying a reservoir computing (RC) framework to quantum recurrent neural networks (QRNN-RC) that are based on classical RNN, LSTM and GRU.
Quantum deep recurrent reinforcement learning
Reinforcement learning (RL) is one of the ML paradigms which can be used to solve complex sequential decision making problems.
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)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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.
