Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Enabling Non-Linear Quantum Operations through Variational Quantum Splines
The postulates of quantum mechanics impose only unitary transformations on quantum states, which is a severe limitation for quantum machine learning algorithms. Quantum Splines (QSplines) have recently been proposed to a…
Quantum Machine LearningHybrid quantum-classical convolutional neural network for phytoplankton classification
The taxonomic composition and abundance of phytoplankton, having direct impact on marine ecosystem dynamic and global environment change, are listed as essential ocean variables. Phytoplankton classification is very cruc…
Classificationimage-classificationImage ClassificationQuantum Machine LearningSpacetime-Efficient Low-Depth Quantum State Preparation with Applications
We propose a novel deterministic method for preparing arbitrary quantum states. When our protocol is compiled into CNOT and arbitrary single-qubit gates, it prepares an $N$-dimensional state in depth $O(\log(N))$ and spa…
Quantum Machine LearningOn the universality of $S_n$-equivariant $k$-body gates
The importance of symmetries has recently been recognized in quantum machine learning from the simple motto: if a task exhibits a symmetry (given by a group $\mathfrak{G}$), the learning model should respect said symmetr…
Quantum Machine LearningQuantum Machine Learning hyperparameter search
This paper presents a quantum-based Fourier-regression approach for machine learning hyperparameter optimization applied to a benchmark of models trained on a dataset related to a forecast problem in the airline industry…
Hyperparameter OptimizationQuantum Machine LearningFirst steps towards quantum machine learning applied to the classification of event-related potentials
Low information transfer rate is a major bottleneck for brain-computer interfaces based on non-invasive electroencephalography (EEG) for clinical applications. This led to the development of more robust and accurate clas…
EEGElectroencephalogram (EEG)Quantum Machine LearningTowards interpretable quantum machine learning via single-photon quantum walks
Variational quantum algorithms represent a promising approach to quantum machine learning where classical neural networks are replaced by parametrized quantum circuits. However, both approaches suffer from a clear limita…
Decision MakingQuantum Machine Learningreinforcement-learningReinforcement Learning (RL)+1Fourier series weight in quantum machine learning
In this work, we aim to confirm the impact of the Fourier series on the quantum machine learning model. We will propose models, tests, and demonstrations to achieve this objective. We designed a quantum machine learning …
Quantum Machine LearningQuantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation
A significant challenge in the field of quantum machine learning (QML) is to establish applications of quantum computation to accelerate common tasks in machine learning such as those for neural networks. Ridgelet transf…
Quantum Machine LearningQuantum anomaly detection in the latent space of proton collision events at the LHC
The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies. Established approaches like machine learning, along with emerging technologies such as quantu…
Anomaly DetectionQuantum Machine LearningExplaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
Methods of artificial intelligence (AI) and especially machine learning (ML) have been growing ever more complex, and at the same time have more and more impact on people's lives. This leads to explainable AI (XAI) manif…
Explainable Artificial Intelligence (XAI)Quantum Machine LearningLearning Quantum Processes with Memory -- Quantum Recurrent Neural Networks
Recurrent neural networks play an important role in both research and industry. With the advent of quantum machine learning, the quantisation of recurrent neural networks has become recently relevant. We propose fully qu…
Quantum Machine LearningThe quantum cost function concentration dependency on the parametrization expressivity
Although we are currently in the era of noisy intermediate scale quantum devices, several studies are being conducted with the aim of bringing machine learning to the quantum domain. Currently, quantum variational circui…
Quantum Machine LearningHybrid quantum-classical convolutional neural networks to improve molecular protein binding affinity predictions
One of the main challenges in drug discovery is to find molecules that bind specifically and strongly to their target protein while having minimal binding to other proteins. By predicting binding affinity, it is possible…
Drug DiscoveryQuantum Machine LearningQuantum-inspired tensor network for Earth science
Deep Learning (DL) is one of many successful methodologies to extract informative patterns and insights from ever increasing noisy large-scale datasets (in our case, satellite images). However, DL models consist of a few…
GPUQuantum Machine LearningTensor DecompositionAsynchronous training of quantum reinforcement learning
The development of quantum machine learning (QML) has received a lot of interest recently thanks to developments in both quantum computing (QC) and machine learning (ML). One of the ML paradigms that can be utilized to a…
Decision MakingQuantum Machine Learningreinforcement-learningReinforcement Learning+2VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum
With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or qua…
Quantum Machine LearningUnitySEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training
Applying new computing paradigms like quantum computing to the field of machine learning has recently gained attention. However, as high-dimensional real-world applications are not yet feasible to be solved using purely …
Hybrid Machine Learningimage-classificationImage ClassificationQuantum Machine Learning+1Quantum Machine Learning Applied to the Classification of Diabetes
Quantum Machine Learning (QML) shows how it maintains certain significant advantages over machine learning methods. It now shows that hybrid quantum methods have great scope for deployment and optimisation, and hold prom…
ClassificationDimensionality ReductionQuantum Machine LearningRestricting to the chip architecture maintains the quantum neural network accuracy
In the era of noisy intermediate-scale quantum devices, variational quantum algorithms (VQAs) stand as a prominent strategy for constructing quantum machine learning models. These models comprise both a quantum and a cla…
Quantum Machine Learning