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08/24/2023
Parallel-in-time quantum simulation via Page and Wootters quantum time
In the past few decades, researchers have created a veritable zoo of qua...
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05/17/2023
Deep quantum neural networks form Gaussian processes
It is well known that artificial neural networks initialized from indepe...
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03/16/2023
Challenges and Opportunities in Quantum Machine Learning
At the intersection of machine learning and quantum computing, Quantum M...
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03/01/2023
On the universality of S_n-equivariant k-body gates
The importance of symmetries has recently been recognized in quantum mac...
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02/10/2023
Effects of noise on the overparametrization of quantum neural networks
Overparametrization is one of the most surprising and notorious phenomen...
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11/09/2022
Resource frugal optimizer for quantum machine learning
Quantum-enhanced data science, also known as quantum machine learning (Q...
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10/21/2022
Quantum Algorithms for Geologic Fracture Networks
Solving large systems of equations is a challenge for modeling natural p...
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10/18/2022
Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
Despite the great promise of quantum machine learning models, there are ...
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10/16/2022
Theory for Equivariant Quantum Neural Networks
Most currently used quantum neural network architectures have little-to-...
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10/14/2022
Representation Theory for Geometric Quantum Machine Learning
Recent advances in classical machine learning have shown that creating m...
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08/23/2022
Exponential concentration and untrainability in quantum kernel methods
Kernel methods in Quantum Machine Learning (QML) have recently gained si...
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06/20/2022
Inference-Based Quantum Sensing
In a standard Quantum Sensing (QS) task one aims at estimating an unknow...
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05/04/2022
Group-Invariant Quantum Machine Learning
Quantum Machine Learning (QML) models are aimed at learning from data en...
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04/07/2022
Covariance matrix preparation for quantum principal component analysis
Principal component analysis (PCA) is a dimensionality reduction method ...
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11/09/2021
Generalization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally opti...
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10/27/2021
Subtleties in the trainability of quantum machine learning models
A new paradigm for data science has emerged, with quantum data, quantum ...
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09/23/2021
Theory of overparametrization in quantum neural networks
The prospect of achieving quantum advantage with Quantum Neural Networks...
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09/08/2021
Entangled Datasets for Quantum Machine Learning
High-quality, large-scale datasets have played a crucial role in the dev...
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09/02/2021
Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
Variational Quantum Algorithms (VQAs) are widely viewed as the best hope...
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04/12/2021
Equivalence of quantum barren plateaus to cost concentration and narrow gorges
Optimizing parameterized quantum circuits (PQCs) is the leading approach...
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03/11/2021
A semi-agnostic ansatz with variable structure for quantum machine learning
Quantum machine learning (QML) offers a powerful, flexible paradigm for ...
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01/06/2021
Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Parameterized quantum circuits serve as ansätze for solving variational ...
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12/16/2020
Variational Quantum Algorithms
Applications such as simulating large quantum systems or solving large-s...
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11/24/2020
Effect of barren plateaus on gradient-free optimization
Barren plateau landscapes correspond to gradients that vanish exponentia...
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11/17/2020
Optimizing parametrized quantum circuits via noise-induced breaking of symmetries
Very little is known about the cost landscape for parametrized Quantum C...
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11/05/2020
Absence of Barren Plateaus in Quantum Convolutional Neural Networks
Quantum neural networks (QNNs) have generated excitement around the poss...
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07/28/2020
Noise-Induced Barren Plateaus in Variational Quantum Algorithms
Variational Quantum Algorithms (VQAs) may be a path to quantum advantage...
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07/09/2020
Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
The No-Free-Lunch (NFL) theorem is a celebrated result in learning theor...
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05/26/2020
Trainability of Dissipative Perceptron-Based Quantum Neural Networks
Several architectures have been proposed for quantum neural networks (QN...
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01/02/2020