Pulse-level parameterization of quantum Fourier models replaces single gate angles with multiple independent sub-angles, relaxing monomial couplings and improving gradient descent performance on Fourier series tasks.
Effect of data encoding on the expressive power of variational quantum-machine-learning models
13 Pith papers cite this work, alongside 575 external citations. Polarity classification is still indexing.
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representative citing papers
Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.
MerLin is a new open-source discovery engine for photonic and hybrid quantum machine learning that integrates circuit simulations into standard ML frameworks and reproduces 18 prior works as reusable benchmarks.
All embedding quantum kernels can be understood as entangled tensor kernels, yielding new insights into their inductive bias and potential dequantization.
Hybrid agent with variational quantum circuits for feature extraction in hierarchical RL outperforms classical baselines with 66% parameter savings, but quantum value estimation degrades results.
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
A quantum autoencoder for multivariate time series anomaly detection achieves competitive performance with neural-network autoencoders using fewer trainable parameters.
Simulated fidelity quantum kernels achieve competitive or better accuracy than RBF kernels on Indian Pines binary and multiclass tasks and Methane Detection data without heavy dimensionality reduction.
Hybrid quantum-classical physics-informed neural networks reach accurate solutions to nonlinear PDEs in substantially fewer training epochs than purely classical networks, with larger gains on complex problems.
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
citing papers explorer
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Beyond Gates: Pulse Level Quantum Fourier Models
Pulse-level parameterization of quantum Fourier models replaces single gate angles with multiple independent sub-angles, relaxing monomial couplings and improving gradient descent performance on Fourier series tasks.
-
Local tensor-train surrogates for quantum learning models
Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
-
Learning PDEs for Portfolio Optimization with Quantum Physics-Informed Neural Networks
Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.
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MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning
MerLin is a new open-source discovery engine for photonic and hybrid quantum machine learning that integrates circuit simulations into standard ML frameworks and reproduces 18 prior works as reusable benchmarks.
-
New perspectives on quantum kernels through the lens of entangled tensor kernels
All embedding quantum kernels can be understood as entangled tensor kernels, yielding new insights into their inductive bias and potential dequantization.
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Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
Hybrid agent with variational quantum circuits for feature extraction in hierarchical RL outperforms classical baselines with 66% parameter savings, but quantum value estimation degrades results.
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Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
-
Quantum Autoencoder for Multivariate Time Series Anomaly Detection
A quantum autoencoder for multivariate time series anomaly detection achieves competitive performance with neural-network autoencoders using fewer trainable parameters.
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Large-Scale Quantum Kernels for Hyperspectral Data Classification
Simulated fidelity quantum kernels achieve competitive or better accuracy than RBF kernels on Indian Pines binary and multiclass tasks and Methane Detection data without heavy dimensionality reduction.
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Quantum-Enhanced Convergence of Physics-Informed Neural Networks
Hybrid quantum-classical physics-informed neural networks reach accurate solutions to nonlinear PDEs in substantially fewer training epochs than purely classical networks, with larger gains on complex problems.
-
Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.
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A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
- Software Between Quantum and Machine Learning -- And Down to Pulses