Probabilistic modeling over permutations using quantum computers
Fuente:
arXiv
Guardado en:
| Autores principales: | Belis, Vasilis, Crognaletti, Giulio, Argenton, Matteo, Grossi, Michele, Schuld, Maria |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Spectral methods: crucial for machine learning, natural for quantum computers?
por: Belis, Vasilis, et al.
Publicado: (2026)
por: Belis, Vasilis, et al.
Publicado: (2026)
Estimates of loss function concentration in noisy parametrized quantum circuits
por: Crognaletti, Giulio, et al.
Publicado: (2024)
por: Crognaletti, Giulio, et al.
Publicado: (2024)
Guided Quantum Compression for High Dimensional Data Classification
por: Belis, Vasilis, et al.
Publicado: (2024)
por: Belis, Vasilis, et al.
Publicado: (2024)
Guided Graph Compression for Quantum Graph Neural Networks
por: Casals, Mikel, et al.
Publicado: (2025)
por: Casals, Mikel, et al.
Publicado: (2025)
Better than classical? The subtle art of benchmarking quantum machine learning models
por: Bowles, Joseph, et al.
Publicado: (2024)
por: Bowles, Joseph, et al.
Publicado: (2024)
Learning Reduced Representations for Quantum Classifiers
por: Odagiu, Patrick, et al.
Publicado: (2025)
por: Odagiu, Patrick, et al.
Publicado: (2025)
Machine Learning for Anomaly Detection in Particle Physics
por: Belis, Vasilis, et al.
Publicado: (2023)
por: Belis, Vasilis, et al.
Publicado: (2023)
Inference, interference and invariance: How the Quantum Fourier Transform can help to learn from data
por: Wakeham, David, et al.
Publicado: (2024)
por: Wakeham, David, et al.
Publicado: (2024)
Quantum anomaly detection in the latent space of proton collision events at the LHC
por: Belis, Vasilis, et al.
Publicado: (2023)
por: Belis, Vasilis, et al.
Publicado: (2023)
Characterization and upgrade of a quantum graph neural network for charged particle tracking
por: Argenton, Matteo, et al.
Publicado: (2026)
por: Argenton, Matteo, et al.
Publicado: (2026)
Expressive equivalence of classical and quantum restricted Boltzmann machines
por: Demidik, Maria, et al.
Publicado: (2025)
por: Demidik, Maria, et al.
Publicado: (2025)
Symmetry breaking in geometric quantum machine learning in the presence of noise
por: Tüysüz, Cenk, et al.
Publicado: (2024)
por: Tüysüz, Cenk, et al.
Publicado: (2024)
Automatic and effective discovery of quantum kernels
por: Incudini, Massimiliano, et al.
Publicado: (2022)
por: Incudini, Massimiliano, et al.
Publicado: (2022)
Trainability barriers and opportunities in quantum generative modeling
por: Rudolph, Manuel S., et al.
Publicado: (2023)
por: Rudolph, Manuel S., et al.
Publicado: (2023)
Hybrid model of the kernel method for quantum computers
por: de Borba, Jhordan Silveira, et al.
Publicado: (2024)
por: de Borba, Jhordan Silveira, et al.
Publicado: (2024)
Mitigating Coherent Errors through a Decoherence-Resistant Variational Framework employing Stabilizer State
por: Di Bartolomeo, Giovanni, et al.
Publicado: (2025)
por: Di Bartolomeo, Giovanni, et al.
Publicado: (2025)
Reinforcement learning for ion shuttling on trapped-ion quantum computers
por: Schier, Maximilian, et al.
Publicado: (2026)
por: Schier, Maximilian, et al.
Publicado: (2026)
HyQuRP: Hybrid quantum-classical neural network with rotational and permutational equivariance
por: Park, Semin, et al.
Publicado: (2026)
por: Park, Semin, et al.
Publicado: (2026)
Learning a quantum computer's capability
por: Hothem, Daniel, et al.
Publicado: (2023)
por: Hothem, Daniel, et al.
Publicado: (2023)
Variational measurement-based quantum computation for generative modeling
por: Majumder, Arunava, et al.
Publicado: (2023)
por: Majumder, Arunava, et al.
Publicado: (2023)
Expressivity of deterministic quantum computation with one qubit
por: Kim, Yujin, et al.
Publicado: (2024)
por: Kim, Yujin, et al.
Publicado: (2024)
The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
por: Incudini, Massimiliano, et al.
Publicado: (2022)
por: Incudini, Massimiliano, et al.
Publicado: (2022)
Equivariant Variational Quantum Eigensolver to detect Phase Transitions through Energy Level Crossings
por: Crognaletti, Giulio, et al.
Publicado: (2024)
por: Crognaletti, Giulio, et al.
Publicado: (2024)
Approximately Equivariant Quantum Neural Network for $p4m$ Group Symmetries in Images
por: Chang, Su Yeon, et al.
Publicado: (2023)
por: Chang, Su Yeon, et al.
Publicado: (2023)
Kernel-based optimization of measurement operators for quantum reservoir computers
por: Gross, Markus, et al.
Publicado: (2026)
por: Gross, Markus, et al.
Publicado: (2026)
Biclustering a dataset using photonic quantum computing
por: Borle, Ajinkya, et al.
Publicado: (2024)
por: Borle, Ajinkya, et al.
Publicado: (2024)
Minimizing classical resources in variational measurement-based quantum computation for generative modeling
por: Majumder, Arunava, et al.
Publicado: (2026)
por: Majumder, Arunava, et al.
Publicado: (2026)
Time series generation for option pricing on quantum computers using tensor network
por: Kobayashi, Nozomu, et al.
Publicado: (2024)
por: Kobayashi, Nozomu, et al.
Publicado: (2024)
Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
por: de Schoulepnikoff, Paulin, et al.
Publicado: (2023)
por: de Schoulepnikoff, Paulin, et al.
Publicado: (2023)
Bridging quantum and classical computing for partial differential equations through multifidelity machine learning
por: Jacob, Bruno, et al.
Publicado: (2025)
por: Jacob, Bruno, et al.
Publicado: (2025)
What is my quantum computer good for? Quantum capability learning with physics-aware neural networks
por: Hothem, Daniel, et al.
Publicado: (2024)
por: Hothem, Daniel, et al.
Publicado: (2024)
Topological data analysis on noisy quantum computers
por: Akhalwaya, Ismail Yunus, et al.
Publicado: (2022)
por: Akhalwaya, Ismail Yunus, et al.
Publicado: (2022)
Coherence influx is indispensable for quantum reservoir computing
por: Kobayashi, Shumpei, et al.
Publicado: (2024)
por: Kobayashi, Shumpei, et al.
Publicado: (2024)
Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing
por: Anteneh, Amanuel, et al.
Publicado: (2025)
por: Anteneh, Amanuel, et al.
Publicado: (2025)
Extending echo state property for quantum reservoir computing
por: Kobayashi, Shumpei, et al.
Publicado: (2024)
por: Kobayashi, Shumpei, et al.
Publicado: (2024)
Generative modeling using evolved quantum Boltzmann machines
por: Wilde, Mark M.
Publicado: (2025)
por: Wilde, Mark M.
Publicado: (2025)
Quantum reservoir computing in Jaynes-Cummings models: Nonlinear memory and time-series prediction
por: Das, Sreetama, et al.
Publicado: (2025)
por: Das, Sreetama, et al.
Publicado: (2025)
Sample-based training of quantum generative models
por: Demidik, Maria, et al.
Publicado: (2025)
por: Demidik, Maria, et al.
Publicado: (2025)
Estimating quantum relative entropies on quantum computers
por: Lu, Yuchen, et al.
Publicado: (2025)
por: Lu, Yuchen, et al.
Publicado: (2025)
Is data-efficient learning feasible with quantum models?
por: Sakhnenko, Alona, et al.
Publicado: (2025)
por: Sakhnenko, Alona, et al.
Publicado: (2025)
Ejemplares similares
-
Spectral methods: crucial for machine learning, natural for quantum computers?
por: Belis, Vasilis, et al.
Publicado: (2026) -
Estimates of loss function concentration in noisy parametrized quantum circuits
por: Crognaletti, Giulio, et al.
Publicado: (2024) -
Guided Quantum Compression for High Dimensional Data Classification
por: Belis, Vasilis, et al.
Publicado: (2024) -
Guided Graph Compression for Quantum Graph Neural Networks
por: Casals, Mikel, et al.
Publicado: (2025) -
Better than classical? The subtle art of benchmarking quantum machine learning models
por: Bowles, Joseph, et al.
Publicado: (2024)