Imagen de Portada

Analogy between Boltzmann machines and Feynman path integrals

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Iyengar, Srinivasan S., Kais, Sabre
Formato:	Preprint
Publicado:	2023
Materias:	Quantum Physics Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Enhancing Quantum Federated Learning with Fisher Information-Based Optimization
por: Bhatia, Amandeep Singh, et al.
Publicado: (2025)

Multi-Omic and Quantum Machine Learning Integration for Lung Subtypes Classification
por: Saggi, Mandeep Kaur, et al.
Publicado: (2024)

A large language model-type architecture for high-dimensional molecular potential energy surfaces
por: Zhu, Xiao, et al.
Publicado: (2024)

Shadows of quantum machine learning
por: Jerbi, Sofiene, et al.
Publicado: (2023)

Training-efficient density quantum machine learning
por: Coyle, Brian, et al.
Publicado: (2024)

Quantum-inspired tensor networks in machine learning models
por: Valverde, Guillermo, et al.
Publicado: (2026)

Walking with the atoms in a chemical bond : A perspective using quantum phase transition
por: Kais, Sabre
Publicado: (2022)

Generative quantum machine learning via denoising diffusion probabilistic models
por: Zhang, Bingzhi, et al.
Publicado: (2023)

Quantum-secure multiparty deep learning
por: Sulimany, Kfir, et al.
Publicado: (2024)

Training quantum machine learning models on cloud without uploading the data
por: He, Guang Ping
Publicado: (2024)

Universal scaling laws in quantum-probabilistic machine learning by tensor network towards interpreting representation and generalization powers
por: Bai, Sheng-Chen, et al.
Publicado: (2024)

Cutoff phenomenon and entropic uncertainty for random quantum circuits
por: Oh, Sangchul, et al.
Publicado: (2023)

Universal Complex Quantum-Like Bits from Hermitian Weighted Graphs
por: Dickey, Ethan, et al.
Publicado: (2026)

The qubit information logic theory for understanding multi-qubit entanglement and designing exotic entangled states
por: Hu, Zixuan, et al.
Publicado: (2024)

A Classical Analogue of Entanglement for a Kicked Top
por: Khalid, Bilal, et al.
Publicado: (2024)

Expressive equivalence of classical and quantum restricted Boltzmann machines
por: Demidik, Maria, et al.
Publicado: (2025)

Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing
por: Sinno, Salvatore, et al.
Publicado: (2025)

Non-native Quantum Generative Optimization with Adversarial Autoencoders
por: Wilson, Blake A., et al.
Publicado: (2024)

Privacy-preserving machine learning with tensor networks
por: Pozas-Kerstjens, Alejandro, et al.
Publicado: (2022)

Breaking QAOA's Fixed Target Hamiltonian Barrier: A Fully Connected Quantum Boltzmann Machine via Bilevel Optimization
por: Liu, Jun
Publicado: (2026)

Structured quantum learning via em algorithm for Boltzmann machines
por: Kimura, Takeshi, et al.
Publicado: (2025)

What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement
por: Alexander, Yotam, et al.
Publicado: (2023)

Enhancing variational quantum state diagonalization using reinforcement learning techniques
por: Kundu, Akash, et al.
Publicado: (2023)

Quantum circuit synthesis with diffusion models
por: Fürrutter, Florian, et al.
Publicado: (2023)

Tensor Networks for Explainable Machine Learning in Cybersecurity
por: Aizpurua, Borja, et al.
Publicado: (2023)

Simulation of a Variational Quantum Perceptron using Grover's Algorithm
por: Innan, Nouhaila, et al.
Publicado: (2023)

Computing the gradients with respect to all parameters of a quantum neural network using a single circuit
por: He, Guang Ping
Publicado: (2023)

The power and limitations of learning quantum dynamics incoherently
por: Jerbi, Sofiene, et al.
Publicado: (2023)

Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation
por: Hasan, Mohammad Junayed, et al.
Publicado: (2023)

Approximately Equivariant Quantum Neural Network for $p4m$ Group Symmetries in Images
por: Chang, Su Yeon, et al.
Publicado: (2023)

Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes
por: Ganguly, Bhargav, et al.
Publicado: (2023)

Noise-Agnostic Quantum Error Mitigation with Data Augmented Neural Models
por: Liao, Manwen, et al.
Publicado: (2023)

Fundamental causal bounds of quantum random access memories
por: Wang, Yunfei, et al.
Publicado: (2023)

Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
por: Zhong, Han, et al.
Publicado: (2023)

Financial Fraud Detection using Quantum Graph Neural Networks
por: Innan, Nouhaila, et al.
Publicado: (2023)

Variational measurement-based quantum computation for generative modeling
por: Majumder, Arunava, et al.
Publicado: (2023)

Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments
por: Jaouni, Tareq, et al.
Publicado: (2023)

Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation
por: Cheng, Jinglei, et al.
Publicado: (2024)

Improved Offline Reinforcement Learning via Quantum Metric Encoding
por: Lv, Outongyi, et al.
Publicado: (2025)

QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memory
por: Hsu, Yu-Chao, et al.
Publicado: (2025)