Copertina

Unlocking the Power of Boltzmann Machines by Parallelizable Sampler and Efficient Temperature Estimation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kubo, Kentaro, Goto, Hayato
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Quantum Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

High-performance multiqubit system with double-transmon couplers: Toward scalable superconducting quantum computers
di: Kubo, Kentaro, et al.
Pubblicazione: (2024)

Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learning
di: Gerlach, Thore, et al.
Pubblicazione: (2025)

On the Sample Complexity of Quantum Boltzmann Machine Learning
di: Coopmans, Luuk, et al.
Pubblicazione: (2023)

Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines
di: Neto, João Marcos Cavalcanti de Albuquerque, et al.
Pubblicazione: (2025)

Using Quantum Solved Deep Boltzmann Machines to Increase the Data Efficiency of RL Agents
di: Kent, Daniel, et al.
Pubblicazione: (2024)

Investigation of D-Wave quantum annealing for training Restricted Boltzmann Machines and mitigating catastrophic forgetting
di: El-Yazizi, Abdelmoula, et al.
Pubblicazione: (2025)

Realization of High-Fidelity CZ Gate based on a Double-Transmon Coupler
di: Li, Rui, et al.
Pubblicazione: (2024)

Parametrically Driven iSWAP Gate Using a Capacitively Shunted Double-Transmon Coupler at the Zero-Flux Sweet Spot
di: Inoue, Shinichi, et al.
Pubblicazione: (2026)

Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification
di: Schuman, Daniëlle, et al.
Pubblicazione: (2025)

Machine Failure Detection Based on Projected Quantum Models
di: Bowden, Larry, et al.
Pubblicazione: (2026)

Meta-learning of Gibbs states for many-body Hamiltonians with applications to Quantum Boltzmann Machines
di: Bhat, Ruchira V, et al.
Pubblicazione: (2025)

Comparison of D-Wave Quantum Annealing and Markov Chain Monte Carlo for Sampling from a Probability Distribution of a Restricted Boltzmann Machine
di: Yazizi, Abdelmoula El, et al.
Pubblicazione: (2025)

Many-hypercube codes: High-rate quantum error-correcting codes for high-performance fault-tolerant quantum computing
di: Goto, Hayato
Pubblicazione: (2024)

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

Capacitively Shunted Double-Transmon Coupler Realizing Bias-Free Idling and High-Fidelity CZ Gate
di: Li, Rui, et al.
Pubblicazione: (2025)

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

Efficient Mutation Testing of Quantum Machine Learning Models
di: Andrews, Emma, et al.
Pubblicazione: (2026)

A Study on Stabilizer Rényi Entropy Estimation using Machine Learning
di: Lipardi, Vincenzo, et al.
Pubblicazione: (2025)

Neural Architecture Search Algorithms for Quantum Autoencoders
di: Kulshrestha, Ankit, et al.
Pubblicazione: (2025)

Surrogate Quantum Circuit Design for the Lattice Boltzmann Collision Operator
di: Lăcătuş, Monica, et al.
Pubblicazione: (2025)

Optimized Many-Hypercube Codes toward Lower Logical Error Rates and Earlier Realization
di: Goto, Hayato
Pubblicazione: (2025)

A Modified Depolarization Approach for Efficient Quantum Machine Learning
di: Khanal, Bikram, et al.
Pubblicazione: (2024)

Efficient Bit Labeling in Factorization Machines with Annealing for Traveling Salesman Problem
di: Koshikawa, Shota, et al.
Pubblicazione: (2024)

QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
di: Kulshrestha, Ankit, et al.
Pubblicazione: (2024)

A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning
di: Cowlessur, Hevish, et al.
Pubblicazione: (2025)

Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential
di: Hino, Kentaro, et al.
Pubblicazione: (2024)

Generative modeling using evolved quantum Boltzmann machines
di: Wilde, Mark M.
Pubblicazione: (2025)

Efficient Quantum Gradient and Higher-order Derivative Estimation via Generalized Hadamard Test
di: Li, Dantong, et al.
Pubblicazione: (2024)

Analogy between Boltzmann machines and Feynman path integrals
di: Iyengar, Srinivasan S., et al.
Pubblicazione: (2023)

Fundamentals of quantum Boltzmann machine learning with visible and hidden units
di: Wilde, Mark M.
Pubblicazione: (2025)

Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors
di: Kim, Gilhan, et al.
Pubblicazione: (2026)

Subsystem many-hypercube codes: High-rate concatenated codes with low-weight syndrome measurements
di: Nakai, Ryota, et al.
Pubblicazione: (2025)

Fault-tolerant quantum computing with a high-rate symplectic double code
di: Kanomata, Naoyuki, et al.
Pubblicazione: (2025)

Quantum chemistry based on classical mechanics inspired by simulated bifurcation
di: Aiga, Fumihiko, et al.
Pubblicazione: (2026)

Quantum-Powered Personalized Learning
di: Zhou, Yifan, et al.
Pubblicazione: (2024)

Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines
di: Schuman, Daniëlle, et al.
Pubblicazione: (2023)

Fast elementary gates for universal quantum computation with Kerr parametric oscillator qubits
di: Kanao, Taro, et al.
Pubblicazione: (2023)

High-performance conditional-driving gate for Kerr parametric oscillator qubits
di: Chono, Hiroomi, et al.
Pubblicazione: (2024)

Subsampling Factorization Machine Annealing
di: Hama, Yusuke, et al.
Pubblicazione: (2025)

Quantum Dynamics of Machine Learning
di: Wang, Peng, et al.
Pubblicazione: (2024)