Cover Image

Filtering out mislabeled training instances using black-box optimization and quantum annealing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Otsuka, Makoto, Kodama, Kento, Morita, Keisuke, Ohzeki, Masayuki
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Statistical Mechanics Quantum Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Population Annealing as a Discrete-Time Schrödinger Bridge
by: Ohzeki, Masayuki
Published: (2026)

Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization
by: Haba, Renichiro, et al.
Published: (2025)

Graph minor embedding can affect sampling degenerate ground states using quantum annealing
by: Maruyama, Naoki, et al.
Published: (2021)

Interpretation of Unfair Sampling in Quantum Annealing by Node Centrality
by: Maruyama, Naoki, et al.
Published: (2026)

Duality analysis in symmetric group and its application to random tensor network model
by: Ohzeki, Masayuki
Published: (2023)

Uniformity Bias in Ground-State Sampling Induced by Replica Alignment in Quantum Monte Carlo for Quantum Annealing
by: Maruyama, Naoki, et al.
Published: (2025)

Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
by: Egenlauf, Patrick, et al.
Published: (2025)

Utilizing intermediate states in quantum annealing for multi-objective optimization
by: Takahashi, Keita, et al.
Published: (2026)

Fermi-Dirac thermal measurements: A framework for quantum hypothesis testing and semidefinite optimization
by: Liu, Nana, et al.
Published: (2026)

Improving search efficiency via adaptive acquisition function selection in discrete black-box optimization
by: Shikanai, Reo, et al.
Published: (2026)

Quantitative analysis of the effectiveness of mid-anneal measurement in quantum annealing
by: Takahashi, Keita, et al.
Published: (2025)

Black-box optimization using factorization and Ising machines
by: Tamura, Ryo, et al.
Published: (2025)

Advantages of fixing spins in quantum annealing
by: Hattori, Tomohiro, et al.
Published: (2024)

Circumventing superexponential runtimes for hard instances of quantum adiabatic optimization
by: Schiffer, Benjamin F., et al.
Published: (2023)

Discovering quantum phenomena with Interpretable Machine Learning
by: de Schoulepnikoff, Paulin, et al.
Published: (2026)

Finite-temperature criticality through quantum annealing
by: Teza, Gianluca, et al.
Published: (2025)

Routing and Scheduling Optimization for Urban Air Mobility Fleet Management using Quantum Annealing
by: Haba, Renichiro, et al.
Published: (2024)

Quantum-data-driven dynamical transition in quantum learning
by: Zhang, Bingzhi, et al.
Published: (2024)

Analysis of Critical Points in a Permutation Model on Hierarchical Lattices by Real-Space Renormalization Group
by: Ito, Ryuki, et al.
Published: (2026)

Dynamical transition in controllable quantum neural networks with large depth
by: Zhang, Bingzhi, et al.
Published: (2023)

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

A universal black-box quantum Monte Carlo approach to quantum phase transitions
by: Ezzell, Nic, et al.
Published: (2024)

SMT-AD: a scalable quantum-inspired anomaly detection approach
by: Sornsaeng, Apimuk, et al.
Published: (2026)

Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
by: de Schoulepnikoff, Paulin, et al.
Published: (2025)

A quantum annealing approach to the minimum distance problem of quantum codes
by: Ismail, Refat, et al.
Published: (2024)

Learning quantum many-body data locally: A provably scalable framework
by: Chinzei, Koki, et al.
Published: (2025)

Improving the efficiency of quantum annealing with controlled diagonal catalysts
by: Hattori, Tomohiro, et al.
Published: (2025)

Enhancement of quantum annealing via n-local catalysts
by: Ghosh, Roopayan, et al.
Published: (2024)

All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
by: East, Richard D. P., et al.
Published: (2023)

Simulated outperforms quantum reverse annealing in mean-field models
by: Baldwin, Christopher L.
Published: (2025)

Boltzmann sampling with quantum annealers via fast Stein correction
by: Shibukawa, Ryosuke, et al.
Published: (2023)

Reducing errors and gate operations in digitized quantum annealing with local counterdiabatic driving
by: Hatomura, Takuya
Published: (2024)

Existence of long-range order in random-field Ising model on Dyson hierarchical lattice
by: Okuyama, Manaka, et al.
Published: (2024)

Sampling diverse near-optimal solutions via algorithmic quantum annealing
by: Mohseni, Masoud, et al.
Published: (2021)

Effectiveness of Binary Autoencoders for QUBO-Based Optimization Problems
by: Abe, Tetsuro, et al.
Published: (2026)

Generative Learning of Continuous Data by Tensor Networks
by: Meiburg, Alex, et al.
Published: (2023)

Quantum Inception Score
by: Sone, Akira, et al.
Published: (2023)

Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
by: de Schoulepnikoff, Paulin, et al.
Published: (2023)

Local Diffusion Models and Phases of Data Distributions
by: Hu, Fangjun, et al.
Published: (2025)

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