{"total":37,"rows":[{"id":"2305.18321","title":"Training an Ising Machine with Equilibrium Propagation","created":"2023-05-22","cited_by_count":29,"area":null},{"id":"2303.10728","title":"Training Deep Boltzmann Networks with Sparse Ising Machines","created":"2023-03-19","cited_by_count":24,"area":null},{"id":"2107.04032","title":"Adiabatic Quantum Graph Matching with Permutation Matrix Constraints","created":"2021-07-08","cited_by_count":20,"area":null},{"id":"2103.06720","title":"Variational inference with a quantum computer","created":"2021-03-11","cited_by_count":18,"area":null},{"id":"2110.05089","title":"Feature Selection for Recommender Systems with Quantum Computing","created":"2021-10-11","cited_by_count":14,"area":null},{"id":"2205.04346","title":"Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers","created":"2022-05-09","cited_by_count":11,"area":null},{"id":"2312.03179","title":"CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models","created":"2023-12-05","cited_by_count":11,"area":null},{"id":"2510.23972","title":"An efficient probabilistic hardware architecture for diffusion-like models","created":"2025-10-28","cited_by_count":8,"area":null},{"id":"2106.02964","title":"A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications","created":"2021-06-05","cited_by_count":7,"area":null},{"id":"2111.15295","title":"On the challenges of using D-Wave computers to sample Boltzmann Random Variables","created":"2021-11-30","cited_by_count":4,"area":null},{"id":"2210.01311","title":"Quark: A Gradient-Free Quantum Learning Framework for Classification Tasks","created":"2022-10-02","cited_by_count":4,"area":null},{"id":"2304.12418","title":"A hybrid quantum-classical approach for inference on restricted Boltzmann machines","created":"2023-03-31","cited_by_count":3,"area":null},{"id":"2310.15128","title":"Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradients","created":"2023-10-23","cited_by_count":3,"area":null},{"id":"2311.03408","title":"Training Multi-layer Neural Networks on Ising Machine","created":"2023-11-06","cited_by_count":3,"area":null},{"id":"2311.15966","title":"Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines","created":"2023-11-27","cited_by_count":3,"area":null},{"id":"2103.08373","title":"Assessment of image generation by quantum annealer","created":"2021-03-15","cited_by_count":2,"area":null},{"id":"2112.00313","title":"Discriminating Quantum States with Quantum Machine Learning","created":"2021-12-01","cited_by_count":2,"area":null},{"id":"2203.00601","title":"Beyond Ans\\\"atze: Learning Quantum Circuits as Unitary Operators","created":"2022-03-01","cited_by_count":2,"area":null},{"id":"2209.00873","title":"Three Learning Stages and Accuracy-Efficiency Tradeoff of Restricted Boltzmann Machines","created":"2022-09-02","cited_by_count":2,"area":null},{"id":"2307.06542","title":"Quantum Image Denoising: A Framework via Boltzmann Machines, QUBO, and Quantum Annealing","created":"2023-07-13","cited_by_count":2,"area":null},{"id":"2312.12848","title":"Quantum Annealing for Computer Vision Minimization Problems","created":"2023-12-20","cited_by_count":2,"area":null},{"id":"2502.03086","title":"Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing","created":"2025-02-05","cited_by_count":2,"area":null},{"id":"2303.12352","title":"Training Multilayer Perceptrons by Sampling with Quantum Annealers","created":"2023-03-22","cited_by_count":1,"area":null},{"id":"2508.10228","title":"Comparison of D-Wave Quantum Annealing and Gibbs Monte Carlo for Sampling from a Probability Distribution of a Restricted Boltzmann Machine","created":"2025-08-13","cited_by_count":1,"area":null},{"id":"2107.08710","title":"Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer","created":"2021-07-19","cited_by_count":0,"area":null},{"id":"2301.05146","title":"Heuristic for Diverse Kemeny Rank Aggregation based on Quantum Annealing","created":"2023-01-12","cited_by_count":0,"area":null},{"id":"2401.01996","title":"Mean-Field Assisted Deep Boltzmann Learning with Probabilistic Computers","created":"2024-01-03","cited_by_count":0,"area":null},{"id":"2401.12485","title":"Adiabatic Quantum Support Vector Machines","created":"2024-01-23","cited_by_count":0,"area":null},{"id":"2405.18622","title":"Biclustering a dataset using photonic quantum computing","created":"2024-05-28","cited_by_count":0,"area":null},{"id":"2410.12636","title":"Towards Arbitrary QUBO Optimization: Analysis of Classical and Quantum-Activated Feedforward Neural Networks","created":"2024-10-16","cited_by_count":0,"area":null},{"id":"2506.00247","title":"Performance Analysis of Convolutional Neural Network By Applying Unconstrained Binary Quadratic Programming","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2507.14116","title":"Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification","created":"2025-07-18","cited_by_count":0,"area":null},{"id":"2508.15697","title":"Investigation of D-Wave quantum annealing for training Restricted Boltzmann Machines and mitigating catastrophic forgetting","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2512.02323","title":"Training Energy-Based Models with Non-MCMC Samplers and Efficient Temperature Estimation","created":"2025-12-02","cited_by_count":0,"area":null},{"id":"2512.17660","title":"Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2606.09117","title":"Optimizing Energy-based Neural Network Training with Coherent Ising Machine","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2607.24065","title":"Variational Quantum Conditional Boltzmann Machines for Time-Series Forecasting: Architectures, Symmetric Hyperparameter Evaluation, and a Nonlinear Benchmark","created":"2026-07-27","cited_by_count":0,"area":null}],"next_cursor":null}