Towards Provable and Scalable Training of Quantized Neural Networks with Ising Optimization

arXiv:2506.18240 · cs.LG, cs.AI, physics.optics · Submitted 2025-06-23 · Read on arXiv

cs.LG, cs.AI, physics.optics

Submitted: 2025-06-23

Updated: 2026-08-29

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Quantization — 44× in this paper · explained in 12 episodes
• Activation Function — 39× in this paper · explained in 1 episode
• Accuracy — 36× in this paper · explained in 1 episode
• Robustness — 7× in this paper · explained in 5 episodes
• Forward Interval Propagation — 6× in this paper
• Objective Function — 6× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Combinatorial Optimization — 5× in this paper · explained in 2 episodes
• Loss Function — 5× in this paper · explained in 2 episodes
• Quantum Conditional Gradient Descent — 5× in this paper
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• European Conference on Computer Vision — 4× in this paper
• Quantization-Aware Training — 4× in this paper · explained in 3 episodes
• Adiabatic Quantum Computing — 3× in this paper
• Sample Complexity — 3× in this paper · explained in 15 episodes
• VC Dimension — 3× in this paper · explained in 2 episodes
• Adaptability — 2× in this paper · explained in 1 episode
• Block Coordinate Descent — 2× in this paper
• Decomposed Lower-Bound Optimization — 2× in this paper
• F1 Score — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• In International Conference on Learning Representations — 2× in this paper
• Quadratic Constrained Binary Optimization — 2× in this paper
• Abundance — 1× in this paper · explained in 1 episode
• Adiabatic Evolution — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Edge Deployment — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• QCGD and Quantum Progressive Hedging — 1× in this paper
• QUBO Formulation — 1× in this paper · explained in 3 episodes
• Semidefinite Programming (SDP) — 1× in this paper · explained in 3 episodes
• Spectral Gap — 1× in this paper · explained in 5 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
• Is (Selective) Round-To-Nearest Quantization All You Need?
• Training Neural Networks with Universal Adiabatic Quantum Computing
• A White Paper on Neural Network Quantization
• Training Multi-layer Neural Networks on Ising Machine

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks