Frontiers in Industrial Engineering. Purdue Quantum AI

.- Quantum Machine Learning and Models.

.- Quantum Artificial Intelligence: Reinforcement Learning, Memory Models, and Self-Programming Architectures.

.- Compact Quantum Modules for Scalable Fine-Tuning in Large Language Models.

.- Robustness of Classical and Quantum Inspired Architectures Against Structured Corruptions in Vision Tasks.

.- Geometric Origin of Fourier Coefficients in Data-Reuploading Single-Qubit Circuits.

.- Accelerating Tensor Ring VQE with Batched Contraction.

.- Satellite Constellation Scheduling with Quantum Reinforcement Learning.

.- Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach.

.- Quantum Kernel Methods for Industrial Anomaly Detection.

.- Quantum Optimization and Linear Systems.

.- An Exclusive-Sum-of-Products Pipeline for QAOA.

.- A performance comparison of variable encoding techniques for QUIO
and QUBO problems.

.- Quantum Interior Point Methods: A Review of Developments and an Optimally Scaling Framework.

.- Benchmarking Refined Quantum Linear Systems Algorithms.

.- Building Hybrid Quantum-Classical Computing Ecosystems: A Survey.

.- Quantum Optimization via Gradient-Based Hamiltonian Descent.

.- Quantum Cognition and Human-Centered Computing.

.- Human Factors of Quantum Modeling and Computing.

.- Q-XAI: Towards Explainable Quantum Neural Networks for
Human-Centered Intelligence.

.- QToMNet: A Quantum-enhanced Machine Theory of Mind.

.- Financial Fraud Detection with Entropy Computing.