6 papers
Evolving Hybrid Quantum-Classical Architectures for Image Classification
Devroop Kar, Daniel Krutz, Travis Desell
Hybrid quantum classical neural networks integrate parameterized quantum circuits (PQCs) with established deep learning architectures, but their performance depends strongly on the…
Uncertainty-Driven Replay Memory for Reinforcement Learning
Sheeraja Rajakrishnan, Alexander G. Ororbia, Travis Desell +1
Uncertainty estimation provides promising capabilities for reinforcement learning (RL) agents. Notably, estimating uncertainty can reduce the training time and enable agents to obt…
On Kernel Eigen-alignments of KRR: Reconstruction and Generalization
Yang Liu, Ernest Fokoue, Richard Lange +1
This paper investigates the critical role of eigenalignments between the kernel matrix and learning targets in achieving robust generalization in learning problems. We establish a…
GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation
Steven Szachara, Sheeraja Rajakrishnan, Dylan Jay Van Allen +3
Quantum error mitigation (QEM) is essential for extracting reliable results from near-term quantum devices, yet practical deployments must balance mitigation strength against runti…
Investigating Quantum Circuit Designs Using Neuro-Evolution
Devroop Kar, Daniel Krutz, Travis Desell
Designing effective quantum circuits remains a central challenge in quantum computing, as circuit structure strongly influences expressivity, trainability, and hardware feasibility…
Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions
Devroop Kar, Zimeng Lyu, Sheeraja Rajakrishnan +4
Stock trading has always been a challenging task due to the highly volatile nature of the stock market. Making sound trading decisions to generate profit is particularly difficult…