Qiskit and Q# are major quantum programming languages from IBM and Microsoft, respectively, used for creating and testing ...
Abstract: Recent research has demonstrated the exponential potential of hybrid quantum–classical algorithms (HQAs) in solving electromagnetic (EM) problems. However, the optimization objective of HQAs ...
Quantum computers—devices that process information using quantum mechanical effects—have long been expected to outperform classical systems on certain tasks. Over the past few decades, researchers ...
Abstract: Optimization algorithms are widely employed to tackle complex problems, but designing them manually is often labor-intensive and requires significant expertise. Global placement is a ...
The computing world stands at a historic inflection point. Compute demand for frontier AI is expected to grow 1,000 times over the next four to five years. Classical compute is hitting thermal, energy ...
ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
portfolio-optimization-rl/ ├── src/ │ ├── envs/ │ │ └── portfolio_env.py # Portfolio optimization environments │ ├── agents/ │ │ └── rl_agents.py # RL agent implementations │ └── config.py # ...
Important Note: This project explores quantum-inspired algorithms using classical implementations. All code is research-grade, not production-ready. Hybrid methods combine tensor operations with ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results