In this work, we address a question that has attracted intense interest in recent years: whether machine learning-assisted algorithms can genuinely outperform classical approaches in challenging ...

Optimization algorithms constitute a foundational pillar of computational science, encompassing a spectrum of methods designed to locate minima or maxima of objective functions under a variety of ...

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Abstract: This study explores the integration of quantum computing techniques, specifically the Quantum Approximate Optimization Algorithm (QAOA), with classical portfolio optimization frameworks ...

Optimiz-rs provides blazingly fast, production-ready implementations of advanced optimization and statistical inference algorithms. Built with Rust for maximum performance and exposed to Python ...

Abstract: This research investigates the efficacy of quantum and classical algorithms in the context of portfolio optimization, focusing on a dataset comprising 20 equities from India's National Stock ...

quantum-protein-folding/ ├── qpf/ # Main package │ ├── __init__.py │ ├── encoding.py # Sequence encoding and preprocessing │ ├── circuits.py # Quantum circuit designs │ ├── operators.py # Quantum ...

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 ...

In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability of ...