Foundational optimization algorithms are the core driving force behind deep learning, evolving from early stochastic gradient descent (SGD) to the widely adopted Adam family. However, as the scale of ...

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 ...

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 ...

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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 ...

Optimization on quantum hardware is required in many applications, including Hamiltonian simulation to quantum machine learning; this entails interesting problems that must be addressed both for noisy ...

Abstract: Widely used quantum optimization algorithms such as quantum annealing and quantum approximate optimization algorithm (QAOA) for quadratically unconstrained binary optimization (QUBO) ...