Barren plateaus are stretches of the quantum algorithm optimization landscape so unnervingly flat that gradient‑based methods ...

Los Alamos National Laboratory has led the world in trying to understand one of the most frustrating barriers that faces ...

In the training process of classifiers based on variational quantum algorithms (VQA), parameter optimization is one of the most critical steps. Generally, VQA classifiers rely on Parameterized ...

Scientists published the Cascaded Variational Quantum Eigensolver (CVQE) algorithm in a recent article, expected to become a powerful tool to investigate the physical properties in electronic systems.

“The two main methods for solving COPs with quantum devices are variational scheduling and post-processing. Our algorithm combines variational scheduling with a post-processing method that ...

such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), to solve optimization and eigenvalue problems more efficiently than classical counterparts.

by employing variational quantum algorithms (VQA) to construct trainable parameterized quantum gate sequences, with a classical optimizer adjusting the quantum circuit parameters to minimize the ...

An example is a variational quantum migrate solver, which is a hybrid algorithm. “But at the application scale, it gets really interesting,” he says. “If we consider applications rather than ...

SHENZHEN, China, May 2, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO) announced today the launch of their latest classifier auto-optimization technology based ...

WASHINGTON – U.S. Naval Research Laboratory (NRL) scientists published the Cascaded Variational Quantum Eigensolver (CVQE) algorithm in a recent Physical Review Research article, expected to ...