Variational quantum algorithms are widely viewed as a route to near-term quantum advantage, but their performance often depends on a small subset of influential parameters and on how entanglement ...
In this tutorial, we present an advanced, hands-on tutorial that demonstrates how we use Qrisp to build and execute non-trivial quantum algorithms. We walk through core Qrisp abstractions for quantum ...
The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical algorithm that seeks to achieve approximate solutions to optimization problems by iteratively alternating between ...
On April 13, 2025, researchers published Hierarchical Quantum Optimization via Backbone-Driven Problem Decomposition: Integrating Tabu-Search with QAOA, detailing a novel hybrid framework that ...
Researchers demonstrated a quantum algorithmic speedup with the quantum approximate optimization algorithm, laying the groundwork for advancements in telecommunications, financial modeling, materials ...
In a new paper in Science Advances on May 29, researchers at JPMorgan Chase, the U.S. Department of Energy’s (DOE) Argonne National Laboratory and Quantinuum have demonstrated clear evidence of a ...
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically ...
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