Graph representation learning for parameter transferability in quantum approximate optimization algorithm

J Falla, Q Langfitt, Y Alexeev, I Safro - Quantum Machine Intelligence, 2024 - Springer
The quantum approximate optimization algorithm (QAOA) is one of the most promising
candidates for achieving quantum advantage through quantum-enhanced combinatorial …

[HTML][HTML] Scaling whole-chip QAOA for higher-order Ising spin glass models on heavy-hex graphs

E Pelofske, A Bärtschi, L Cincio, J Golden… - npj Quantum …, 2024 - nature.com
We show that the quantum approximate optimization algorithm (QAOA) for higher-order,
random coefficient, heavy-hex compatible spin glass Ising models has strong parameter …

Hybrid quantum-classical multilevel approach for maximum cuts on graphs

A Angone, X Liu, R Shaydulin… - 2023 IEEE High …, 2023 - ieeexplore.ieee.org
Combinatorial optimization is one of the fields where near term quantum devices are being
utilized with hybrid quantum-classical algorithms to demonstrate potentially practical …

Trainability barriers in low-depth qaoa landscapes

J Rajakumar, J Golden, A Bärtschi… - Proceedings of the 21st …, 2024 - dl.acm.org
The Quantum Alternating Operator Ansatz (QAOA) is a prominent variational quantum
algorithm for solving combinatorial optimization problems. Its effectiveness depends on …

Artificial Intelligence for Quantum Computing

Y Alexeev, MH Farag, TL Patti, ME Wolf, N Ares… - ar** provides a geometrically local encoding of the Quantum Approximate
Optimization Algorithm (QAOA), at the expense of having a quadratic qubit overhead for all …

On the Effects of Small Graph Perturbations in the MaxCut Problem by QAOA

L Lavagna, S Piperno, A Ceschini… - arxiv preprint arxiv …, 2024 - arxiv.org
We investigate the Maximum Cut (MaxCut) problem on different graph classes with the
Quantum Approximate Optimization Algorithm (QAOA) using symmetries. In particular …