17 citations · 21 across the 9 of their papers we have counts for
13 papers · 1 filter
Warm-Starting MaxCut Relaxation via Low-Depth Quantum Approximate Optimization Algorithm
Bao G. Bach, Ilya Safro, Filip B. Maciejewski
Quantum optimization has attracted growing interest as quantum hardware continues to improve, yet state-of-the-art classical solvers remain a formidable benchmark for practical uti…
Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting
Filip B. Maciejewski, Stuart Hadfield, Oscar Wallis +5
Progress towards a quantum advantage using known heuristic methods for combinatorial optimization is impeded by hardware noise and limited qubit count. Here, we propose a noise-awa…
Noise-Directed Adaptive Remapping for Integer Optimization: from qubits to (encoded) qudits
Stuart Hadfield, Filip B. Maciejewski, Davide Venturelli
We extend Noise-Directed Adaptive Remapping (NDAR), a recently proposed heuristic meta-algorithm that leverages device noise as a computational resource, to optimization problems o…
Solving Large-Scale QUBO with Transferred Parameters from Multilevel QAOA of low depth
Bao G Bach, Filip B. Maciejewski, Ilya Safro
The Quantum Approximate Optimization Algorithm (QAOA) is a promising quantum approach for tackling combinatorial optimization problems. However, hardware constraints such as limite…
A Multilevel Approach For Solving Large-Scale QUBO Problems With Noisy Hybrid Quantum Approximate Optimization
Filip B. Maciejewski, Bao Gia Bach, Maxime Dupont +5
Quantum approximate optimization is one of the promising candidates for useful quantum computation, particularly in the context of finding approximate solutions to Quadratic Uncons…
Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study
Eleanor G. Rieffel, Ata Akbari Asanjan, M. Sohaib Alam +20
Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum co…