6 papers
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…
Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems
Andrea Grimaldi, Christian Duffee, Eleonora Raimondo +8
Extremely large-scale multiple-input multiple-output (XL-MIMO) architectures are a key enabler of forthcoming 6G wireless communication networks by allowing high data rates through…
Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping
Filip B. Maciejewski, Jacob Biamonte, Stuart Hadfield +1
We present Noise-Directed Adaptive Remapping (NDAR), a heuristic algorithm for approximately solving binary optimization problems by leveraging certain types of noise. We consider…
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…