From the 1 of 7 linked papers with an AI index.
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Separating Geometry From Interference in Constrained Quantum Optimization
Chinonso Onah, Stuart Hadfield, Kristel Michielsen
The paper analyzes how constraint‑preserving mixing operators move quantum amplitudes in constrained optimization problems and shows that quantum advantage depends on aligning the…
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…
Measurement-driven Quantum Approximate Optimization
Tobias Stollenwerk, Stuart Hadfield
Algorithms based on non-unitary evolution have attracted much interest for ground state preparation on quantum computers. One recently proposed method makes use of ancilla qubits a…
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…
Challenges and Opportunities in Quantum Optimization
Amira Abbas, Andris Ambainis, Brandon Augustino +43
Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum a…