QAOA is analyzed with QFI on cyclic and complete graphs showing entanglement redistributes sensitivity from diagonal to off-diagonal terms, and a QFI-informed mutation heuristic improves mean energy and reduces variance on 7- and 10-qubit instances.
Comparison of QAOA with Quantum and Simulated Annealing
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abstract
We present a comparison between the Quantum Approximate Optimization Algorithm (QAOA) and two widely studied competing methods, Quantum Annealing (QA) and Simulated Annealing (SA). To achieve this, we define a class of optimization problems with respect to their spectral properties which are exactly solvable with QAOA. In this class, we identify instances for which QA and SA have an exponentially small probability to find the solution. Consequently, our results define a first demarcation line between QAOA, Simulated Annealing and Quantum Annealing, and highlight the fundamental differences between an interference-based search heuristic such as QAOA and heuristics that are based on thermal and quantum fluctuations like SA and QA respectively.
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A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
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Exploring Entanglement and Parameter Sensitivity in QAOA through Quantum Fisher Information
QAOA is analyzed with QFI on cyclic and complete graphs showing entanglement redistributes sensitivity from diagonal to off-diagonal terms, and a QFI-informed mutation heuristic improves mean energy and reduces variance on 7- and 10-qubit instances.
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A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.