When optimizing computing architecture for specific computationally intensive tasks, such as evaluating or training a neural network, it is important to identify what computational subtasks will offer the greatest decrease in cost (e.g., wall clock time or energy usage). This problem is known as Hardware-Software (HS)...
Cameron Ibrahim, S. M. Ferdous, Erdal Mutlu et al.· 0 citations
Feasibility-preserving mixer Hamiltonians offer an attractive alternative to penalty encodings for constrained quantum optimization, yet how enforcing constraints alters the state space reachable by the Quantum Approximate Optimization Algorithm (QAOA) remains largely unexplored. We address this question within the con...
Boris Tsvelikhovskiy, Bao Gia Bach, Ilya Safro· 1 citation
This work introduces a warm-start method based on local correlators obtained from the Quantum Approximate Optimization Algorithm (QAOA), and uses this information to initialize the Burer-Monteiro (BM) rank-two relaxation.
Bao Gia Bach, Ilya Safro, Filip B. Maciejewski· 0 citations
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