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Feedback-Based Quantum Algorithm for Constrained Optimization Problems
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The feedback-based algorithm for quantum optimization (FALQON) has recently been proposed to solve quadratic unconstrained binary optimization problems. This paper efficiently generalizes FALQON to tackle quadratic constrained binary optimization (QCBO) problems. For this purpose, we introduce a new operator that encodes the problem's solution as its ground state. Using Lyapunov control theory, we design a quantum control system such that the state converges to the ground state of this operator. When applied to the QCBO problem, we show that our proposed algorithm saves computational resources by reducing the depth of the quantum circuit and can perform better than FALQON. The effectiveness of our proposed algorithm is further illustrated through numerical simulations.
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Non-Variational ADAPT algorithm for quantum simulations
NoVa-ADAPT replaces ADAPT-VQE's classical optimization with direct gradient-based parameter updates and reaches comparable measurement cost to ADAPT-VQE on H4 simulations.
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