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Feedback-based Quantum Algorithm Inspired by Counterdiabatic Driving

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arxiv 2401.15303 v2 pith:XS74J4QT submitted 2024-01-27 quant-ph

classification quant-ph
keywords quantumalgorithmcontrolcounterdiabaticdrivingfeedback-basedstatesacceleration
verification ladder T0 review T1 audit T2 compute T3 formal
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In recent quantum algorithmic developments, a feedback-based approach has shown promise for preparing quantum many-body system ground states and solving combinatorial optimization problems. This method utilizes quantum Lyapunov control to iteratively construct quantum circuits. Here, we propose a substantial enhancement by implementing a protocol that uses ideas from quantum Lyapunov control and the counterdiabatic driving protocol, a key concept from quantum adiabaticity. Our approach introduces an additional control field inspired by counterdiabatic driving. We apply our algorithm to prepare ground states in one-dimensional quantum Ising spin chains. Comprehensive simulations demonstrate a remarkable acceleration in population transfer to low-energy states within a significantly reduced time frame compared to conventional feedback-based quantum algorithms. This acceleration translates to a reduced quantum circuit depth, a critical metric for potential quantum computer implementation. We validate our algorithm on the IBM cloud computer, highlighting its efficacy in expediting quantum computations for many-body systems and combinatorial optimization problems.

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  1. Non-Variational ADAPT algorithm for quantum simulations

    quant-ph 2024-11 conditional novelty 5.0 of 10

    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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