Optimal schedule of multi-channel quantum Zeno dragging with application to solving the k-SAT problem
1Berkeley Center for Quantum Information and Computation, Berkeley, California 94720, USA
2Department of Chemistry, University of California, Berkeley, California 94720, USA
3Laboratoire de Physique de l’École Normale Supérieure Inria, ENS, Mines ParisTech, Université PSL, Sorbonne Université Paris France
4Department of Physics, University of California, Berkeley, California 94720, USA
| Published: | 2026-07-20, volume 10, page 2167 |
| Editor: | Milad Marvian |
| Eprint: | arXiv:2507.16128v2 |
| Doi: | https://doi.org/10.22331/q-2026-07-20-2167 |
| Citation: | Quantum 10, 2167 (2026). |
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Abstract
Quantum Zeno dragging enables the preparation of common eigenstates of a set of observables by frequent measurement and adiabatic-like modulation of the measurement basis. In this work, we present a deeper analysis of multi-channel Zeno dragging using generalized measurements, i.e. simultaneously measuring a set of non-commuting observables that vary slowly in time, to drag the state towards a target subspace. For concreteness, we will focus on a measurement-driven approach to solving k-SAT problems as examples. We first compute some analytical upper bounds on the convergence time, including the effect of finite measurement time resolution. We then apply optimal control theory to obtain the optimal dragging schedule that lower bounds the convergence time, for low-dimensional settings. This study provides a theoretical foundation for multi-channel Zeno dragging and its optimization, and also serves as a guide for designing optimal dragging schedules for quantum information tasks including measurement-driven quantum algorithms.
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[1] Franz J. Schreiber, Maximilian J. Kramer, Alexander Nietner, and Jens Eisert, "A measurement-driven quantum algorithm for SAT: Performance guarantees via spectral gaps and measurement parallelization", arXiv:2511.09647, (2025).
[2] Tim Pokart, Frank Pollmann, and Jan Carl Budich, "Entanglement Barriers from Computational Complexity: Matrix-Product-State Approach to Satisfiability", arXiv:2602.20299, (2026).
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