Rodeo filtering enables direct steady-state estimation for open quantum systems by centering a stochastic spectral filter at the known zero mode of the Liouvillian, achieving logarithmic error scaling and restart capability versus phase estimation.
Filtered Quantum Phase Estimation
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Accurate state preparation is a critical bottleneck in many quantum algorithms, particularly those for ground-state energy estimation. Even in fault-tolerant quantum computing, preparing a quantum state with sufficient overlap with the desired eigenstate remains a major challenge. To address this, we develop a unified cost-aware framework for filtered-state preparation that enhances the overlap of a given input state through spectral filtering. The framework covers polynomial and trigonometric realizations of filters and makes explicit the trade-off among overlap amplification, preparation success probability, and filter-implementation cost. As representative examples, we analyze Gaussian filters and introduce a modified Krylov-subspace-based filter that improves the success-probability/overlap trade-off relevant to filtered state preparation. Within this framework, we study a filtered variant of quantum phase estimation (FQPE) that mitigates the unfavorable dependence on the initial overlap present in standard QPE. Numerical experiments on Fermi-Hubbard models show that FQPE reduces the total runtime by more than two orders of magnitude in the high-precision regime, with overlap amplification exceeding a factor of one hundred.
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper proposes an eigenstate filtering (EF) variant of quantum inverse power iteration (QIPI) that uses symmetric QSVT polynomials to robustly target excited states, showing better convergence than Chebyshev or Fourier approaches on H2, LiH, and BeH2.
citing papers explorer
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Rodeo Filtering for Direct Steady-State Estimation in Open Quantum Systems
Rodeo filtering enables direct steady-state estimation for open quantum systems by centering a stochastic spectral filter at the known zero mode of the Liouvillian, achieving logarithmic error scaling and restart capability versus phase estimation.
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Efficient targeting of arbitrary excited states with quantum inverse power iteration through filtering polynomials
The paper proposes an eigenstate filtering (EF) variant of quantum inverse power iteration (QIPI) that uses symmetric QSVT polynomials to robustly target excited states, showing better convergence than Chebyshev or Fourier approaches on H2, LiH, and BeH2.