Rodeo Filtering for Direct Steady-State Estimation in Open Quantum Systems

• URL: http://arxiv.org/abs/2606.29218v1
• Date: Sun, 28 Jun 2026 05:56:34 GMT
• Title: Rodeo Filtering for Direct Steady-State Estimation in Open Quantum Systems
• Abstract summary: Non-equilibrium steady states of open quantum systems are a challenging task on conventional computers.<n>We formulate this task as a known-zero-sector projection problem and implement the corresponding filter using the Rodeo algorithm.<n>In the steady-state setting, the filter can be centered directly at the known zero eigenvalue, avoiding the spectral search required in generic eigenstate preparation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computing non-equilibrium steady states of open quantum systems is a challenging task on conventional computers, motivating quantum algorithms for direct steady-state estimation. A natural route is to regard the steady state as the zero mode of the Liouvillian and to isolate this sector spectrally. We formulate this task as a known-zero-sector projection problem and implement the corresponding filter using the Rodeo algorithm, which performs stochastic spectral filtering through repeated controlled evolutions and measurement-conditioned filtering steps. In the steady-state setting, the filter can be centered directly at the known zero eigenvalue, avoiding the spectral search required in generic eigenstate preparation. Compared with a phase-estimation-based implementation of the same projection, the Rodeo approach enables restart on failure and reduces the target-error dependence of the filtering cost and controlled-evolution depth from power-law to logarithmic. This advantage becomes more pronounced as the spectral separation of the Hermitian Liouvillian embedding increases, allowing Rodeo filtering to outperform phase-estimation filtering already at modest controlled-evolution depths. Our results identify Rodeo filtering as a resource-efficient primitive for estimating steady-state observables in open quantum systems.

Related papers

• Classical Limits of Spectral Filtering in Quantum Generative Models [0.0]
  diagonal filter suppresses the high frequencies associated with finite-sample noise.<n>We examine whether this coherent operation produces anything that classical post-processing of samples from the unfiltered model cannot match.
  arXiv  Detail & Related papers  (2026-08-14T10:29:09Z)
• Closed-Form Spectral Regularization for Multi-Task Model Merging [96.82449201305234]
  Model merging combines several independently fine-tuned experts into a single multi-task model without any training data.<n>State-of-the-art merging methods formulate merging as a layer-wise interference problem.<n>We propose SWUDI, a closed-form method that combines a soft exponential filter, which matches the gradient-flow trajectory of iterative descent.
  arXiv  Detail & Related papers  (2026-06-05T14:00:47Z)
• Optimal filtering for a giant cavity in waveguide QED systems [4.3917238074122]
  In waveguide quantum electrodynamics (QED) systems, a giant cavity can be engineered to interact with quantum fields by multiple distant coupling points.<n>This paper designs an optimal filter for the giant cavity systems to estimate its state evolution under continuous quantum measurements.
  arXiv  Detail & Related papers  (2026-03-24T02:02:41Z)
• Filtered Quantum Phase Estimation [2.6967769052209873]
  We develop a unified framework for filtered-state preparation that enhances the overlap of a given input state through spectral filtering.<n>We further develop a filtered variant of QPE that mitigates the unfavorable dependence on the initial overlap present in standard QPE.<n>Experiments 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.
  arXiv  Detail & Related papers  (2025-10-05T17:11:30Z)
• Feedback stabilization via a quantum projection filter [0.0]
  This paper considers a model of open quantum systems undergoing imperfect measurements obtained via a projection filter approach. We use this approximate filter in the feedback stabilization problem specifically in the case of Quantum Non-Demolition (QND) measurements.
  arXiv  Detail & Related papers  (2024-05-12T21:32:26Z)
• Inverse Unscented Kalman Filter [16.975704972827305]
  A cognitive 'adversary' tracks its target of interest via a framework such as a Kalman filter (KF) The target or 'defender' then employs another inverse unscented filter to infer the forward filter estimates of the defender computed by the adversary. For linear systems, the inverse Kalman filter (I-KF) has been recently shown to be effective in these counter-adversarial applications.
  arXiv  Detail & Related papers  (2023-04-04T10:51:43Z)
• Suppressing Amplitude Damping in Trapped Ions: Discrete Weak Measurements for a Non-unitary Probabilistic Noise Filter [62.997667081978825]
  We introduce a low-overhead protocol to reverse this degradation. We present two trapped-ion schemes for the implementation of a non-unitary probabilistic filter against amplitude damping noise. This filter can be understood as a protocol for single-copy quasi-distillation.
  arXiv  Detail & Related papers  (2022-09-06T18:18:41Z)
• Computational Doob's h-transforms for Online Filtering of Discretely Observed Diffusions [65.74069050283998]
  We propose a computational framework to approximate Doob's $h$-transforms. The proposed approach can be orders of magnitude more efficient than state-of-the-art particle filters.
  arXiv  Detail & Related papers  (2022-06-07T15:03:05Z)
• Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes [71.23286211775084]
  We propose an adaptive low-pass filter based on Gaussian process regression. We show that the estimation error of the proposed method is uniformly bounded.
  arXiv  Detail & Related papers  (2021-11-05T17:06:59Z)
• Approximate Optimal Filter for Linear Gaussian Time-invariant Systems [10.47198224467133]
  State estimation is critical to control systems, especially when the states cannot be directly measured. This paper presents an approximate optimal filter, which enables to use policy iteration technique to obtain the steady-state gain. The results show that the policy converges to the steady-state Kalman gain, and its accuracy is within 2 %.
  arXiv  Detail & Related papers  (2021-03-09T15:51:08Z)
• SCOP: Scientific Control for Reliable Neural Network Pruning [127.20073865874636]
  This paper proposes a reliable neural network pruning algorithm by setting up a scientific control. Redundant filters can be discovered in the adversarial process of different features. Our method can reduce 57.8% parameters and 60.2% FLOPs of ResNet-101 with only 0.01% top-1 accuracy loss on ImageNet.
  arXiv  Detail & Related papers  (2020-10-21T03:02:01Z)
• Dependency Aware Filter Pruning [74.69495455411987]
  Pruning a proportion of unimportant filters is an efficient way to mitigate the inference cost. Previous work prunes filters according to their weight norms or the corresponding batch-norm scaling factors. We propose a novel mechanism to dynamically control the sparsity-inducing regularization so as to achieve the desired sparsity.
  arXiv  Detail & Related papers  (2020-05-06T07:41:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.