arxiv: 2604.24742 · v1 · submitted 2026-04-27 · 🪐 quant-ph

Recognition: unknown

Application of a Quantum Amplitude Redistribution Algorithm to the Data Filtering Problem

Karina Zakharova , Artem Chernikov , Sergey Sysoev

Authors on Pith no claims yet

Pith reviewed 2026-05-08 03:46 UTC · model grok-4.3

classification 🪐 quant-ph

keywords quantum algorithmsdata filteringamplitude redistributionmedian filterquantum simulationsignal processingnoise removal

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The pith

A quantum amplitude redistribution algorithm can filter data, with classical simulations showing performance comparable to median filters.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper analyzes whether a quantum algorithm for redistributing amplitudes can solve data filtering problems such as noise removal. It includes modeling of the algorithm on test cases and direct comparison of outputs against a classical median filter. A sympathetic reader would care if this shows a path for quantum methods to handle signal cleaning tasks that currently rely on classical sorting or averaging steps. The work establishes applicability through these models rather than a full quantum implementation.

Core claim

The authors establish that the quantum amplitude redistribution algorithm applies to the data filtering problem, as evidenced by classical modeling of its operation that produces results compared directly to those of a median filter.

What carries the argument

The quantum amplitude redistribution algorithm, which modifies amplitudes within a quantum state to achieve filtering by suppressing unwanted signal components.

If this is right

The algorithm could process data filtering tasks on quantum computers once hardware supports the required amplitude operations.
Modeling indicates the approach achieves noise reduction effects similar to median filtering through amplitude adjustment rather than explicit sorting.
This application suggests quantum amplitude methods may extend to related signal processing steps such as smoothing or feature extraction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Hybrid systems could combine this quantum filtering step with classical post-processing to handle larger datasets before full quantum advantage is available.
Real hardware tests would need to account for decoherence and gate errors that classical models omit, potentially altering the observed filtering quality.
If the redistribution preserves certain amplitude ratios under noise, it might connect to other quantum algorithms that rely on probability amplitude control for data tasks.

Load-bearing premise

That classical modeling of the quantum amplitude redistribution algorithm accurately represents its behavior when applied to real data filtering tasks on actual quantum hardware.

What would settle it

Running the amplitude redistribution algorithm on a physical quantum processor with the same noisy input data used in the models and checking whether the filtered output matches or exceeds median filter performance in outlier suppression.

Figures

Figures reproduced from arXiv: 2604.24742 by Artem Chernikov, Karina Zakharova, Sergey Sysoev.

**Figure 1.** Figure 1: Loading the value 𝐴5 = 5. As a result, the index |101⟩ in |𝐶⟩ is entangled with the value |101⟩ in |𝐷⟩ view at source ↗

**Figure 2.** Figure 2: Schematic diagram of the rotations of the quantum algorithm for view at source ↗

**Figure 3.** Figure 3: The first step in finding the decomposition of view at source ↗

**Figure 4.** Figure 4: Decomposition of 𝑅𝑛+1(𝜙). Here sin 𝜓𝑘 2 = √ 1 2 𝑘+1−1 . × (︂ 𝐼 0 0 𝑅𝑛(𝜓) )︂ = (︂ 𝐼 cos 𝜙 2 −𝐼 sin 𝜙 2 𝐼 sin 𝜙 2 𝐼 cos 𝜙 2 )︂ Thus, we find the basis in which the original operator 𝑅𝑛+1(𝜙) acts as a rotation around the 𝑌 axis on the Bloch sphere for the most significant qubit by the angle 𝜙, implemented by just a single gate. To carry out the transformation, it is sufficient to switch to this basis, perform… view at source ↗

**Figure 5.** Figure 5: Probabilities of array indices [5 0 15 10] for the reference value view at source ↗

**Figure 6.** Figure 6: Probabilities of array indices [8 3 29 63 14 2 45 10] for the reference view at source ↗

**Figure 7.** Figure 7: Processing a noisy signal with two outliers (blue) using a median filter view at source ↗

**Figure 8.** Figure 8: Processing a black-and-white 8-bit image with an artifact in PNG for view at source ↗

**Figure 9.** Figure 9: Processing a black-and-white 8-bit image with an artifact in PNG for view at source ↗

**Figure 10.** Figure 10: Processing a black-and-white 8-bit image with an artifact in TIFF view at source ↗

**Figure 11.** Figure 11: Processing a black-and-white 8-bit image with an artifact in TIFF view at source ↗

**Figure 12.** Figure 12: Processing a black-and-white 8-bit MRI image with an artifact in view at source ↗

**Figure 13.** Figure 13: Processing a black-and-white 8-bit MRI image with an artifact in view at source ↗

**Figure 14.** Figure 14: Processing a black-and-white 8-bit MRI image with an artifact in view at source ↗

**Figure 15.** Figure 15: Processing a black-and-white 8-bit MRI image with an artifact in view at source ↗

read the original abstract

This paper presents an analysis of the applicability of a quantum amplitude redistribution algorithm to the data filtering problem and the results of modeling the algorithm's operation in comparison with a median filter.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper runs classical simulations of a quantum amplitude redistribution algorithm on data filtering and compares outputs to a median filter, but the modeling gives no evidence the approach would survive on real quantum hardware.

read the letter

The main takeaway is that the authors simulate a quantum amplitude redistribution algorithm classically for a data filtering task and benchmark it against a median filter. They present this as an applicability analysis plus modeling results. If the simulations are implemented correctly and the comparison metrics are reported clearly, that supplies a baseline data point for anyone tracking quantum methods for signal processing. Beyond that, nothing in the work introduces new quantum primitives or derives fresh theoretical bounds. The simulations appear to be the only concrete output. The central problem is that classical modeling alone cannot establish applicability to quantum hardware. The paper does not report circuit depth, a noise model, data characteristics such as image size or noise type, or any hardware constraints like connectivity or gate fidelity. Without those, the reported performance cannot be extrapolated past the ideal case, and the applicability claim does not hold. This leaves the work as a preliminary simulation study rather than evidence that the algorithm functions under realistic conditions. The paper is aimed at researchers who catalog possible uses of amplitude-based quantum routines on classical problems. A reader seeking either a new algorithmic framework or reproducible evidence of quantum advantage will find little to take away. I would send it for peer review only if the full manuscript contains enough implementation detail for others to reproduce the simulations and if the authors scale back the applicability language to match what the classical runs actually demonstrate. Otherwise it is too thin for referee time.

Referee Report

1 major / 0 minor

Summary. The paper claims that a quantum amplitude redistribution algorithm is applicable to the data filtering problem, as shown by the results of modeling the algorithm's operation and comparing its performance to that of a median filter.

Significance. If the classical modeling faithfully captures the quantum algorithm and demonstrates meaningful performance characteristics relative to the median filter, the work could provide an initial bridge between quantum amplitude techniques and practical signal-processing tasks. However, the absence of any visible equations, circuit descriptions, noise models, or hardware parameters in the provided abstract limits the ability to evaluate whether the claimed applicability would survive realistic quantum constraints.

major comments (1)

Abstract: the central applicability claim rests on 'modeling the algorithm's operation' compared to a median filter, yet no information is given on whether the modeling is a classical simulation, the noise model employed (if any), circuit depth, data characteristics (e.g., image size or noise type), or hardware constraints such as qubit connectivity and gate fidelity. This omission is load-bearing because the conclusion that the algorithm is applicable to data filtering on quantum hardware does not follow from classical modeling alone.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive feedback. We address the major comment below and agree that the abstract requires clarification on the modeling approach to better support the claims.

read point-by-point responses

Referee: Abstract: the central applicability claim rests on 'modeling the algorithm's operation' compared to a median filter, yet no information is given on whether the modeling is a classical simulation, the noise model employed (if any), circuit depth, data characteristics (e.g., image size or noise type), or hardware constraints such as qubit connectivity and gate fidelity. This omission is load-bearing because the conclusion that the algorithm is applicable to data filtering on quantum hardware does not follow from classical modeling alone.

Authors: We agree that the abstract should explicitly describe the modeling methodology to avoid ambiguity. The modeling in the manuscript consists of a classical simulation of the quantum amplitude redistribution algorithm applied to data filtering tasks, performed in an ideal noiseless setting with no noise model included. Circuit depth, qubit connectivity, and gate fidelity are not addressed because the work examines algorithmic applicability rather than hardware realization. Data characteristics involve standard filtering benchmarks such as small-scale images with additive noise, as elaborated in the main text. We acknowledge that demonstrating applicability on actual quantum hardware would require separate analysis of noise and device constraints, which lies outside the present scope. In the revised version we will expand the abstract to state that the results derive from classical simulation of the quantum algorithm in an ideal case, briefly note the data characteristics used, and qualify the applicability claim as algorithmic-level with potential extension to hardware pending further investigation. This revision ensures the conclusion is properly grounded in the modeling performed. revision: yes

Circularity Check

0 steps flagged

No derivation chain or self-referential steps present in the paper

full rationale

The paper describes an analysis of applicability of a quantum amplitude redistribution algorithm to data filtering, supported by results of classical modeling compared against a median filter. No equations, derivations, parameter fittings presented as predictions, uniqueness theorems, or self-citations are referenced in the abstract or described content. The central claim rests on simulation outcomes rather than any claimed first-principles derivation that reduces to its inputs by construction. As a result, there are no load-bearing steps that qualify as circular under the specified patterns, and the work is self-contained as an empirical modeling comparison.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations, parameters, or postulates; free_parameters, axioms, and invented_entities lists are therefore empty.

pith-pipeline@v0.9.0 · 5308 in / 968 out tokens · 49497 ms · 2026-05-08T03:46:58.059785+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

12 extracted references · 1 canonical work pages · 1 internal anchor

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