pith. sign in

arxiv: 2605.03612 · v1 · submitted 2026-05-05 · 🪐 quant-ph · cs.CC

A Critical Comment on 'Entropy Computing: A Paradigm for Optimization in Open Photonic Systems'

Ali Hamed Moosavian , Bahram Abedi Ravan This is my paper

Pith reviewed 2026-05-07 17:14 UTC · model grok-4.3

classification 🪐 quant-ph cs.CC
keywords entropy quantum computingopen photonic systemsoptimizationclassical algorithmscritical commentdecoherence
0
0 comments X

The pith

Entropy Quantum Computing claims can be formalized more carefully but still fail to beat state-of-the-art classical algorithms.

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

This paper examines Entropy Quantum Computing, an approach that seeks to harness environmental noise and decoherence in open photonic systems instead of fighting them. It shows that selected claims from the original work can receive tighter mathematical grounding, yet the strengthened versions still do not demonstrate superiority over leading classical optimization methods run on ordinary computers. The authors frame their findings as descriptive of the approach at its present early stage and explicitly call for continued rigorous testing rather than dismissal.

Core claim

The central claim is that even after making the original Entropy Quantum Computing assertions more rigorous, the paradigm does not currently outperform state-of-the-art classical algorithms on conventional hardware, though this does not preclude future advantages.

What carries the argument

Direct performance comparison of the open photonic EQC model against established classical optimization solvers after reformulating the entropy-as-fuel claims.

If this is right

  • Current EQC prototypes do not deliver a computational edge on standard optimization benchmarks.
  • Any claimed advantage from embracing decoherence must still be demonstrated against the strongest classical baselines.
  • The technology remains at an early developmental stage where classical methods set the performance bar.

Where Pith is reading between the lines

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

  • Identifying narrow problem classes where noise tolerance provides a practical edge could guide targeted hardware experiments.
  • Hybrid classical-quantum workflows that hand off subproblems to EQC might be worth testing before standalone superiority is achieved.
  • Repeating the same benchmarks on larger photonic instances would clarify whether scale changes the current gap.

Load-bearing premise

The critique assumes the set of classical algorithms and hardware resources considered in the comparison is complete enough that no overlooked classical or photonic advantage remains.

What would settle it

An experiment showing a specific optimization instance solved faster or to higher accuracy by a working EQC device than by the best classical solver on equivalent computational resources would refute the no-advantage conclusion.

Figures

Figures reproduced from arXiv: 2605.03612 by Ali Hamed Moosavian, Bahram Abedi Ravan.

Figure 1
Figure 1. Figure 1: The schematics of Dirac-3. Image copied from [29], licensed under CC BY 4.0 view at source ↗
Figure 2
Figure 2. Figure 2: ECA and PSO Convergence on the non-convex polynomial g(x,y). The red line depicts the path of the optimum view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of calculating the Max-k-Cut problem on the Dirac-3 machine and on a conventional classical computer. The first row represents the graph we selected for this comparison; as we didn’t have access to the graph data in view at source ↗
Figure 4
Figure 4. Figure 4: The number of configurations that correspond to a certain cut value, averaged over 1000 random graphs with view at source ↗
Figure 5
Figure 5. Figure 5: Model prediction versus the measurement outcomes. The fit was done by calculating the least squared value of view at source ↗
Figure 1
Figure 1. Figure 1: The plot shows how Evolutionary Centers Algorithms [4] and Particle Swarm Optimization [3] can find the view at source ↗
read the original abstract

In this article, we take a close look at Entropy Quantum Computing (EQC), a computational paradigm developed by Quantum Computing Inc. (QCi), which deviates from mainstream quantum computing by embracing rather than battling environmental noise and decoherence arXiv:2407.04512 . In their words this approach purports EQC as an open quantum system that turns "entropy into super-power fuels of its computing engine". We show that some of the claims in the main article can be made more rigorous, and yet these are still not good enough to beat state of the art classical algorithms on conventional classical computers. Note that these conclusions reflect the technology's current early stage of development and are not meant to discourage its pursuit. Continued rigorous exploration is necessary to fully assess the long-term viability and potential advantages of this distinct computational approach.

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.

Referee Report

1 major / 1 minor

Summary. The manuscript critically examines the Entropy Quantum Computing (EQC) paradigm from arXiv:2407.04512, which embraces environmental noise in open photonic systems for optimization. The authors rigorize selected claims from the original work and conclude that, even with this added rigor, EQC does not outperform state-of-the-art classical algorithms on conventional computers at its present early stage of development, while explicitly calling for continued exploration rather than discouragement.

Significance. If the benchmarking holds, the paper contributes a measured, non-dismissive critique that reinforces the value of direct, rigorous comparisons to classical baselines in evaluating unconventional computing approaches. It models constructive commentary by acknowledging technological immaturity without internal contradictions or overstatements, thereby supporting careful progress in noise-tolerant photonic optimization research.

major comments (1)
  1. [performance comparisons] The central comparison to classical algorithms (abstract and performance discussion): the conclusion that rigorized EQC claims remain insufficient to beat SOTA classical methods rests on the assumption that the referenced benchmarks are comprehensive; without explicit enumeration of problem instances, sizes, or the precise classical solvers and metrics applied, the claim is difficult to verify independently and could be strengthened by additional detail.
minor comments (1)
  1. [abstract] The abstract refers to 'some of the claims' being rigorized but does not list or cross-reference the specific original assertions from arXiv:2407.04512; adding a brief enumeration or table would improve traceability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive assessment of our manuscript and for the constructive suggestion to improve the verifiability of our performance comparisons. We address the major comment below.

read point-by-point responses

  1. Referee: The central comparison to classical algorithms (abstract and performance discussion): the conclusion that rigorized EQC claims remain insufficient to beat SOTA classical methods rests on the assumption that the referenced benchmarks are comprehensive; without explicit enumeration of problem instances, sizes, or the precise classical solvers and metrics applied, the claim is difficult to verify independently and could be strengthened by additional detail.

    Authors: We agree that the current presentation would benefit from greater explicitness. In the revised manuscript we will add a short dedicated paragraph (and, if space permits, a compact table) that enumerates the concrete problem instances drawn from the EQC literature, their sizes (number of variables/qubits), the specific classical solvers used for comparison (e.g., Gurobi, CPLEX, or standard meta-heuristics), and the performance metrics (time-to-solution, solution quality, approximation ratio). This addition will allow readers to assess the comparisons directly without having to consult the referenced EQC paper for every detail. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a critical commentary referencing an external arXiv preprint (2407.04512) and state-of-the-art classical algorithms as independent benchmarks. No equations, derivations, fitted parameters, or self-citations appear in the provided text that reduce any claim to a self-definitional loop or construction from the paper's own inputs. The central assessment—that rigorized claims still fall short of classical performance—is presented as an early-stage evaluation without internal reduction to fitted values or renamed results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper relies on standard concepts from quantum information and complexity theory without introducing new fitted parameters or entities.

axioms (1)
  • domain assumption Open quantum systems can be modeled using standard master equations and decoherence theory.
    Invoked when discussing EQC as an open photonic system that embraces noise.

pith-pipeline@v0.9.0 · 5442 in / 1045 out tokens · 58607 ms · 2026-05-07T17:14:11.125727+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

6 extracted references · 6 canonical work pages

  1. [1]

    Grover Speedup from Many Forms of the Zeno Effect

    J. Berwald, N. Chancellor, and R. Dridi. “Grover Speedup from Many Forms of the Zeno Effect”. In:Quantum8 (2024), p. 1269.doi:10.22331/q-2024-02-29-1269

    work page doi:10.22331/q-2024-02-29-1269 2024

  2. [2]

    Financial Fraud Detection with Entropy Computing

    B. Emami et al. “Financial Fraud Detection with Entropy Computing”. In: 2025. arXiv:2503.11273v1 [quant-ph]

    work page arXiv 2025

  3. [3]

    Particle Swarm Optimization

    J. Kennedy and R. Eberhart. “Particle Swarm Optimization”. In:Proceedings of ICNN’95 - International Conference on Neural Networks. Vol. 4. Nov. 1995, 1942–1948 vol.4.doi:10.1109/ICNN.1995.488968. (Visited on 02/10/2026)

    work page doi:10.1109/icnn.1995.488968 1995

  4. [4]

    A New Evolutionary Optimization Method Based on Center of Mass

    Jesús-Adolfo Mejía-de-Dios and Efrén Mezura-Montes. “A New Evolutionary Optimization Method Based on Center of Mass”. In:Decision Science in Action: Theory and Applications of Modern Decision Analytic Optimisation. Ed. by Kusum Deep, Madhu Jain, and Said Salhi. Singapore: Springer, 2019, pp. 65–74.isbn: 978-981-13-0860-4.doi: 10.1007/978-981-13-0860-4_6. ...

    work page doi:10.1007/978-981-13-0860-4_6 2019

  5. [5]

    EntropyComputing,aParadigmforOptimizationinOpenPhotonicSystems

    LacNguyenetal.“EntropyComputing,aParadigmforOptimizationinOpenPhotonicSystems”.In:Communications Physics8.1 (Oct. 2025), p. 411.issn: 2399-3650.doi:10.1038/s42005-025-02324-6. (Visited on 12/09/2025)

    work page doi:10.1038/s42005-025-02324-6 2025

  6. [6]

    Accessed: Nov 4, 2025.url:https://quantumcomputinginc.com/products/ commercial-products/dirac-3(visited on 11/04/2025)

    Quantum Computing Inc.Dirac-3. Accessed: Nov 4, 2025.url:https://quantumcomputinginc.com/products/ commercial-products/dirac-3(visited on 11/04/2025). 3

    work page 2025