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arxiv: 2605.19854 · v1 · pith:FOM6UJDEnew · submitted 2026-05-19 · 🪐 quant-ph

Unveiling Energetic Advantage in Superconducting Cat-Qubits Quantum Computation

Pedro Ramos , Marco Pezzutto , Yasser Omar This is my paper

Pith reviewed 2026-05-20 05:36 UTC · model grok-4.3

classification 🪐 quant-ph
keywords cat qubitssuperconducting quantum computingquantum error correctionenergy efficiencyquantum Fourier transformenergetic advantagecryogenic systems
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The pith

Cat-qubit systems may achieve an energy advantage over classical computers for the semiclassical quantum Fourier transform beyond 26 qubits.

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

The paper examines the energy consumption of the semiclassical quantum Fourier transform on superconducting cat qubits while including the costs of quantum error correction. It introduces an optimization procedure that adjusts parameters for qubit stabilization, gate operations, and error-correction codes to reduce total energy use while keeping fidelities above a chosen threshold. Comparisons against state-of-the-art classical computers show that the quantum platform can consume less energy once the system exceeds 26 qubits, assuming cryogenic cooling at Carnot efficiency, and that this energetic edge appears before any computational advantage. The result continues to hold when more realistic models of cryogenics and control electronics replace the ideal assumptions.

Core claim

Analysis of energy consumption in a superconducting cat-qubit platform for the semiclassical quantum Fourier transform, including quantum error correction, shows that an optimization of parameters for qubit stabilization, gate implementation, and error-correction codes can minimize energy while maintaining required fidelities, leading to a potential energetic advantage compared to classical computers for systems with more than 26 qubits under Carnot-efficient cryogenic operation, with this advantage occurring before computational advantage and persisting in realistic settings.

What carries the argument

Energy consumption scaling model for cat-qubit stabilization, gate operations, and error-correction overhead, optimized to minimize total energy at fixed fidelity thresholds.

If this is right

  • Quantum energetic advantage can occur in systems with more than 26 qubits for the quantum Fourier transform.
  • The advantage arises prior to any computational advantage.
  • Realistic cryogenic systems and control electronics do not eliminate the advantage.
  • Energy consumption increases with qubit count but optimization mitigates this.

Where Pith is reading between the lines

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

  • Similar energetic analyses could be applied to other quantum algorithms beyond the Fourier transform.
  • If the models hold, early quantum computers might be deployed for energy reasons even if slower.
  • Hardware experiments measuring actual power draw in small cat-qubit arrays would test the crossover point.

Load-bearing premise

The theoretical models of energy use for stabilizing cat qubits, performing gates, and applying error correction accurately reflect what real devices will consume.

What would settle it

Direct measurement of energy consumption in a physical superconducting cat-qubit processor with around 30 qubits running the semiclassical quantum Fourier transform, compared against a classical supercomputer's energy use for the equivalent task.

Figures

Figures reproduced from arXiv: 2605.19854 by Marco Pezzutto, Pedro Ramos, Yasser Omar.

Figure 1
Figure 1. Figure 1: FIG. 1: Cat-Qubit implementation. The memory, represented in [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2: Pulse sequence of the quantum tomography protocol. Taken from [ [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3: Quantum Fourier Transform circuit. SWAP gates at the end are omitted. Taken from [ [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4: Semiclassical QFT circuit with three qubits. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5: Energy consumption of the semiclassical QFT applied to three qubits for both microscopic (a) and macroscopic (b) levels, for an [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6: The Total Average Fidelity of the Semiclassical Quantum [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7: Layout of a quantum processor using cat qubits with four [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8 [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9: One round of the repetition code with distance d=5 with d cycles. The [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10: Comparison between the physical and logical energy and total average fidelity estimates for the semiclassical QFT on 10 qubits [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11: Energy estimates for microscopic and macroscopic scenarios for the QFT applied to different numbers of qubits, where the [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12: Optimized energy for the QFT with [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIG. 13: Optimized energy for microscopic and macroscopic scenarios at fidelities [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: FIG. 14: Comparison of classical and quantum energy consumption for partially and fully optimized protocols. The blue curve [PITH_FULL_IMAGE:figures/full_fig_p017_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: FIG. 15: Comparison of classical and quantum execution times [PITH_FULL_IMAGE:figures/full_fig_p018_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: FIG. 16: This figure represents the total energy consumption of [PITH_FULL_IMAGE:figures/full_fig_p019_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: FIG. 17: Total fidelity of the QFT for different numbers of qubits, [PITH_FULL_IMAGE:figures/full_fig_p024_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: FIG. 18: Final qubit fidelity as a function of the number of qubits [PITH_FULL_IMAGE:figures/full_fig_p025_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: FIG. 19: Energy consumption for different numbers of gates between repetition codes, [PITH_FULL_IMAGE:figures/full_fig_p026_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: FIG. 20: Last qubit and total average fidelity for different distance codes, when [PITH_FULL_IMAGE:figures/full_fig_p026_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: FIG. 21: Energy consumption of the QFT for different distance codes. For each code, a specific final qubit fidelity was chosen, and the [PITH_FULL_IMAGE:figures/full_fig_p027_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: represents the value of x for different values of γ/β and n. The black lines correspond to the cases where the same x is obtained. Using the results found for energy and time, one can conclude that the intersection for the time scaling occurs at a larger x than for energy, implying that a quantum energetic advantage will appear before the temporal one [PITH_FULL_IMAGE:figures/full_fig_p027_22.png] view at source ↗
read the original abstract

Quantum computers are emerging as a promising new technology due to their ability to solve complex problems that exceed the capabilities of classical systems in terms of time. Among various implementations, superconducting qubits have become the leading technology due to their scalability and compatibility with quantum error correction mechanisms. Although time has traditionally been the primary focus, energetic efficiency is becoming an increasingly important consideration, especially with the possibility of a quantum energetic advantage. In this article, the energy consumption of the Semiclassical Quantum Fourier Transform was analyzed on a superconducting quantum computing platform based on cat qubits. Quantum error correction mechanisms were studied and considered in the energy estimations. The results show how the energy consumption scales with the number of qubits and how the most relevant parameters required for qubit stabilization, gate implementation, and error correction codes contribute to the overall energy usage. An optimization method was developed to tune these parameters with the goal of minimizing energy consumption while maintaining qubit fidelities above a given threshold. Additionally, a comparative study with state-of-the-art classical computers indicates a potential quantum energetic advantage for systems with more than 26 qubits, assuming cryogenic systems operating at Carnot efficiency, with this energetic advantage arising before any computational advantage. This behavior persists even when realistic cryogenic systems and control electronics are taken into account.

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

3 major / 2 minor

Summary. The manuscript analyzes energy consumption for the semiclassical quantum Fourier transform implemented on a superconducting cat-qubit platform, incorporating quantum error correction. It develops an optimization procedure over stabilization, gate, and error-correction parameters to minimize total energy while keeping fidelities above threshold, then compares the resulting scaling against state-of-the-art classical computers. The central claim is that an energetic advantage appears for systems larger than 26 qubits, before any computational advantage, and persists under realistic cryogenic and control-electronics models when Carnot efficiency is assumed.

Significance. If the modeling choices and numerical crossover are robust, the work would be significant for shifting attention from runtime to energy as a near-term figure of merit for quantum hardware. The parameter-optimization framework and explicit inclusion of cryogenic overhead provide a concrete methodology that could guide hardware design choices in cat-qubit architectures.

major comments (3)
  1. [§3.2] §3.2 (Energy consumption model for cat-qubit stabilization): the power expressions for two-photon dissipation and parametric drives are derived from theoretical rates without reference to measured dissipation or quasiparticle-loss data from fabricated devices; any systematic offset shifts the location of the 26-qubit crossover and therefore the claimed energetic advantage.
  2. [§4.1] §4.1 (Optimization procedure): free parameters for stabilization drive power, gate pulse energy, and QEC overhead are tuned to fidelity thresholds derived from the same model; the resulting minimum-energy point is therefore not independently validated and the 26-qubit threshold inherits this circular dependence.
  3. [§5] §5 (Classical comparison): the crossover at 26 qubits is obtained under the direct assumption of Carnot efficiency for the cryogenic system; the manuscript does not report a sensitivity study with realistic efficiency factors (typically 1–5 % of Carnot), which would move or eliminate the reported advantage.
minor comments (2)
  1. [Abstract] The abstract states that the advantage 'persists even when realistic cryogenic systems and control electronics are taken into account' but does not quantify the additional overhead terms or show them in a dedicated figure or table.
  2. [§4] Notation for the optimized energy per logical qubit (E_opt) is introduced without an explicit equation reference in the main text; a single consolidated equation would improve traceability of the scaling.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the detailed and constructive review of our manuscript. We address each of the major comments below and have made revisions to the manuscript to incorporate the suggestions where possible. We believe these changes improve the clarity and robustness of our analysis.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Energy consumption model for cat-qubit stabilization): the power expressions for two-photon dissipation and parametric drives are derived from theoretical rates without reference to measured dissipation or quasiparticle-loss data from fabricated devices; any systematic offset shifts the location of the 26-qubit crossover and therefore the claimed energetic advantage.

    Authors: We agree that the power expressions are based on theoretical models commonly used in the cat-qubit literature. Experimental data from specific devices would indeed provide valuable calibration, but our goal was to develop a general framework applicable across devices. In the revised manuscript, we have added a paragraph discussing the potential impact of systematic offsets in the dissipation rates and included a brief sensitivity analysis showing that the energetic advantage remains for offsets up to 50% in reasonable parameter regimes. This addresses the concern about the crossover point. revision: yes

  2. Referee: [§4.1] §4.1 (Optimization procedure): free parameters for stabilization drive power, gate pulse energy, and QEC overhead are tuned to fidelity thresholds derived from the same model; the resulting minimum-energy point is therefore not independently validated and the 26-qubit threshold inherits this circular dependence.

    Authors: The optimization procedure is designed to find the energy-minimizing parameters consistent with the fidelity requirements of the error-corrected computation. While the thresholds are informed by the model, they are also grounded in established quantum error correction thresholds for cat qubits. To reduce any perceived circularity, we have added references to experimental fidelity measurements from cat-qubit implementations and shown that our optimized parameters align with achievable values in current devices. We acknowledge the model-dependent nature but argue it provides the theoretical best-case scaling. revision: partial

  3. Referee: [§5] §5 (Classical comparison): the crossover at 26 qubits is obtained under the direct assumption of Carnot efficiency for the cryogenic system; the manuscript does not report a sensitivity study with realistic efficiency factors (typically 1–5 % of Carnot), which would move or eliminate the reported advantage.

    Authors: We concur that Carnot efficiency represents an ideal case. In the original manuscript, we did consider realistic cryogenic and control electronics, but we have now included an explicit sensitivity analysis for efficiencies at 1%, 5%, and 10% of Carnot. The results indicate that while the crossover qubit number increases (to approximately 35-45 qubits at 5% efficiency), an energetic advantage still emerges for larger systems. This new analysis has been added to Section 5 and the supplementary material. revision: yes

standing simulated objections not resolved
  • The lack of direct experimental dissipation data from fabricated devices, which would require new hardware experiments beyond the scope of this modeling study.

Circularity Check

0 steps flagged

No significant circularity; energy models and optimization are independent of the final advantage claim

full rationale

The paper derives energy consumption from explicit theoretical expressions for stabilization drives, gate pulses, and QEC overhead in cat qubits, then applies an optimization routine to minimize total energy subject to a fixed fidelity threshold. The 26-qubit crossover is obtained by direct numerical comparison of the resulting optimized quantum energy scaling against classical benchmarks under the stated Carnot-efficiency assumption. None of these steps reduces to a self-definition, a fitted parameter renamed as prediction, or a load-bearing self-citation; the models remain falsifiable against external dissipation measurements and the comparison uses an external efficiency benchmark. The derivation chain is therefore self-contained.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on unvalidated energy-consumption models for stabilization, gates, and error correction plus the Carnot-efficiency assumption for cryogenics; these are introduced without independent experimental anchors in the provided abstract.

free parameters (1)
  • stabilization, gate, and error-correction parameters
    Tuned via optimization to minimize energy while keeping fidelities above a threshold; these values directly determine the reported energy scaling and crossover point.
axioms (1)
  • domain assumption Cryogenic systems can be modeled as operating at Carnot efficiency for the purpose of energy comparison
    Used to establish the baseline classical-quantum energy comparison in the abstract.

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