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arxiv: 2605.15090 · v1 · submitted 2026-05-14 · 🪐 quant-ph

Recognition: 2 theorem links

· Lean Theorem

Energy efficiency of quantum computers

Miquel Carrasco-Codina , Pau Escofet , Paul Hilaire , Ariane Soret , Sam Nerenberg , Victor Champain , Gerard Milburn , Klara Theophilo
show 8 more authors
Sophie H. Li Irais Bautista Andr\'es G\'omez Jose Miralles Sergi Abadal Carmen G. Almud\'ever Eduard Alarc\'on Raja Yehia
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Pith reviewed 2026-05-15 03:13 UTC · model grok-4.3

classification 🪐 quant-ph
keywords quantum computingenergy efficiencysuperconducting qubitstrapped ionsneutral atomsphotonic qubitssilicon spin qubits
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The pith

Energy efficiency of a quantum computer is defined as the number of algorithms it can run per unit of energy consumed, including all hardware overheads.

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

The paper defines energy efficiency for quantum computers as the ratio of algorithms executable in a fixed time interval to the total energy drawn by the hardware during that interval. It applies this definition to five representative platforms—superconducting qubits, silicon spin qubits, trapped ions, neutral atoms, and photonic qubits—incorporating expert estimates of power requirements together with cooling, control electronics, and compilation overheads. The resulting analysis supplies concrete energy-consumption figures for present machines and creates a uniform benchmark that any future architecture can be measured against on the same scale.

Core claim

The authors define the energy efficiency of a quantum computer as the number of algorithms it can perform during a given time divided by the energy consumed by the hardware during that time. They evaluate this quantity across the leading physical platforms by combining expert assessments of power draw with algorithm compilation constraints, thereby generating numerical energy figures for existing devices and establishing a reusable framework for comparing any future quantum computing architecture.

What carries the argument

The energy-efficiency ratio (algorithms per unit energy), which incorporates full-system power consumption including cooling, control electronics, and compilation costs to rank platforms.

If this is right

  • Each of the five platforms exhibits distinct energy efficiencies once cooling, control, and compilation overheads are included.
  • Current quantum computers have concrete, benchmarkable energy-consumption values that differ by platform.
  • The framework enables consistent side-by-side evaluation of any new quantum architecture on energy grounds.
  • Expert-derived insights identify platform-specific advantages such as lower cooling demands and inconveniences such as higher control power.

Where Pith is reading between the lines

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

  • The metric could inform hardware selection for future energy-constrained quantum data centers.
  • Reductions in classical control electronics or compiler efficiency could improve overall energy performance without changes to the quantum hardware.
  • The approach could be extended to hybrid quantum-classical workloads to capture complete end-to-end energy costs.

Load-bearing premise

The energy consumption estimates for each platform, including overheads from cooling, control systems, and algorithm compilation, are accurate and representative based on expert insights.

What would settle it

A direct measurement of the total electrical energy drawn by an operational quantum computer of each platform while running a standardized set of algorithms, compared against the paper's estimated figures.

Figures

Figures reproduced from arXiv: 2605.15090 by Andr\'es G\'omez, Ariane Soret, Carmen G. Almud\'ever, Eduard Alarc\'on, Gerard Milburn, Irais Bautista, Jose Miralles, Klara Theophilo, Miquel Carrasco-Codina, Pau Escofet, Paul Hilaire, Raja Yehia, Sam Nerenberg, Sergi Abadal, Sophie H. Li, Victor Champain.

Figure 1
Figure 1. Figure 1: Effect of the transpilation step. Relative depth overhead ratio of the QFT, CDKM adder and GHZ state preparation circuits for different basis gate sets in a 100 qubit quantum computer, based on the Qiskit transpiler. The labels of the gates in the x-axis correspond to the Pauli Z gate (Z), controlled gates (CX and CZ), the Hadamard gate (H), parametric rotations (RX and RZ), the phase gate (S) and the maxi… view at source ↗
Figure 2
Figure 2. Figure 2: Effect of the routing for solid-state platforms. Post-routing circuit depth of a circuit as a function of D0. The three panels correspond to different ratios of layers with two-qubit gates α2q depth D0 executed in a solid-state qubit based processor can be approximated as follows: Dsolid−state ≈ D0 + α2q · 3 & d(G) − 1 2 ' , (10) where d(G) is the average distance between nodes of the graph G representing … view at source ↗
Figure 3
Figure 3. Figure 3: Power breakdown of a superconducting-qubit computer with [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Energy efficiency (left y-axis) and number of computations (right y-axis) in 24 hours of a [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Energy efficiency of a superconducting-qubit computer, depending on the depth of the executed [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Power breakdown of a spin-qubit computer with 49 qubits, according to the components shown [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Energy efficiency (left y-axis) and number of computations (right y-axis) in 24 hours of a spin [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Energy efficiency of a spin-qubit computer executing 1 sample of a circuit, depending on its [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Power breakdown of a trapped ions computer, according to the components shown in Table 6. [PITH_FULL_IMAGE:figures/full_fig_p022_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Energy efficiency (left y-axis) and number of computations (right y-axis) in 24 hours of a [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The upper panel shows the minimum value of the transport per layer ratio [PITH_FULL_IMAGE:figures/full_fig_p025_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Power breakdown of a neutral-atoms computer, according to the components shown in Table 8. [PITH_FULL_IMAGE:figures/full_fig_p027_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Energy efficiency (left y-axis) and number of computations (right y-axis) in 24 hours of a [PITH_FULL_IMAGE:figures/full_fig_p029_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Energy efficiency of a neutral-atoms computer executing 1 sample of a circuit, depending on [PITH_FULL_IMAGE:figures/full_fig_p030_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Power breakdown of a photonic quantum computer of 12 qubits (24 modes) according to the [PITH_FULL_IMAGE:figures/full_fig_p033_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Energy efficiency of a photonic quantum computer as a function of the number of samples, [PITH_FULL_IMAGE:figures/full_fig_p034_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Comparison between state-of-the-art chip technologies assuming a 12-qubit photonic quantum [PITH_FULL_IMAGE:figures/full_fig_p035_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Energy breakdown of a QMIO superconducting-qubits computer (32 qubits), according to the [PITH_FULL_IMAGE:figures/full_fig_p042_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: ). v0 v1 v2 v3 v4 = v0 v1 v2 v3 v4 = v0 v1 v1 v2 v2 v0 v3 v4 v4 v3 [PITH_FULL_IMAGE:figures/full_fig_p048_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Different types of connectivity considered in this work for solid-state-qubits computers. Panel [PITH_FULL_IMAGE:figures/full_fig_p050_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Diagram of a circuit being compiled into two atom-based devices with 2 and 4 gate zones. The [PITH_FULL_IMAGE:figures/full_fig_p050_21.png] view at source ↗
read the original abstract

How much energy does a quantum computer consume? Are they more efficient than their classical counterparts? In this work, we make a step towards answering these questions. We define the energy efficiency of a quantum computer as the ratio of the number of algorithms it can perform during a given time over the energy consumed by the hardware during this time. We analyze the most representative physical platforms currently envisioned to be used as building blocks of quantum computers: superconducting qubits, silicon spin qubits, trapped ions, neutral atoms and photonic qubits. Including insights from experts in all these technologies and taking into account algorithm compilation constraints, we discuss the advantages and inconveniences of each platform from an energy standpoint. Beyond providing concrete values of the energy consumption of current quantum computers, we lay the foundation of a framework to benchmark the energy efficiency of any future quantum computing architecture.

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

2 major / 2 minor

Summary. The paper defines the energy efficiency of a quantum computer as the ratio of the number of algorithms it can perform during a given time over the energy consumed by the hardware during this time. It evaluates this metric across five representative platforms (superconducting qubits, silicon spin qubits, trapped ions, neutral atoms, and photonic qubits), incorporating expert insights on power draw including cooling, control electronics, and compilation overheads, provides concrete numerical estimates, and discusses platform-specific advantages and disadvantages to establish a benchmarking framework for future architectures.

Significance. If the platform-specific energy estimates prove accurate and robust, the proposed framework offers a practical tool for comparing quantum hardware on energy efficiency beyond speed or qubit count alone, with the inclusion of compilation constraints adding realism. The work's strength lies in its attempt to quantify overheads across diverse technologies, but its value as a reproducible benchmark hinges on the transparency of the underlying numbers.

major comments (2)
  1. [Platform analysis and energy estimates] The concrete numerical values for energy consumption (including cryogenic cooling for superconducting qubits, laser power for trapped ions, etc.) are obtained via expert consultations rather than explicit derivations, published measurements, or first-principles formulas. This is load-bearing for the central claim because the efficiency ratios and resulting platform rankings depend directly on these figures; any systematic offset would invert the ordering without altering the formal definition.
  2. [Discussion of advantages and inconveniences] No sensitivity analysis or error propagation is provided for the expert-derived parameters (e.g., how variations in cooling overhead estimates affect the final ratios). This undermines the robustness of the cross-platform comparisons presented.
minor comments (2)
  1. [Definition section] Clarify the time interval T used in the efficiency definition and whether it is normalized across platforms or chosen per platform.
  2. [Results on concrete values] Add units and ranges explicitly when reporting the concrete energy values in the platform comparisons.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which have helped us improve the clarity and robustness of our work. We address each major comment below and outline the revisions we plan to implement.

read point-by-point responses

  1. Referee: [Platform analysis and energy estimates] The concrete numerical values for energy consumption (including cryogenic cooling for superconducting qubits, laser power for trapped ions, etc.) are obtained via expert consultations rather than explicit derivations, published measurements, or first-principles formulas. This is load-bearing for the central claim because the efficiency ratios and resulting platform rankings depend directly on these figures; any systematic offset would invert the ordering without altering the formal definition.

    Authors: We agree that the reliance on expert consultations for the numerical estimates is a limitation in terms of reproducibility. However, this approach was necessary because detailed, published measurements of full-system power consumption for these quantum platforms are not widely available in the literature. In the revised manuscript, we will add an appendix detailing the specific expert inputs and any supporting references or measurements used, along with a clearer discussion of the uncertainties involved. This will make the basis for our estimates more transparent. revision: partial

  2. Referee: [Discussion of advantages and inconveniences] No sensitivity analysis or error propagation is provided for the expert-derived parameters (e.g., how variations in cooling overhead estimates affect the final ratios). This undermines the robustness of the cross-platform comparisons presented.

    Authors: We acknowledge the absence of sensitivity analysis in the original submission. To address this, we will incorporate a dedicated sensitivity analysis section in the revised manuscript. This will include varying key parameters such as cooling power overheads and control electronics consumption within plausible ranges (e.g., ±20-50% based on expert feedback) and showing the impact on the efficiency metrics and platform rankings. We believe this will demonstrate that our main conclusions remain robust. revision: yes

Circularity Check

0 steps flagged

No circularity: energy-efficiency definition is independent and platform values are external expert inputs

full rationale

The paper defines energy efficiency directly as the ratio of executable algorithms per unit time to hardware energy consumed during that interval. This definition stands alone and does not reduce to any fitted parameter, self-citation chain, or prior result by construction. Energy-consumption figures for the five platforms are stated to come from expert consultations that incorporate cooling, control, and compilation overheads; these are external inputs rather than quantities derived from the efficiency metric itself. No equations, uniqueness theorems, or ansatzes are shown to be smuggled in via self-citation or to rename a known empirical pattern. The resulting platform comparisons therefore rest on independent (if subjective) data rather than tautological re-expression of the paper's own inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract, the central claim rests on domain assumptions about expert-provided energy data rather than explicit derivations; no free parameters or invented entities are identifiable at this level of detail.

free parameters (1)
  • Platform-specific energy consumption estimates
    Values derived from expert insights for each qubit technology, including overheads, function as assumed inputs to the efficiency ratio.
axioms (1)
  • domain assumption Expert insights provide accurate representations of energy use and compilation constraints for each physical platform.
    The analysis of advantages and inconveniences for superconducting, spin, ion, atom, and photonic systems relies on these inputs.

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Reference graph

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