PaQit: Energy-Runtime-Fidelity Co-Optimization for Neutral Atom Quantum Computers

Jason Ludmir; Tirthak Patel

REVIEW 3 major objections 5 minor 26 references

PaQit: Energy-Runtime-Fidelity Co-Optimization for Neutral Atom Quantum Computers

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read On neutral-atom quantum computers, the energy- and runtime-optimal schedule is to pack identical circuit shots as densely as the target fidelity allows.

desk verdict A useful qualitative packing rule that collapses on an uncalibrated and likely mis-exponented fidelity model; the hardware data is the real contribution. read the letter →

arxiv 2608.02815 v1 pith:GCNZNDIL submitted 2026-08-03 quant-ph

Jason Ludmir , Tirthak Patel This is my paper · ORCID

classification quant-ph

keywords neutralatomquantumcomputingenergyefficiencyqubitpackingresourceschedulingfidelityoptimizationRydbergblockaderuntimehardwaresystems

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Neutral-atom quantum computers consume a large fixed baseline of power just to keep trap lasers and control electronics running, regardless of how many qubits are active. This paper seizes on that static-power-dominated regime: run many copies of the same circuit in the same atom array, packed as close as interaction physics permits, so the baseline power is paid once for many computations. The paper derives a quantitative relation between packing density and fidelity, showing that residual Rydberg interactions between packed copies degrade fidelity exponentially as packing efficiency cubed. It then turns a user-chosen fidelity target into a concrete packing density and, from there, into runtime and energy. If the model holds, schedulers can systematically trade a controlled amount of fidelity for large energy and time savings, or the reverse.

What carries the argument

The central object is the exponential fidelity model $f(\eta)=f_{\max}\exp[-\kappa(r_b/d_0)^6\eta^3]$. Packing efficiency $\eta$ is the fraction of array qubits actively used; the blockade radius $r_b$ is the distance inside which two Rydberg atoms suppress each other's excitation. The model converts the $1/r^6$ van der Waals interaction between packed copies into a single analytic curve, and inverting it yields the maximum packing efficiency for a target fidelity. That number feeds the runtime formula $T_{\mathrm{eff}}=sn/(\eta N)\,t$ and the energy formula $E_{\mathrm{eff}}=T_{\mathrm{eff}}\,(P_0+\alpha(\eta N)^\beta)$, reducing the entire scheduling problem to a one-dimensional choice of $\eta$.

What would settle it

Measure the state-preparation fidelity of a fixed atom grid on real hardware across a sweep of nearest-neighbor spacings $d$ and plot $-\ln(f/f_{\max})$ against $d^{-6}$; the exponential model predicts a straight line through the origin, so a clearly nonlinear or intercept-shifted plot would falsify the packing optimization.

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Extended reading notes

Core claim

The paper's central claim is that on neutral-atom computers, the energy- and runtime-optimal way to run a circuit is to execute as many identical shots as possible in parallel in the same atom array, and the only limit is the fidelity one is willing to accept. The quantitative core is Eq. (12): with packing efficiency $\eta$, fidelity is $f(\eta)=f_{\max}\exp[-\kappa(r_b/d_0)^6\eta^3]$, where $r_b$ is the Rydberg blockade radius and $d_0$ is the full-occupancy lattice spacing. Because the interaction between neighboring circuit instances falls as the sixth power of their separation, dense packing eventually costs accuracy, but the static-power-dominated energy budget makes extra parallelism otherwise nearly free. Inverting the fidelity formula gives the largest $\eta$ allowed by a fidelity target, which directly determines effective runtime and energy. The paper validates the qualitative relationship on simulated and real hardware, where fidelity degrades as spacing shrinks and levels off once spacing exceeds roughly ten micrometers.

Load-bearing premise

The load-bearing premise is that fidelity falls exponentially as $\kappa(r_b/d)^6$, with the proportionality constant $\kappa$ set to 1; the hardware data confirm only the direction of the effect, not this specific functional form.

Editorial extensions

If this is right

In the static-power-dominated regime, dense packing of identical shots reduces both wall-clock time and total energy, so the energy-optimal schedule and the runtime-optimal schedule coincide.
Packing efficiency falls only as $(-\ln f^*)^{1/3}$, but runtime and energy rise steeply as fidelity targets tighten, making low-fidelity requirements cheap and very strict requirements expensive.
On weakly interacting hardware, small $r_b/d_0$, near-maximal packing costs almost nothing; on strongly interacting hardware, the same fidelity target forces a much sparser layout and much higher energy.
Spacing, not raw atom count, is the dominant scheduling variable: once atoms are far enough apart to avoid crosstalk, adding more atoms to the array causes little additional fidelity loss.
The scheduler operates purely at the software and control-layout level, requiring no hardware modifications and recomputing packing density when fidelity targets or calibration parameters change.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper: the same packing rule should extend to partitioning the array into zones that run different circuits, each with its own spacing threshold, although the paper only analyzes batched identical shots.
Beyond the paper: if near-term hardware has significant per-shot loading, measurement, or reset overhead, the energy benefit of dense packing shrinks; adding a serial overhead term to $E_{\mathrm{eff}}$ would make the model more predictive.
Beyond the paper: the observed spacing threshold of about ten micrometers offers a direct calibration path—estimating $\kappa$ from a log-linear fit of fidelity versus $d^{-6}$ would upgrade the model from qualitative to quantitative, since the paper sets $\kappa=1$ without fitting.
Beyond the paper: the fidelity-versus-spacing curve could be measured routinely on larger arrays during device calibration, turning packing efficiency into a continuously tunable knob for energy-aware scheduling in production workloads.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear X,

The one-sentence take: the qualitative rule PaQit proposes—pack concurrent circuit copies as densely as the fidelity budget allows, because static power dominates—is sensible and useful, but the quantitative model behind it is asserted, and the fidelity exponent in Eq. (12) is likely wrong by a factor of two. The operating points in Figs. 3-5 are therefore not supported as they stand.

Credit where it's due. The paper cleanly derives serial and parallel runtime/energy bounds (Sec. IV), and the insight that the packing-fidelity trade-off can be turned into a scheduler knob is genuinely useful. The real Aquila data in Fig. 7 is the strongest part: it shows that spacing, not raw atom count, drives the fidelity loss, and that sufficiently spaced atoms can be added almost for free. That is a concrete, reproducible observation. The simulator is simple and the paper is clearly written.

The soft spots are serious. Eq. (12) is load-bearing and it's just asserted: 'the cumulative perturbation scales with (rb/d)^6' and then an exponential appears. For the detuned π/2 pulse they actually run, the per-pair error is second order in the detuning, so the exponent should be 12, not 6, and the packing dependence should be η^6, not η^3. I checked this against the stress-test note and the argument holds up. κ=1 and fmax=1 are hand-set, so the curves aren't calibrated. The power model leans on non-peer-reviewed blog posts. The paper claims a non-monotonic energy profile, but with its own β=1 linear power model and P0 dominating, E_eff is monotonically increasing with fidelity—there's no interior optimum. And the code availability statement contradicts itself: the abstract says the code is available on GitHub, while Sec. VI.D says it will be released upon publication.

These are all addressable. A revision should derive or fit the fidelity law, calibrate κ on the Aquila data, correct the monotonicity claim, and clean up the code statement. If the exponent does change, the quantitative conclusions shift substantially, but the qualitative framework and the hardware validation survive.

This paper is for systems people working on neutral-atom scheduling and energy efficiency. They'd get value from the qualitative analysis and the experimental trends. I wouldn't cite the current quantitative model in my own work, but I'd track a revised version.

I'd send it to a serious referee—an editor shouldn't desk-reject it—but it needs major revision. I'd recommend major revision, with an eye toward accepting after the fidelity model is recalibrated.

Referee Report

3 major / 5 minor

Summary. The paper proposes PaQit, a scheduling framework for neutral-atom quantum computers that selects qubit packing density to trade fidelity against runtime and energy. The authors introduce a power model P(n)=P0+αn^β, derive serial and parallel runtime/energy bounds, define a packing efficiency η, and posit an exponential fidelity law f(η)=fmax exp[-κ(rb/d0)^6 η^3]. They invert this law to compute the packing efficiency that meets a target fidelity and then evaluate PaQit via analytical curves, Bloqade simulations, real QuEra Aquila hardware executions, and Gemini noise-model simulations. The central claim is that maximizing concurrent circuit instances, subject to a fidelity constraint set by spatial packing, minimizes both runtime and energy in static-power-dominated neutral-atom systems.

Significance. If the fidelity model were quantitatively correct, PaQit would provide a practical and useful scheduling policy for Rydberg platforms. The time and energy algebra in Sec. IV is straightforward and sound, and the observation that static power dominates, so parallelism amortizes the dominant energy cost, is a valuable systems-level insight. The qualitative direction of the packing-fidelity trade-off is supported by both emulation and real hardware data. However, the central quantitative fidelity law, Eq. (12), is asserted without a derivation and is not fitted to data; moreover, the physical scaling exponent is likely wrong. Consequently, the numerical operating points, runtime/energy curves, and the claimed quantitative predictive power are not established. The work is a reasonable framework proposal but requires a corrected, validated fidelity model before its quantitative claims can be accepted.

major comments (3)

[Sec. V.B, Eq. (12)] Equation (12) is asserted without derivation from the Rydberg Hamiltonian, and the stated justification is physically incomplete. For an atom driven near resonance, the leading correction to the π/2-pulse excitation probability from a small detuning δ is second order in δ/Ω, not first order. Since δ/Ω=(rb/d)^6, the per-pair infidelity should scale as (rb/d)^12, giving log f ∝ -(rb/d0)^12 η^6 rather than -(rb/d0)^6 η^3. Because Eq. (13) inverts Eq. (12) to produce PaQit's packing efficiencies, and Eqs. (14)-(15) and Figs. 3-5 depend on that inversion, the quantitative schedule is currently unsupported. The authors need either a careful derivation from the Rydberg dynamics or a quantitative fit to experimental fidelity data that can discriminate the exponent.
[Sec. VI.B and VI.C] The constants κ=1.0 and fmax=1.0 are set by hand, and no quantitative comparison is made between Eq. (12) and the measured fidelity curves in Figs. 6 and 7. The real-hardware single-atom fidelity proxy is around 0.9, not 1.0, so fmax=1.0 is not hardware-grounded; the lack of a fit or uncertainty analysis for κ and fmax means the validation supports only the qualitative direction of the trade-off, not the claimed quantitative agreement. Since PaQit outputs numerical operating points, the paper needs a calibrated fidelity model with confidence intervals or at least a sensitivity analysis.
[Sec. VII.A, Figs. 3-5] Figures 3-5 are direct plots of the assumed fidelity model with κ=1 and fmax=1, so they are consequences of the postulate rather than independent validation. The text in Sec. VII.A presents these trends as if they were confirmed by the experiments, but the experiments confirm only monotonic degradation with denser packing. A quantitative overlay of the model on the Figs. 6 and 7 data, or a statement that the analytical curves are illustrative only, is necessary to avoid circular support for the model.

minor comments (5)

[Sec. VI.D] The introduction states that PaQit is available at a GitHub URL, while Sec. VI.D says the source code will be released upon publication; please make the availability statement consistent.
[Sec. VI.D] There is a typo: "on real neutral-atom hardware,, we" should read "on real neutral-atom hardware, we".
[Sec. V.C, Eq. (13)] Equation (13) requires ln(f*/fmax) < 0, which excludes f* = 1 when fmax = 1; the domain of validity of the inversion should be stated explicitly.
[Sec. VI.A] The power-model parameters α and β are taken from non-peer-reviewed web sources; please either cite a peer-reviewed source or state clearly that α and β are illustrative estimates with unknown uncertainty.
[Sec. VI.B] The fidelity proxy F = 1 - (1/p_ideal)(1/N)Σ|ρ_i - p_ideal| can exceed 1 or become negative for large deviations; please state its range and explain why it is an appropriate proxy for the qualitative packing trend.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PaQit's trade-off curves are explicit consequences of its stated fidelity model, and the hardware experiments provide independent qualitative validation.

full rationale

Walking the derivation chain: Section IV's runtime and energy bounds are algebraic consequences of the stated execution definitions and power model P(n)=P0+αn^β; no bound is defined in terms of a conclusion it is supposed to establish. Section V introduces f(η)=fmax exp[-κ(rb/d0)^6 η^3] as an explicit modeling assumption, not as a derived result, and Eq. (13) is its algebraic inversion; Eqs. (14)-(15) merely substitute that inverse into the Section IV bounds. Thus Figs. 3-5 are model outputs rather than independent empirical discoveries, but they are not circular: the paper does not define the fidelity model in terms of the runtime/energy curves and does not use those curves to infer the model. The hardware and Bloqade experiments (Figs. 6-8) are external checks that qualitatively confirm the assumed monotonic spacing-fidelity trend. The normalization κ=1.0 and fmax=1.0 in Sec. VI.B is an acknowledged simplification ('While simplified, these normalized parameters allow us to isolate and study the structural dependence of system behavior on packing and interaction strength independent of hardware-specific calibration'), which weakens quantitative validation but does not create a definitional circle. Self-citations [19],[20],[24],[25] support background and prior modeling tools, not the load-bearing derivation. No step reduces by construction to its inputs, so the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central model rests on four hand-set or fitted parameters (κ, fmax, α, β) plus the assumed exponential fidelity form; no new physical entities are introduced. The power constants come from non-peer-reviewed reports, and the fidelity constant is a placeholder.

free parameters (4)

κ = 1.0
Proportionality factor in the exponential fidelity model (Eq. 12), set to 1.0 by hand in Sec VI.B, not derived or fitted to data; controls the strength of fidelity degradation with packing.
fmax = 1.0
Maximum fidelity at zero packing, set to 1.0 by hand in Sec VI.B.
α = 0.000308 kW/qubit
Per-qubit dynamic power coefficient in Eq. 3, adopted from reported Aquila power figures [5],[6], not measured in this work.
β = 1
Scaling exponent in Eq. 3, assumed linear ('β≈1') from reported near-linear scaling; not directly measured in this paper.

assumptions (5)

domain assumption The total device power is P(n) = P0 + α n^β (Eq. 3), with static power strongly dominant (P0 >> α n^β) in current neutral-atom systems.
Introduced in Sec III; the static-dominated regime is the basis of the energy-runtime trade-off. It rests on reported figures [5],[6] rather than measurement in this paper.
standard math The pairwise interaction energy decays as C6/r^6 with blockade radius rb = (C6/sqrt(Omega^2+Delta^2))^(1/6) (Eqs. 9-10).
Standard Rydberg physics cited from [14],[19],[20]; used to motivate the fidelity model.
ad hoc to paper The fidelity of a shot obeys f(η) = fmax exp[-κ (rb/d0)^6 η^3] (Eq. 12), i.e., exponential degradation with the cube of packing efficiency.
Introduced without derivation in Sec V.B; κ is set to 1.0. This is the crucial assumption that makes the PaQit curves take their shape.
standard math Average inter-shot spacing d = d0 η^{-1/2} for a 2D square lattice (Eq. 11).
Geometric relation for a square-packed array; harmless.
domain assumption Multiple identical circuit shots can be executed concurrently in a single physical array under a shared Hamiltonian (Sec II, 'Parallel Execution Opportunity').
Underlies the parallel runtime formulas; this is a claimed hardware capability of neutral-atom systems.

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Cite this review

Pith. "Pith review of PaQit: Energy-Runtime-Fidelity Co-Optimization for Neutral Atom Quantum Computers." pith.science (2026). https://pith.science/paper/GCNZNDIL

@misc{pith2026260802815,
  author       = {Pith},
  title        = {Pith review of: PaQit: Energy-Runtime-Fidelity Co-Optimization for Neutral Atom Quantum Computers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GCNZNDIL}},
  note         = {Machine review of arXiv:2608.02815}
}

read the original abstract

Neutral-atom quantum computers provide a scalable platform for large-scale quantum computation due to their all-optical control, room-temperature operation, and flexible lattice geometry. Although the favorable energy characteristics of these systems are well recognized, the relationship between system-level energy consumption, runtime, and computational fidelity remains poorly understood, limiting practical scheduling decisions. In this work, we develop a hardware-grounded analytical model that captures how energy and runtime scale with qubit utilization in neutral-atom systems. We introduce PaQit, a fidelity-aware qubit packing framework that integrates device-level Rydberg interaction physics with system-level scheduling to jointly optimize energy, runtime, and fidelity. By translating fidelity targets into packing decisions, PaQit identifies operating regimes that maximize parallelism while respecting interaction-driven crosstalk constraints. We validate the analytical framework using simulations of QuEra's analog Aquila system and digital Gemini system, as well as real hardware executions, demonstrating close agreement between the predicted trends and observed system behavior.

Figures

Figures reproduced from arXiv: 2608.02815 by the authors.

**Figure 2.** PaQit determines the packing efficiency of the qubits on the neutral [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** The packing efficiency decreases with increasing fidelity, with strong [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 7.** Figure 7: Real Aquila hardware executions showing state-preparation fidelity [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

**Figure 6.** Figure 6: Simulation on an Aquila array showing state-preparation fidelity versus [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 8.** Figure 8: Gemini one-zone simulation of state-preparation fidelity versus qubit [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

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