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REVIEW 4 major objections 4 minor 2 cited by

Awesome Quantum Computing Experiments: Benchmarking Experimental Progress Towards Fault-Tolerant Quantum Computation

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This review benchmarks two decades of quantum hardware progress and projects that neutral-atom processors could reach 10,000 qubits and 0.1% entanglement error as early as 2025, if current exponential trends hold.

desk verdict Useful benchmarking review and open-source tracker, but the headline neutral-atom 2025 utility projection does not follow from the fits because it conjoins separate platform records. read the letter →

arxiv 2507.03678 v1 pith:ZLYJYRMT submitted 2025-07-04 quant-ph

François-Marie Le Régent This is my paper
classification quant-ph PACS 03.67.Pp03.67.Lx
keywords fault-tolerantquantumcomputationerrorcorrectionexperimentalbenchmarkingcoherencetimeentanglementqubitcountscalingneutralatomssuperconductingcircuits
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

This review attempts to establish a quantitative, continuously updatable record of experimental progress toward fault-tolerant quantum computation. Using curated databases of published experiments, it fits exponential trends to coherence times, entanglement error, and physical qubit counts across trapped ions, superconducting circuits, neutral atoms, NV centers, and semiconductors, reporting characteristic doubling and halving times. The paper also surveys quantum error correction experiments, tracking realized $[[n,k,d]]$ parameters from early repetition codes to distance-7 surface and color codes, including operation below threshold. Its headline projection is that, if current exponential rates persist, neutral-atom platforms could satisfy a working 'utility scale' of 10,000 physical qubits with sub-0.1% entanglement error as early as 2025.

What carries the argument

The carrying mechanism is the combination of four open datasets (physical_qubits, entangled_state_error_exp, qubit_count, and qec_exp) with exponential trend fitting: the model $y = A \cdot B^x$, linearized as $\log_{10}(y)$ versus year, with linear regression and $R^2$ as goodness-of-fit. A second load-bearing object is the $[[n,k,d]]$ parameterization of quantum error correction codes and the minimum-qubit table for early fault-tolerance demonstrations, which selects which experiments the survey tracks. The 'utility scale' working definition (10,000 physical qubits, entanglement error below 0.1%) converts the fitted doubling and halving rates into projected years.

What would settle it

Check the first post-2024 neutral-atom results: if by the end of 2026 no experiment demonstrates both at least 10,000 fully addressable physical qubits and a two-qubit or Bell-pair entanglement error below 0.1%, the paper's 2025 utility-scale projection for neutral atoms is falsified. More generally, fit all four metrics with the paper's method using data through 2026 and test whether the new points fall within the 95% prediction intervals of the Table 3 fits; systematic departure would falsify the exponential-continuation assumption.

Watch

Extended reading notes

Core claim

The paper assembles open-source databases of published experimental results and fits exponential curves to them, finding that physical-qubit coherence and gate fidelity have improved by orders of magnitude while processor scale has grown exponentially. The fitted rates are concrete: superconducting $T_1$ doubles every ~1.0 year, ion-trap $T_2$ every ~1.9 years, neutral-atom qubit count every ~1.4 years, and entanglement error halves every ~1.2–2.6 years depending on platform. On the logical level, it records progress from three-qubit repetition codes to surface and color codes with distance up to 7, including a 101-qubit distance-7 surface code operating below threshold with a logical error rate of 0.143% per cycle and a logical memory lifetime 2.4 times the best physical qubit. Extrapolating the fitted trends to a working definition of utility scale (10,000 physical qubits and entanglement error below 0.1%), the paper projects that neutral atoms reach it as early as 2025, with superconducting circuits and trapped ions following in 2039 and 2038 respectively; it notes in addition that recent entanglement-error results sit above their fits.

Load-bearing premise

The projections assume that the exponential improvement rates (doubling and halving times) fitted from 1998–2025 data continue unchanged for years or decades; if progress slows, as recent entanglement-error points already hint, every projected utility-scale year shifts or disappears.

Editorial extensions

If this is right

  • If current exponential rates persist, neutral-atom platforms reach 10,000 qubits and sub-0.1% entanglement error by 2025, decades ahead of superconducting circuits (2039) and trapped ions (2038).
  • Semiconductor spin qubits show the fastest error-rate reduction (halving every ~1.2 years) but the smallest qubit counts, so they remain far from utility scale on the chosen metrics.
  • The recent entanglement-error data sitting above their fits implies the error-halving rates may already be slowing, which would delay the projected years.
  • The distance-7 surface code result establishes operation below the fault-tolerance threshold on 101 physical qubits, confirming that logical error suppression now follows code-distance scaling in at least one platform.

Reading between the lines

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

  • If a 2025–2026 neutral-atom experiment fails to approach 10,000 controllable qubits with sub-0.1% entanglement error, that single datapoint would test the exponential-continuation assumption more sharply than any goodness-of-fit statistic; the fits themselves would remain descriptive but the utility-scale year would be invalid.
  • The same fitting machinery could be applied to logical-level metrics, such as the suppression factor $\Lambda$ across code distances, once enough experiments accumulate, turning the review's projection method into a predictor of fault-tolerance milestones.
  • Raw qubit count likely overstates usable scale for neutral atoms, since the count threshold excludes site-resolved readout and control requirements; an 'algorithmic qubit' version of the count metric might change the projected year.
  • The choice of 10,000 qubits and 0.1% error as utility scale is acknowledged as arbitrary; using a target derived from a specific algorithm, such as the Shor factoring requirements cited in the paper, would produce a harder and less optimistic timeline.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper compiles experimental data on physical-qubit metrics (T1/T2 coherence times, entanglement error, and qubit count) across trapped-ion, superconducting, neutral-atom, NV-center, and semiconductor platforms, fits exponential trends to characterize progress, and surveys experimental QEC code implementations with [[n,k,d]] parameters. It introduces an open-source repository for community-maintained tracking of these experiments. The paper also projects, in Appendix 7.1, when platforms might reach a chosen 'utility scale' of 10,000 physical qubits and 0.1% entanglement error, concluding that neutral-atom platforms could satisfy both criteria as early as 2025. The descriptive benchmarking and the repository are the main contributions; the projection is an extrapolation of fitted trends and carries several structural assumptions.

Significance. If the projection issue is resolved, this paper would be a genuinely useful, updatable quantitative resource for the quantum-computing community. Its strengths include the open-source GitHub repository with machine-readable datasets, reproducibility of the fits through the provided code, reporting of R² values and standard errors in Table 3, and explicit caveats about extrapolation in Appendix 7.1. The descriptive claim that coherence times, gate fidelities, and qubit counts have improved by orders of magnitude over two decades is well supported by the plotted data and the disclosed fit statistics. The significance is tempered by the unsupported 'both criteria by 2025' projection, which is not a mere presentation issue but affects a headline claim.

major comments (4)
  1. [Appendix 7.1, Table 2] The claim that 'neutral atom platforms could satisfy both criteria as early as 2025' is not supported by the table as constructed. The US-PQC and US-EE years are obtained by separately extrapolating the exponential fits for physical qubit count and entanglement error, and the joint year is taken as their maximum. This inference is valid only under the additional assumption that both thresholds are reached on the same platform lineage, ideally the same device. In the dataset, the 6,100-qubit neutral-atom record (Manetsch et al., 2024) and the best neutral-atom EE of 0.002 (2023) come from different experiments with different system sizes and control capabilities. Section 2.2.3 explicitly states that the qubit-count metric does not require full quantum-computing capability for every qubit, so even if both exponential trends continue individually, a single device satisfying both criteria does not follow. Please either remove the 'both criteria' claim, rephrase it as separate single-metric projections, or add a joint-capability model supported by evidence.
  2. [Section 2.2.2 and Appendix 7.1] The entanglement-error metric conflates Bell-state preparation error with two-qubit gate error. The text states that these are treated as equivalent 'as both provide a measure of the system's ability to create and manipulate entangled states,' but the two quantities are measured by different protocols and have different relationships to the QEC accuracy threshold. Since the utility-scale projections in Table 2 use the pooled entanglement-error metric, a mixed dataset can bias the fitted halving time and the projected threshold-crossing year. Please provide a sensitivity analysis fitting gate-error-only and Bell-state-error-only subsets, or restrict the projections to a single well-defined metric.
  3. [Appendix 7.1, Table 2 and Table 3] The projected utility-scale years are point estimates with no uncertainty, despite Table 3 reporting standard errors for the fitted rates. For small datasets, such as semiconductor T1 with three data points and neutral-atom T1 with R² = 0.122, the exponential extrapolation has very wide confidence intervals. Without propagating fit uncertainties or performing a sensitivity analysis that drops the most recent data points, the exact '2025' date conveys false precision. Please report uncertainty intervals or soften the timeline claims accordingly.
  4. [Section 2.2.3 and Figure 3] The qubit-count metric for neutral atoms includes large arrays, such as the 6,100-qubit tweezer array, that demonstrate coherence and readout but not necessarily full quantum-computing capability for every qubit. Section 2.2.3 acknowledges this for the neutral-atom platform, but the utility-scale projection in Table 2 uses this count as if it were directly comparable to the qubit-count metric of platforms where all qubits are used in a computational circuit. This comparability issue should be discussed when interpreting the 'US PQC 2024' entry and the resulting projection.
minor comments (4)
  1. [Section 2.2.1] Calling T1 the 'bitflip time' is imprecise: T1 measures energy relaxation/amplitude damping, not a bit-flip error rate. The subsequent clarification helps, but the phrase should be revised for accuracy.
  2. [Table 3 and Section 7.3.1] The text states the model y = A·B^x and the log-linear transformation, but it does not give the formula connecting the fitted slope to the reported '×2 every N years' values. Please add the conversion (e.g., doubling time = ln(2)/(m ln(10)) for base-10 logged data).
  3. [Figure 1 and Table 3] For fits with very low R², such as neutral-atom T1 (R² = 0.122) and superconducting-circuit T2 (R² = 0.418), the corresponding trend lines are still plotted as solid/dashed lines in Figure 1. Please visually distinguish poor fits or add a note in the caption directing readers to the R² values in Table 3.
  4. [Appendix 7.1, Table 2] The table shows the neutral-atom US-PQC year as 2024 and US-EE year as 2025, but the text summarizes these as 'both criteria as early as 2025.' Please state explicitly that the table gives separate single-metric projections and that the joint claim requires an additional assumption.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the utility-scale projections are explicitly labeled extrapolations of independent fits, not fitted inputs relabeled as predictions.

full rationale

The paper's central empirical content is a curated database of external experimental results, fit with exponential models y = A·B^x (Appendix 7.3.1). The utility-scale projections in Appendix 7.1 are computed by extrapolating these fitted doubling/halving times to externally chosen thresholds (PQC ≥ 10,000, EE < 0.1%). The projected years are not data points used in the fits, nor are the fitted parameters renamed as predictions; the threshold-crossing year is a derived quantity. The paper explicitly states that the targets are 'chosen somehow arbitrarily' and that the projection 'relies heavily on the assumption that current exponential scaling trends will continue,' which is an extrapolation risk, not circularity. The only self-citation, the open GitHub repository [26], is the data/code source for the analysis; it is code-reproduced, public, and backed by the primary experimental references, so it is not load-bearing in a circular sense. The 'both criteria on one device' concern raised in review is a composition/inference flaw in the projection, not a reduction of the conclusion to the inputs by construction. Under the stated rubric, no step equates the prediction with the fit input by definition or by fitted-parameter reuse.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central quantitative claims rest on fitted exponential rates and a hand-picked utility threshold; the main structural assumptions are the functional form, the equivalence of different error measures, the neutral-atom T1 and qubit-count conventions, and the continuation of trends for projections. No new particles, forces, or physical entities are introduced.

free parameters (2)
  • Utility-scale thresholds (physical qubit count, entanglement error) = 10,000 qubits; 0.1% error
    Chosen 'somehow arbitrarily' in Appendix 7.1 as a milestone; these target values, combined with the fitted growth rates, determine every projected year in Table 2.
  • Exponential fit coefficients (growth rates per platform and metric) = e.g., semiconductor T1 doubling every 0.86y; superconducting T1 0.99y; neutral atom qubit count 1.37y; see Table 3
    Fitted via log-linear regression in Section 7.3.1; these fitted rates define the reported trend characterization and are the inputs to the Table 2 projections.
assumptions (5)
  • domain assumption Exponential growth model y = A·B^x for all benchmark metrics
    Adopted in Section 7.3.1 without testing alternative functional forms; all trends and projections inherit this choice.
  • domain assumption Bell-state error and two-qubit gate error are treated as the same 'entanglement error' metric
    Stated in Section 2.2.2; this makes Figure 2 and the EE projections comparable across platforms that report different error types.
  • domain assumption The dominant qubit loss time for neutral atoms is categorized as T1
    Section 2.2.1: neutral atom T1 is the off-resonant scattering time, not spontaneous emission; the T1 trends are therefore not strictly the same quantity across platforms.
  • domain assumption Historical exponential trends continue into the future
    Appendix 7.1: 'the projection relies heavily on the assumption that current exponential scaling trends will continue for years or decades.' The paper flags this, but the utility-scale years depend on it.
  • standard math Log-linear regression is an adequate statistical description
    Section 7.3.1 uses scipy.stats.linregress on log10-transformed data and R2 as goodness of fit; this is standard practice.

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Cite this review

Pith. "Pith review of Awesome Quantum Computing Experiments: Benchmarking Experimental Progress Towards Fault-Tolerant Quantum Computation." pith.science (2026). https://pith.science/paper/ZLYJYRMT

@misc{pith2026250703678,
  author       = {Pith},
  title        = {Pith review of: Awesome Quantum Computing Experiments: Benchmarking Experimental Progress Towards Fault-Tolerant Quantum Computation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZLYJYRMT}},
  note         = {Machine review of arXiv:2507.03678}
}
abstract

Achieving fault-tolerant quantum computation (FTQC) demands simultaneous progress in physical qubit performance and quantum error correction (QEC). This work reviews and benchmarks experimental advancements towards FTQC across leading platforms, including trapped ions, superconducting circuits, neutral atoms, NV centers, and semiconductors. We analyze key physical metrics like coherence times, entanglement error, and system size (qubit count), fitting observed exponential trends to characterize multi-order-of-magnitude improvements over the past two decades. At the logical level, we survey the implementation landscape of QEC codes, tracking realized parameters $[[n, k, d]]$ and complexity from early demonstrations to recent surface and color code experiments. Synthesizing these physical and logical benchmarks reveals substantial progress enabled by underlying hardware improvements, while also outlining persistent challenges towards scalable FTQC. The experimental databases and analysis code underpinning this review are publicly available at https://github.com/francois-marie/awesome-quantum-computing-experiments.

Figures

Figures reproduced from arXiv: 2507.03678 by the authors.

Figure 1
Figure 1. Physical qubit bitflip and coherence times. Evolution of reported T1 (solid lines) and T2 (dashed lines) times across different platforms. An exponential fit showing the characteristic doubling time in years is included. The platforms, ordered by their doubling time (fastest first), are: Semiconductor T1 (0.9y but only three data points are displayed), Superconducting circuit T1 (1.0y), Ion traps T2 (1.9y), Supercon… view at source ↗
Figure 2
Figure 2. Entanglement error. Data points represent experimental achievements for ion traps (yellow circles), NV centers (red circle), neutral atoms (grey circles), semiconductor spins (light yellow circles), and superconducting circuits (green circles). Dotted lines show exponential fits to the data for platforms with sufficient data, indicating the approximate time required to halve the error rate: Semiconductor spins (1.2y… view at source ↗
Figure 3
Figure 3. Physical qubit count. Maximum number of physical qubits reported in experiments over time for selected platforms. The color code is the same as in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Cumulative experiment counts by platform. Total number of published QEC-related experiments up to a given year, categorized by platform. See Section 7.2.1 for a yearly (non cumulative) representation The field has witnessed substantial growth in experimental QEC activi…
Figure 5
Figure 5. Figure 5: QEC Code Parameters [[n, k, d]]. Scatter plot showing the number of physical qubits (n) versus the code distance (d) for experimentally implemented QEC codes. Points are labeled by the code type and logical qubit count (k). We see the horizontal line at d = 1 correspon…
Figure 6
Figure 6. Figure 6: Yearly experiment counts by platform. Number of published QEC-related experiments per year, categorized by platform. attention. Early activity is visible for central concepts demonstrated using Bell states and the [[5, 1, 3]] code. The plot shows significant recent gro…
Figure 7
Figure 7. Figure 7: Cumulative Growth of QEC Implementations. Total number of reported experimental implementa￾tions for different QEC code families over time. of QEC experiments to date, followed by trapped ions and, more recently, neutral atoms. It also highlights that certain codes, no…
Figure 8
Figure 8. Figure 8: Timeline of QEC Implementations. Each point represents a reported experimental implementation of a specific QEC code type (y-axis) on a particular quantum platform (color/shape) in a given publication year (x-axis). scipy.stats.linregress function from the SciPy librar…
Figure 9
Figure 9. Figure 9: Distribution of QEC Implementations Across Platforms. Sunburst chart showing the breakdown of reported QEC code implementations (inner ring) by the physical platform used (outer ring). Numbers indicate the count of publications in the qec_exp database [PITH_FULL_IMAGE…

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.