Simulation and Benchmarking of Real Quantum Hardware

A. Auer; D. Smith; M. Bauer; M. Papi\v{c}; M. Sch\"ondorf; S. Pogorzalek; T. Ayral; T. Piskor

REVIEW 3 major objections 4 minor 2 cited by

Simulation and Benchmarking of Real Quantum Hardware

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims that a noise model built solely from vendor calibration data—randomized-benchmarking gate fidelities, T1 and T2 times, gate durations, and readout errors—predicts the full output distributions of real superconducting quantu

desk verdict Useful calibration-only noise model with a plausible placement rule, but the '50% improvement' headline is not yet supported by a controlled comparison, and the dephasing channel likely double-counts T1. read the letter →

arxiv 2508.04483 v1 pith:LRXC44QW submitted 2025-08-06 quant-ph

T. Piskor , M. Schöndorf , M. Bauer , D. Smith , T. Ayral , S. Pogorzalek , A. Auer , M. Papič This is my paper

classification quant-ph

keywords noisemodelquantumsimulationdepolarizingchannelrandomizedbenchmarkingHellingerdistanceNISQsuperconductingqubitsbenchmarkcircuits

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

The paper claims that a quantum computer's noise can be predicted accurately using only the calibration numbers vendors already supply: randomized-benchmarking gate fidelities, coherence times T1 and T2, gate durations, and readout error rates. The proposed model wraps each gate in a depolarizing channel whose strength is set by the gate's average fidelity, applies relaxation and dephasing only while qubits are idle, and replaces ideal measurements with imperfect POVMs. On a 20-qubit superconducting chip, simulated output distributions land within Hellinger distance 0.040 to 0.115 of measured distributions for GHZ, random-unitary, and QAOA circuits. On deep quantum-walk circuits on a 15-qubit device, the model reduces Hellinger distance by about half relative to two published composite noise models. If correct, this means algorithm performance on NISQ hardware can be assessed before execution using data that is routinely available.

What carries the argument

The carrying mechanism is the placement of a depolarizing channel on every gate, with error probability equal to the gate's randomized-benchmarking average infidelity, while amplitude-damping (T1) and pure-dephasing (T2) channels act only during idle periods and readout is modeled as an imperfect POVM with the calibrated error rates. The depolarizing channel is what converts a single scalar calibration number into a full quantum channel; the idle-only placement of relaxation and dephasing is the model's distinctive structural choice.

What would settle it

Perform gate set tomography on the native two-qubit gate of the same 20-qubit chip. If the reconstructed error channel contains a coherent component comparable in size to the depolarizing probability, then a GHZ-3 circuit simulated with this model should fail to reproduce the measured target-state asymmetry; the model's predicted Hellinger distance would be significantly smaller than the experimental one, showing the depolarizing assumption is the bottleneck.

Watch

Extended reading notes

Core claim

The central claim is that the dominant errors of a gate-based superconducting quantum computer are captured by three channels parameterized entirely by vendor calibration data: a depolarizing channel applied during each gate, amplitude-damping and pure-dephasing channels applied during idle periods, and a classical measurement-error POVM at readout. The depolarizing probability is fixed so the channel's average error rate equals the gate fidelity reported by randomized benchmarking. The model's distinctive choice is to place relaxation and dephasing only on idle qubits rather than after every gate; the authors argue this is physically more accurate and attribute their improvement on deep cir

Load-bearing premise

The model assumes each gate's only error is a random, unbiased scrambling of the qubit state, with the amount of scrambling fixed by the gate's calibration fidelity; so any systematic error (like a slight over-rotation), leakage out of the computational subspace, or drift between calibration and execution is invisible to the model.

Editorial extensions

If this is right

Users can estimate whether a given quantum algorithm will produce usable results on a specific device before spending hardware time, using only the device's calibration sheet.
The model stays accurate up to circuit depths around 1000, so it can separate failures caused by algorithm design from failures caused by noise.
Because all parameters are standard calibration outputs, the same model should transfer to trapped-ion or neutral-atom hardware by swapping in the corresponding fidelities, coherence times, and readout errors.
Applying relaxation and dephasing only during idle time, rather than after every gate, is key to the observed ~50% improvement over prior composite models on deep circuits.

Reading between the lines

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

Beyond the paper: the residual asymmetries the authors report (GHZ-3 target-state imbalance, QAOA bias toward low excitations) are the fingerprints of coherent overrotation and leakage, so adding a single overrotation parameter to the native two-qubit gate would likely close most of the remaining gap.
Beyond the paper: with randomized compiling applied to the circuit, the depolarizing approximation becomes exact, which means this model could serve as a null hypothesis for detecting non-Markovian or time-varying noise.
Beyond the paper: a cheap drift detector follows—re-running the same benchmark circuits periodically should keep Hellinger distances stable; a sudden increase would indicate the calibration data no longer describes the device.

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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

Quick read for you. The genuinely valuable part is the IQM benchmark: a noise model built only from vendor calibration data (T1, T2, RB fidelities, readout errors) predicts output distributions with Hellinger distances of 0.04 to 0.115 across GHZ, random-unitary, and QAOA circuits. That is a real out-of-sample result, since nothing is fitted to the benchmark circuits. The channel-placement rule — depolarizing only during gate execution, relaxation/dephasing only during idle — is the main novelty over Georgopoulos et al. [28], and it is physically reasonable. The authors also put the circuits on Zenodo and are explicit about where their model fails (GHZ-3 asymmetry, QAOA bias), which I respect.

The soft spots are real but concentrated. The headline claim of 'improvement in overall prediction accuracy' rests on Fig. 6, where the UNM and Qiskit composite baseline distances are imported from [28], not recomputed for the authors' own compiled circuits, shot counts, or per-qubit parameters. If those differ, the ~50% gap can shrink or vanish. That comparison needs to be re-run or demoted. Second, there is a formula bug in Sec. III B: the text correctly states 1/T2 = 1/(2T1) + 1/T_phi and notes the T1 part is already captured in relaxation, but then writes the pure dephasing probability as 1 - exp(-t/T2). That uses total T2 and double-counts relaxation decay. It should be T_phi. This can materially bias idle-time noise. Third, no error bars on any Hellinger distance; Table III gives averages over 50 repetitions but no spread. Minor, but needed. Fourth, GHZ-8+ are excluded post-hoc, and the QAOA circuit is near-random, so the benchmark set is thinner than it appears. A noiseless simulation baseline would also help show how much of the agreement is trivial.

Bottom line: the IQM result is solid and worth having, but the comparative claim is not yet supported. This deserves a serious referee, and the fixes are addressable: re-run the baselines, fix the dephasing formula, add error bars. I'd take it to reading group — it raises the right questions about what counts as a benchmark in this area.

Referee Report

3 major / 4 minor

Summary. The paper proposes a noise model for NISQ hardware constructed exclusively from vendor calibration data: a depolarizing channel applied after each gate with strength derived from single- and two-qubit randomized-benchmarking fidelities, T1/T2 relaxation and dephasing applied during qubit idle times, and a measurement-error POVM at the end. The model is benchmarked on an IQM 20-qubit chip for GHZ circuits of 2–7 qubits, a random-unitary circuit, and a QAOA circuit, with reported Hellinger distances between 0.040 and 0.115 (Table III). For IBM Q Melbourne quantum-walk circuits QW-2 to QW-6, the authors compare against the unified noise model (UNM) and Qiskit composite model and report approximately 50% improvement for QW-4, QW-5, and QW-6 (Fig. 6). The central claim is that a noise model using only measured hardware calibrations can predict full output distributions of real hardware and does so more accurately than two published baselines on deeper circuits.

Significance. If the results hold, the paper provides a practically valuable and inexpensive 'digital twin' of real hardware: the model has no free parameters fitted to the benchmark outputs, uses per-qubit and per-pair calibration data for the IQM device, and the IQM circuits are made available on Zenodo. The out-of-sample prediction of GHZ, random-unitary, and QAOA distributions with Hellinger distances below 0.12 is a genuine and useful demonstration. These strengths are, however, partially offset by two weaknesses: (i) the headline comparison against UNM/QiskitCM depends on Hellinger distances imported from Ref. [28] rather than recomputed in a controlled setting, and (ii) no uncertainty estimates are given for any of the reported distances. The model's depolarizing-gate approximation also limits its accuracy for structured circuits, as the paper itself acknowledges in Sec. IV.C.1.

major comments (3)

[Sec. IV.C.2, Fig. 6] The claimed ~50% improvement over UNM and QiskitCM is not independently established. The figure caption states that the Hellinger distances for UNM and QiskitCM 'were taken from [28]'. A controlled comparison requires the baselines to be recomputed on the same compiled circuits, the same measurement data, and with the same number of shots as the authors' own simulations. Neither the shot counts nor the compiled circuit layouts used for the imported numbers are verified here; the QW circuits are not among the QASM files linked in App. B. If the baselines were evaluated with different shot counts or different SWAP insertions/gate orderings, the reported gap could be an artifact. Please either rerun UNM and QiskitCM under identical conditions or explicitly weaken the comparison claim to a statement about absolute Hellinger distances on the IQM device.
[Table III, Fig. 6] No error bars or confidence intervals are provided for any Hellinger distance. Table III reports values averaged over 50 repeated measurements, but the caption does not state whether the average is over 50 distances computed from individual 10,000-shot histograms or over pooled counts, and no standard deviation is given. In Fig. 6 the comparison is even less protected: the absence of uncertainties makes it impossible to assess whether, e.g., the difference between the authors' QW-4 value and the UNM value is significant. Please provide bootstrap or analytical finite-sampling uncertainties for every reported Hellinger distance, including the imported baselines.
[Sec. III.C, Eq. (11)-(14)] The depolarizing channel is the model's central error description, but the paper does not write the explicit relation between the measured randomized-benchmarking fidelity and the depolarizing probability p_dep. The mapping matters because the reported single-qubit fidelities are ~99.8% and two-qubit fidelities ~98.6%, and different conventions for 'average fidelity' versus 'process fidelity' change the inferred depolarizing strength. Please give the formula used. In addition, Sec. IV.C.1 already identifies coherent errors, leakage, and crosstalk as the likely causes of the GHZ-3 asymmetry and the QAOA low-excitation bias; a fuller discussion of how these unmodeled errors bound the achievable accuracy for structured circuits would make the scope of the model clearer.

minor comments (4)

[Sec. IV.A, Eq. (16)] The displayed formula for the Hellinger distance appears to omit the outer square root. Please check that the typeset expression matches the standard definition H(p,q) = (1/sqrt(2)) * sqrt( sum_i (sqrt(p_i)-sqrt(q_i))^2 ).
[Table III caption] Please clarify how the average over 50 measurements is computed: is the Hellinger distance calculated for each of the 50 experimental histograms and then averaged, or are the 50 histograms pooled into one distribution before computing a single distance? The two procedures give different values and different interpretations.
[Fig. 8 caption] The caption states both that the simulation and measurement results were obtained with 100,000 shots and that the measurement data were taken from [28]. Please reconcile these statements, and clearly identify which data are new to this work and which are imported.
[App. B] The linked Zenodo record is said to contain only the GHZ, RU, and QAOA circuits. For reproducibility of the IBM Q Melbourne comparison, please also release the quantum-walk circuits in the exact compiled form used for the simulations.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the model's parameters are measured hardware calibrations and the benchmark circuits are independent of those inputs.

full rationale

The derivation chain is self-contained and non-circular. All noise model inputs are vendor-measured calibration quantities: per-qubit and per-pair randomized-benchmarking gate fidelities, T1/T2 times, readout error probabilities, and gate durations (Sec. II, Table I, App. A; Sec. III). These are not fitted to the benchmark outputs. The benchmark circuits (GHZ, random unitary, QAOA on the IQM chip; quantum walks on an emulated IBM Q Melbourne) are structurally distinct from the calibration circuits, and the reported Hellinger distances compare an independently measured experimental histogram with a deterministic simulation of the model without any parameter adjustment to reduce those distances (Sec. IV C). The comparison with the unified noise model and Qiskit composite model imports baseline Hellinger distances from Ref. [28]; this is an external-benchmark dependency and a reproducibility concern about matching shot counts and compiled circuits, but it is not a circular reduction of the paper's central derivation to its own inputs. The self-citations to IQM characterization papers ([24], [27], [31], [38]) provide hardware parameters and device descriptions, not the paper's claimed predictive result, and no uniqueness theorem or ansatz is imported from the authors' prior work to force the model choice. The depolarizing-channel assumption is an explicit modeling simplification, with residual discrepancies openly attributed to coherent errors, leakage, and crosstalk (Sec. IV C 1), which is an acknowledged limitation rather than a circular step.

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

The model's predictive content comes from measured calibration parameters that are not fitted to the benchmark data, so no parameter is introduced ad hoc to match the target histograms. The genuinely free content is the scheduling rule, the depolarizing approximation, and the omission choices (state preparation errors, coherent errors, leakage, crosstalk). No new physical entities are postulated.

free parameters (4)

Depolarizing strength per gate (p_dep) = p_dep from RB fidelity (Tables V-VI; Table IV)
All gate error is mapped to a depolarizing channel whose strength is set by the randomized-benchmarking average fidelity. The conversion is a modeling choice that discards coherent error structure.
Channel scheduling rule = n/a (structural)
The placement of noise (depolarizing on gates only; T1/T2 on idle only) is the paper's main design choice, empirically validated, not derived.
IBM Q Melbourne noise parameters = F_1qb=99.99%, F_2qb=96.83%, times 100/500 ns, T1=56.15 us, T2=56.01 us, readout 7.61% (Table IV)
Imported from [28] and [53]. The IBM comparison inherits another group's parameterization of a device the authors did not measure.
Per-qubit/per-pair IQM calibration parameters = Tables V-VI (T1 7.0-64.9 us, T2 1.8-5.5 us, readout errors, fidelities)
Measured inputs from vendor calibration, not fitted to the benchmark outputs; they fully determine the simulation.

assumptions (5)

standard math Standard CPTP/Kraus channel formalism describes hardware noise
Open quantum systems background invoked in Sec. III, refs [23], [37], [39].
domain assumption RB fidelity adequately characterizes gate error and is stationary between calibration and execution
The depolarizing channel is fixed to RB fidelity (Sec. III C). This assumes no significant coherent error, leakage, crosstalk, or drift; the paper itself lists these as the likely causes of residual discrepancies (Sec. IV C 1).
domain assumption Random-circuit averaging justifies the depolarizing approximation
Invoked in Sec. III C with ref [48] (Wallman-Emerson). The justification applies to random circuits, but the model also uses depolarizing noise on structured circuits (GHZ, QAOA, quantum walks).
ad hoc to paper State-preparation errors are negligible
Sec. III D states SP errors were omitted because they 'did not show any major differences in our results when included', with the supporting data not shown.
domain assumption Independent, static per-qubit readout errors
Readout is modeled as factorized single-qubit POVMs with calibration values (Sec. III D); correlated readout errors are not considered.

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Cite this review

Pith. "Pith review of Simulation and Benchmarking of Real Quantum Hardware." pith.science (2026). https://pith.science/paper/LRXC44QW

@misc{pith2026250804483,
  author       = {Pith},
  title        = {Pith review of: Simulation and Benchmarking of Real Quantum Hardware},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LRXC44QW}},
  note         = {Machine review of arXiv:2508.04483}
}

read the original abstract

The effects of noise are one of the most important factors to consider when it comes to quantum computing in the noisy intermediate-scale quantum computing (NISQ) era that we are currently in. Therefore, it is important not only to gain more knowledge about the noise sources appearing in current quantum computing hardware in order to suppress and mitigate their contributions, but also to evaluate whether a given quantum algorithm can achieve reasonable results on a given hardware. To accomplish this, we need noise models that can describe the real hardware with sufficient accuracy. Here, we present a noise model that has been evaluated on superconducting hardware platforms and could be adapted to other common architectures such as trapped-ion or neutral atom devices. We then benchmark our model by simulating a 20-qubit superconducting quantum computer, and compare the accuracy of our model to similar approaches from the literature and demonstrate an improvement in the overall prediction accuracy.

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Forward citations

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Reference graph

Works this paper leans on

55 extracted references · 29 canonical work pages · cited by 2 Pith papers

[28]

Y. Kim, A. Eddins, S. Anand, K. X. Wei, E. Van Den Berg, S. Rosenblatt, H. Nayfeh, Y. Wu, M. Zale- tel, K. Temme, et al. , Evidence for the utility of quan- tum computing before fault tolerance, Nature 618, 500 (2023)

2023
[1]

GHZ, random unitary and QAOA circuits In this section, we will compare the simulation re- sults of our noise model with the ones obtained from the real IQM quantum computing hardware as presented in Sec. II. For the purpose of our comparison, we will be us- ing the circuits described in Sec. IV B and the Hellinger distance as a quantitative measure of agr...

work page
[2]

Comparison to other noise models To gain more insight into whether our proposed noise model performs well, we also made a comparison with an- C Results 8 �� FIG. 6. Hellinger distances for the quantum walk circuits from this work and [28] that have been measured...

work page arXiv
[3]

Zhou, S.-T

L. Zhou, S.-T. Wang, S. Choi, H. Pichler, and M. D. Lukin, Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near- term devices, Phys. Rev. X 10, 021067 (2020)

2020
[4]

Farhi, J

E. Farhi, J. Goldstone, and S. Gutmann, A quan- tum approximate optimization algorithm (2014), arXiv:1411.4028 [quant-ph]

arXiv 2014
[5]

Peruzzo, J

A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’brien, A variational eigenvalue solver on a photonic quantum processor, Nature Communications 5, 4213 (2014)

2014
[6]

Abbas, A

A. Abbas, A. Ambainis, B. Augustino, A. B¨ artschi, H. Buhrman, C. Coffrin, G. Cortiana, V. Dunjko, D. J. Egger, B. G. Elmegreen, N. Franco, F. Fratini, B. Fuller, J. Gacon, C. Gonciulea, S. Gribling, S. Gupta, S. Had- field, R. Heese, G. Kircher, T. Kleinert, T. Koch, G. Ko- rpas, S. Lenk, J. Marecek, V. Markov, G. Mazzola, S. Mensa, N. Mohseni, G. Nanni...

arXiv 2023
[7]

I. M. Georgescu, S. Ashhab, and F. Nori, Quantum sim- ulation, Reviews of Modern Physics 86, 153 (2014)

2014

Show all 55 references

[8]

A. J. Daley, I. Bloch, C. Kokail, S. Flannigan, N. Pearson, M. Troyer, and P. Zoller, Practical quantum advantage in quantum simulation, Nature 607, 667 (2022)

2022
[9]

Fauseweh, Quantum many-body simulations on digi- tal quantum computers: State-of-the-art and future chal- lenges, Nature Communications 15, 2123 (2024)

B. Fauseweh, Quantum many-body simulations on digi- tal quantum computers: State-of-the-art and future chal- lenges, Nature Communications 15, 2123 (2024)

2024
[10]

Clarke and F

J. Clarke and F. K. Wilhelm, Superconducting quantum bits, Nature 453, 1031 (2008)

2008
[11]

Brecht, W

T. Brecht, W. Pfaff, C. Wang, Y. Chu, L. Frunzio, M. H. Devoret, and R. J. Schoelkopf, Multilayer microwave in- tegrated quantum circuits for scalable quantum comput- ing, npj Quantum Information 2, 16002 (2016)

2016
[12]

You and F

J. You and F. Nori, Atomic physics and quantum optics using superconducting circuits, Nature 474, 589 (2011)

2011
[13]

J. I. Cirac and P. Zoller, Quantum computations with cold trapped ions, Phys. Rev. Lett. 74, 4091 (1995)

1995
[14]

H¨ affner, C

H. H¨ affner, C. Roos, and R. Blatt, Quantum computing with trapped ions, Physics Reports 469, 155 (2008)

2008
[15]

Leibfried, R

D. Leibfried, R. Blatt, C. Monroe, and D. Wineland, Quantum dynamics of single trapped ions, Reviews of Modern Physics 75, 281 (2003)

2003
[16]

Henriet, L

L. Henriet, L. Beguin, A. Signoles, T. Lahaye, A. Browaeys, G.-O. Reymond, and C. Jurczak, Quantum computing with neutral atoms, Quantum 4, 327 (2020)

2020
[17]

Bluvstein, L

D. Bluvstein, L. Evered, Simon J., A. A. Geim, S. H. Li, H. Zhou, T. Manovitz, S. Ebadi, M. Cain, M. Kali- nowski, D. Hangleiter, J. P. Bonilla Ataides, N. Maskara, I. Cong, X. Gao, P. Sales Rodriguez, T. Karolyshyn, G. Semeghini, M. J. Gullans, M. Greiner, V. Vuleti´ c, and M...

2024
[18]

Chatterjee, P

A. Chatterjee, P. Stevenson, S. De Franceschi, A. Morello, N. P. de Leon, and F. Kuemmeth, Semicon- ductor qubits in practice, Nature Reviews Physics 3, 157 (2021)

2021
[19]

Burkard, T

G. Burkard, T. D. Ladd, A. Pan, J. M. Nichol, and J. R. Petta, Semiconductor spin qubits, Reviews of Modern Physics 95, 025003 (2023)

2023
[20]

Flensberg, F

K. Flensberg, F. von Oppen, and A. Stern, Engineered platforms for topological superconductivity and majo- rana zero modes, Nature Reviews Materials 6, 944 (2021)

2021
[21]

Aguado and L

R. Aguado and L. P. Kouwenhoven, Majorana qubits for topological quantum computing, Physics Today 73, 44 (2020)

2020
[22]

Cheng, X.-H

B. Cheng, X.-H. Deng, X. Gu, Y. He, G. Hu, P. Huang, J. Li, B.-C. Lin, D. Lu, Y. Lu, et al., Noisy intermediate- scale quantum computers, Frontiers of Physics 18, 21308 (2023)

2023
[23]

Bharti, A

K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, et al. , Noisy intermediate- scale quantum algorithms, Reviews of Modern Physics 94, 015004 (2022)

2022
[24]

Preskill, Quantum computing in the nisq era and be- yond, Quantum 2, 79 (2018)

J. Preskill, Quantum computing in the nisq era and be- yond, Quantum 2, 79 (2018)

2018
[25]

Breuer and F

H.-P. Breuer and F. Petruccione, The theory of open quantum systems (Oxford University Press on Demand, 2002)

2002
[26]

Papiˇ c, A

M. Papiˇ c, A. Auer, and I. de Vega, Fast estimation of physical error contributions of quantum gates, arXiv preprint arXiv:2305.08916 10.48550/arXiv.2305.08916 (2023)

2023 doi arXiv
[27]

Z. Cai, R. Babbush, S. C. Benjamin, S. Endo, W. J. Hug- gins, Y. Li, J. R. McClean, and T. E. O’Brien, Quantum error mitigation, Reviews of Modern Physics 95, 045005 (2023)

2023
[29]

R¨ onkk¨ o, O

J. R¨ onkk¨ o, O. Ahonen, V. Bergholm, A. Calzona, A. Geresdi, H. Heimonen, J. Heinsoo, V. Milchakov, S. Pogorzalek, M. Sarsby, et al. , On-premises supercon- ducting quantum computer for education and research, EPJ Quantum Technology 11, 32 (2024)

2024
[30]

Georgopoulos, C

K. Georgopoulos, C. Emary, and P. Zuliani, Modeling and simulating the noisy behavior of near-term quantum computers, Phys. Rev. A 104, 062432 (2021)

2021
[31]

Qaptiva, �� , accessed: 2025-07-25

2025
[32]

Saeedi, R

M. Saeedi, R. Wille, and R. Drechsler, Synthesis of quan- tum circuits for linear nearest neighbor architectures, Quantum Information Processing 10, 355 (2011)

2011
[33]

Marxer, A

F. Marxer, A. Veps¨ al¨ ainen, S. W. Jolin, J. Tuorila, A. Landra, C. Ockeloen-Korppi, W. Liu, O. Ahonen, A. Auer, L. Belzane, V. Bergholm, C. F. Chan, K. W. Chan, T. Hiltunen, J. Hotari, E. Hyypp¨ a, J. Ikonen, D. Janzso, M. Koistinen, J. Kotilahti, T. Li, J. Luus, M. Papic, ...

2023
[34]

Krantz, M

P. Krantz, M. Kjaergaard, F. Yan, T. P. Orlando, S. Gus- tavsson, and W. D. Oliver, A quantum engineer’s guide to superconducting qubits, Applied Physics Reviews 6, 10.1063/1.5089550 (2019)

2019 doi
[35]

D. C. McKay, C. J. Wood, S. Sheldon, J. M. Chow, and J. M. Gambetta, Efficient Z gates for quantum comput- ing, Physical Review A 96, 10.1103/physreva.96.022330 (2017)

2017 doi
[36]

Qiskit rgate, �� , accessed: 2024-07-30

2024
[37]

pytket phasedx, �� , accessed: 2024-07-30

2024
[38]

P. Kaye, R. Laflamme, and M. Mosca, An introduction to quantum computing (OUP Oxford, 2006)

2006
[39]

M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, 2010)

2010
[40]

Abdurakhimov, J

L. Abdurakhimov, J. Adam, H. Ahmad, O. Aho- nen, M. Algaba, G. Alonso, V. Bergholm, R. Beriwal, M. Beuerle, C. Bockstiegel, A. Calzona, C. F. Chan, D. Cucurachi, S. Dahl, R. Davletkaliyev, O. Fedorets, A. G. Frieiro, Z. Gao, J. Guldmyr, A. Guthrie, J. Hassel, H. Heimonen, J. H...

2024 arXiv
[41]

Kraus, States, Effects and Operations: Fundamen- tal Notions of Quantum Theory , edited by K

K. Kraus, States, Effects and Operations: Fundamen- tal Notions of Quantum Theory , edited by K. Kraus, A. B¨ ohm, J. D. Dollard, and W. H. Wootters, Lecture Notes in Physics, Vol. 190 (Springer Berlin Heidelberg, 1983)

1983
[42]

Carroll, S

M. Carroll, S. Rosenblatt, P. Jurcevic, I. Lauer, and A. Kandala, Dynamics of superconducting qubit relax- ation times, npj Quantum Information 8, 132 (2022)

2022
[43]

Chen, P.-Y

H.-B. Chen, P.-Y. Lo, C. Gneiting, J. Bae, Y.-N. Chen, and F. Nori, Quantifying the nonclassicality of pure de- phasing, Nature Communications 10, 3794 (2019)

2019
[44]

Nielsen, J

E. Nielsen, J. K. Gamble, K. Rudinger, T. Scholten, K. Young, and R. Blume-Kohout, Gate Set Tomography, Quantum 5, 557 (2021)

2021
[45]

Dankert, R

C. Dankert, R. Cleve, J. Emerson, and E. Livine, Ex- act and approximate unitary 2-designs and their applica- tion to fidelity estimation, Physical Review A—Atomic, Molecular, and Optical Physics 80, 012304 (2009)

2009
[46]

Magesan, J

E. Magesan, J. M. Gambetta, and J. Emerson, Char- acterizing quantum gates via randomized benchmark- ing, Physical Review A—Atomic, Molecular, and Optical Physics 85, 042311 (2012)

2012
[47]

J. Chow, J. M. Gambetta, L. Tornberg, J. Koch, L. S. Bishop, A. A. Houck, B. Johnson, L. Frunzio, S. M. Girvin, and R. J. Schoelkopf, Randomized benchmarking and process tomography for gate errors in a solid-state qubit, Physical Review Letters 102, 090502 (2009)

2009
[48]

J. P. Gaebler, A. M. Meier, T. R. Tan, R. Bowler, Y. Lin, D. Hanneke, J. D. Jost, J. Home, E. Knill, D. Leibfried, et al. , Randomized benchmarking of multiqubit gates, Physical Review Letters 108, 260503 (2012)

2012
[49]

Jin and S.-M

Z.-X. Jin and S.-M. Fei, Quantifying quantum coherence and nonclassical correlation based on hellinger distance, Phys. Rev. A 97, 062342 (2018)

2018
[50]

J. J. Wallman and J. Emerson, Noise tailoring for scalable quantum computation via randomized compiling, Physi- cal Review A 94, 052325 (2016)

2016
[51]

Hirata, M

Y. Hirata, M. Nakanishi, S. Yamashita, and Y. Nakashima, An efficient method to convert ar- bitrary quantum circuits to ones on a linear nearest neighbor architecture (2009)

2009
[52]

Nnizer, �� , accessed: 2025-05-30

2025
[53]

D. M. Greenberger, Ghz (greenberger—horne—zeilinger) theorem and ghz states, in Compendium of Quantum Physics, edited by D. Greenberger, K. Hentschel, and F. Weinert (Springer Berlin Heidelberg, Berlin, Heidel- berg, 2009) pp. 258–263

2009
[54]

Combined quantum noise model for simulating the noisy behaviour of nisq machines, �� , accessed: 2025-03-11

2025
[55]

Etxezarreta Martinez, P

J. Etxezarreta Martinez, P. Fuentes, P. Crespo, and J. Garcia-Frias, Time-varying quantum channel models for superconducting qubits, npj Quantum Information 7, 115 (2021)

2021

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [28]

Y. Kim, A. Eddins, S. Anand, K. X. Wei, E. Van Den Berg, S. Rosenblatt, H. Nayfeh, Y. Wu, M. Zale- tel, K. Temme, et al. , Evidence for the utility of quan- tum computing before fault tolerance, Nature 618, 500 (2023)

2023

[2] [1]

GHZ, random unitary and QAOA circuits In this section, we will compare the simulation re- sults of our noise model with the ones obtained from the real IQM quantum computing hardware as presented in Sec. II. For the purpose of our comparison, we will be us- ing the circuits described in Sec. IV B and the Hellinger distance as a quantitative measure of agr...

work page

[3] [2]

Comparison to other noise models To gain more insight into whether our proposed noise model performs well, we also made a comparison with an- C Results 8 �� FIG. 6. Hellinger distances for the quantum walk circuits from this work and [28] that have been measured...

work page arXiv

[4] [3]

Zhou, S.-T

L. Zhou, S.-T. Wang, S. Choi, H. Pichler, and M. D. Lukin, Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near- term devices, Phys. Rev. X 10, 021067 (2020)

2020

[5] [4]

Farhi, J

E. Farhi, J. Goldstone, and S. Gutmann, A quan- tum approximate optimization algorithm (2014), arXiv:1411.4028 [quant-ph]

arXiv 2014

[6] [5]

Peruzzo, J

A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’brien, A variational eigenvalue solver on a photonic quantum processor, Nature Communications 5, 4213 (2014)

2014

[7] [6]

Abbas, A

A. Abbas, A. Ambainis, B. Augustino, A. B¨ artschi, H. Buhrman, C. Coffrin, G. Cortiana, V. Dunjko, D. J. Egger, B. G. Elmegreen, N. Franco, F. Fratini, B. Fuller, J. Gacon, C. Gonciulea, S. Gribling, S. Gupta, S. Had- field, R. Heese, G. Kircher, T. Kleinert, T. Koch, G. Ko- rpas, S. Lenk, J. Marecek, V. Markov, G. Mazzola, S. Mensa, N. Mohseni, G. Nanni...

arXiv 2023

[8] [7]

I. M. Georgescu, S. Ashhab, and F. Nori, Quantum sim- ulation, Reviews of Modern Physics 86, 153 (2014)

2014