Pith. sign in

REVIEW 4 major objections 6 minor 55 references

Detection states of ions in a Paul trap via conventional and quantum machine learning algorithms

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read This paper reports that a support vector machine, a brightness-threshold statistics method, and a quantum-annealing classifier each read the |0⟩/|1⟩ state of 171Yb+ ions from camera images with 100.00% fidelity.

desk verdict The perfect fidelities are real but meaningless: labels come from the same brightness threshold the algorithms use, so this is a self-consistency check, not verified detection. read the letter →

arxiv 2412.10934 v2 pith:D7H3AVBE submitted 2024-12-14 quant-ph

Ilia Khomchenko , Andrei Fionov , Artem Alekseev , Daniil Volkov , Ilya A. Semerikov , Nikolay N. Kolachevsky , Aleksey K. Fedorov This is my paper · ORCID
classification quant-ph
keywords trapped-ionquantumcomputingstatedetectionelectronshelvingannealingQUBOsupportvectormachineionimagestatisticslearningreadout
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

Trapped-ion quantum computers need to know whether each ion is in its bright or dark state after a computation, and readout usually requires counting scattered photons. This paper tries to establish that a simple camera picture of the ion chain is enough: after locating the ions with k-means clustering on images of a 10-ion 171Yb+ chain, a support vector machine, a brightness-threshold statistics method, and the authors' quantum-annealing classifier "Quant" each reach 100.00% fidelity on the readout task. The claim matters because camera-based readout could replace or complement photomultiplier-style detectors, and because Quant's decision rule reduces to a 2x2 quadratic unconstrained binary optimization (QUBO) matrix that decomposes into single-qubit gates. The authors also report that maximum brightness is the one feature that carries nearly all of the classification power.

What carries the argument

The load-bearing object is the 2x2 QUBO matrix $$Q_i=\begin{pmatrix} \sigma_i & (\sigma_i+\varepsilon_i)/2 \\ (\sigma_i+\varepsilon_i)/2 & -\varepsilon_i \end{pmatrix}$$ built from the maximum brightness $\sigma_i$ of ion $i$ and a threshold $\varepsilon_i=152.8$. Maximizing $x^T Q_i x$ over $x\in\{0,1\}^2$ yields the ion's predicted state, and because the ions are independent the full chain's prediction is the tensor product $|x_1\rangle\otimes\cdots\otimes |x_N\rangle$. The paper also shows $Q_i = \frac{\sigma_i-\varepsilon_i}{2}(I-\sigma_z)+\frac{\sigma_i+\varepsilon_i}{2}\sigma_x$, so the Quant classifier is a single-qubit decomposition. The same brightness feature underlies the ion-image statistics labeler (threshold 153), the SVM features, and the convolution, which is why the comparison hinges on this one scalar per ion.

What would settle it

Record the same ion chain with both the camera and an independent photon-counting detector during readout, and compare each algorithm's label with the photon-counting ground truth; if the brightness-threshold labels disagree with the photon counter on any image, the 100.00% fidelities for SVM, ion-image statistics, and Quant are an artifact of the labeler.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that the |0⟩/|1⟩ state of a 171Yb+ ion in a linear Paul trap can be detected with perfect fidelity from the maximum brightness of its image spot on a 32x200 pixel camera frame. The authors prepared a 10-qubit chain via Hadamard gates to produce an equal mix of bright and dark states, used the equilibrium positions from the Coulomb-plus-harmonic trap model with k-means to define anchor boxes, labeled ten thousand images with a brightness threshold (maximum pixel value 153), and then trained or fitted classifiers. Support vector machine, ion-image statistics, and the quantum-annealing "Quant" algorithm all score 100.00% fidelity; a simple convolution scores 99.20%, k-means clustering 99.76%, and a quantum support vector machine 99.41%. The paper interprets these results as demonstrating that image-based conventional and quantum machine learning can give ultrahigh-fidelity readout of trapped-ion qubits.

Load-bearing premise

All fidelity scores are measured against labels that the ion-image statistics algorithm itself produced, so the results stand or fall on the correctness of that threshold-generated ground truth.

Editorial extensions

If this is right

  • If the reported fidelities are correct, camera images alone, without photomultiplier counting, are enough for perfect readout of 171Yb+ qubits in this Paul trap.
  • Because Quant's decision matrix is 2x2 and decomposes into single-qubit gates, the method scales linearly with ion number and can be implemented on small quantum devices or simulated annealers.
  • The fact that maximum brightness is the decisive feature means future image-based readout pipelines can stay extremely simple rather than requiring full neural networks.
  • The quantum support vector machine reaches 99.41% fidelity, and the paper states that 100% is reachable by increasing the training set to 10% of the data.
  • The fast classical baselines, k-means at 35 ms and ion-image statistics at 94 ms, make real-time camera-based readout practical if the result holds.

Reading between the lines

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

  • Because the same brightness threshold generated the labels against which all algorithms were scored, the three 100% results may partly reflect agreement with that threshold rather than independent knowledge of the ion state; an external validation, such as simultaneous photon-counting readout, would settle this.
  • A natural extension is to test whether the 2x2 QUBO classifier remains perfect at shorter exposure times or lower signal-to-noise, where the label threshold and the SVM's separating hyperplane would be expected to differ.
  • If the result transfers to other ion species and trap geometries, image-based readout could scale to larger two-dimensional arrays, where placing a separate photon detector per site is impractical.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper reports a benchmark of seven algorithms—ion-image statistics, convolution, support vector machine, k-means clustering, quantum support vector machine, and a QUBO-based 'Quant' algorithm—for classifying 171Yb+ ions in camera images as bright or dark qubit states. The H1 dataset consists of 10,000 images of 10-ion chains after Hadamard gates, while an Allbright dataset is used for position calibration. Fidelity is defined as agreement with automatically generated labels. Table 1 reports 100.00% fidelity for ion-image statistics, SVM, and Quant, with convolution, k-means, and QSVM at 99.20%, 99.76%, and 99.41%, respectively. The central claim is that these methods achieve perfect or near-perfect ion-state detection.

Significance. If validated, the claimed 100% readout fidelity from simple camera-image features would be practically interesting, especially the 2x2 QUBO 'Quant' approach that maps to single-qubit gates. The paper also provides a runtime comparison of the methods and makes data and code available. However, the significance of the headline result depends entirely on the correctness of the labels used as ground truth. Because the labels are generated by the same maximum-brightness threshold that the algorithms learn or reproduce, the reported fidelities do not establish detection fidelity against independently known ion states.

major comments (4)
  1. [Section 4 (label generation) and Table 1] The ground-truth labels for all fidelity scores are generated by the ion-image statistics algorithm: 'selecting the ion with the maximum brightness in each image, separated by a threshold value, to generate a dataset of labels, with respect to which we assess the prediction quality.' Since the ion-image-statistics classifier in Section 3 is itself this threshold rule (153), its 100.00% fidelity in Table 1 is self-agreement. No independent reference (e.g., photon-counting detector or independently calibrated state preparation) is provided, so the reported fidelities do not support the abstract's claim of perfect state detection.
  2. [Section 4.4.1, Eq. (15), and Section 5] The Quant algorithm classifies an ion by computing its maximum brightness σ_i and using a QUBO built around a threshold ε; Section 5 reports the optimized ε = 152.8. The auto-labels are produced by thresholding the same maximum-brightness feature at 153. The 100.00% score is therefore agreement between two nearly identical decision rules on the same feature, not independent validation. The SVM's 100.00% is likewise expected if maximum brightness is the dominant feature and the classes are defined by that threshold; the reported fidelities measure internal consistency of the labeling rule.
  3. [Section 5 (hyperparameter optimization)] The threshold values for ion-image statistics (153), convolution (θ = 0.012), and Quant (ε = 152.8) are optimized on the same H1 dataset used for the reported fidelity scores, and no train/test partition or cross-validation is described. This makes the comparison in-sample; an out-of-sample evaluation, or at minimum a description of how the thresholds were selected without using the test labels, is needed before the relative ranking of algorithms can be assessed.
  4. [Table 1] The table reports point fidelities without uncertainty estimates. For a finite dataset, a point estimate of 100.00% should be accompanied by binomial confidence intervals or a statement of the number of independently classified ion images; otherwise the precision implied by '100.00%' is not supported.
minor comments (6)
  1. [Section 1] The sentence 'Various conventional and quantum ML methods ... are presented in In. Sec. 4' contains a typo: 'in In.' should be 'in'.
  2. [Section 2] 'bright images correspond to ions in the sate |0⟩' should read 'state |0⟩'.
  3. [Eq. (7)] The convolution formula uses the summation index m without defining its range and mixes l and n in the subscripts; please rewrite the definition with explicit indices and ranges.
  4. [Eq. (13)] The QUBO matrix expression has notational problems: the off-diagonal entry contains a sum over j before the indices are specified, and the diagonal entry as written does not match the standard mapping from Eq. (12). Please rewrite the expression and verify it by substitution.
  5. [Section 5] The sentence 'Absolute recognition of the ion states of 171Yb+ (F = 100.00%) using QSVM is feasible' is not supported by the reported QSVM fidelity of 99.41%; this should be rephrased as a hypothesis or removed.
  6. [References] Several references are cited with incomplete author lists (e.g., 'et al (2019)' and 'Wu and et al (2021)'); please ensure all entries follow the journal's reference style.

Circularity Check

3 steps flagged · score 8.0 of 10

The reported 100% fidelities are self-consistency scores against auto-generated brightness-threshold labels, not independently verified ion-state detection.

  1. self definitional [Section 4 (Methods), first paragraph]
    "For each of the datasets, we automatically labeled the data for the ion states readout with the proposed approaches. This was done by applying the ion-image statistics algorithm, selecting the ion with the maximum brightness in each image, separated by a threshold value, (See Section 4) to generate a dataset of labels, with respect to which we assess the prediction quality of our methods."

    The ground-truth labels are not independent measurements; they are produced by the ion-image statistics algorithm itself, which thresholds the maximum pixel brightness (threshold set to 153 in Section 3). Consequently, the 'ion image statistics' row in Table 1 (100.00%) is the algorithm agreeing with its own labels by construction, and every other fidelity is measured against those same self-generated labels.

  2. fitted input called prediction [Section 4.4.1 (Quant algorithm) and Section 5 (Results and Discussion)]
    "For an image of each ion in the chain, the maximum brightness, σi (i = 1, 2, . . . , N), is computed. This process is followed by the calculation of the QUBO matrix for every σi, namely Qi = ... where εi is the threshold. ... we optimized the value of the maximum brightness for the algorithm based on ion image statistics, setting it to 153. ... Using a similar technique, we obtained εi = ε = 152.8 for the algorithm based on image parameters, which we later refer to as 'Quant'."

    Quant classifies each ion by the same maximum-brightness feature used to create the labels, with a fitted threshold ε=152.8 that almost coincides with the label-generation threshold 153. The 100.00% fidelity of Quant therefore measures how well a brightness threshold reproduces a dataset generated by an almost identical brightness threshold (153); it is a fitted-input self-consistency result rather than a validated prediction of ion quantum states.

1 more flagged steps
  1. fitted input called prediction [Section 4 (Methods), feature extraction and auto-labeling; Section 5 (Results), fidelity definition]
    "The feature set includes the following parameters: maximum brightness, minimum brightness, the difference between the maximum and minimum brightness values, mean brightness, median brightness, standard deviation, variance, skewness, kurtosis, fundamental tone frequency, and maximum power for the fundamental frequency derived from the Fourier transform. ... we automatically labeled the data ... selecting the ion with the maximum brightness in each image, separated by a threshold value..."

    SVM's feature set includes maximum brightness, and the labels are generated by a threshold on exactly that quantity. Since fidelity is computed against those auto-generated labels, the perfect SVM score can be attained simply by separating on the same brightness feature that defines the labels. The reported 100.00% is therefore agreement with the auto-labeling rule, not verified |0> and |1> detection against an external reference such as a photomultiplier-tube readout.

full rationale

The paper's central claim that SVM, ion-image statistics, and the Quant algorithm achieve perfect detection fidelity for 171Yb+ depends entirely on labels generated by the ion-image statistics threshold rule on maximum brightness (threshold 153). The Quant algorithm uses the same maximum-brightness feature with a fitted threshold ε=152.8, so its 100.00% score is essentially tautological; the ion-image statistics method trivially scores 100% because it defines the labels; and SVM is trained and evaluated against labels that are separable by the same dominant brightness feature. No independent state measurement (e.g., photomultiplier-tube readout) is reported against which the algorithms are checked. The paper even states this explicitly: 'we automatically labeled the data ... to generate a dataset of labels, with respect to which we assess the prediction quality.' This is therefore a circular benchmark: the perfect fidelities measure self-consistency of a brightness threshold, not externally validated ion-state detection. The result is still suggestive that camera images separate bright/dark classes well, but the headline perfect fidelity is an artifact of label construction. I assigned 8 rather than 10 because the underlying image data and brightness separation are physically real and the convolution/k-means/QSVM results do contain some independent algorithmic content; however, the headline perfect scores reduce by construction to the labeling rule.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central evaluation rests on one fitted brightness threshold that generates labels and a nearly identical threshold inside the Quant algorithm. This makes the benchmark largely circular. The remaining assumptions are standard atomic physics and prior trapped-ion models.

free parameters (6)
  • Maximum-brightness threshold for auto-labeling = 153
    Used in the ion-image statistics algorithm to generate the ground-truth labels for all fidelity comparisons; optimized with f1 score on the same data (Sec. 3, Sec. 5).
  • Quant threshold epsilon = 152.8
    Threshold embedded in the 2x2 QUBO matrix of the Quant algorithm; fitted with f1 score on the same dataset (Sec. 4.4.1, Sec. 5).
  • Convolution threshold theta = 0.012 (Sec. 5); 0.001523 (Sec. 4.1.2)
    Decision threshold for the convolution classifier; the paper reports two different values without noting the discrepancy.
  • Convolution reference vector = first element of the dataset
    Chosen as the kernel for convolution, an arbitrary hyperparameter that affects the classification (Sec. 4.1.2).
  • k-means number of clusters = 2 for state classes; 10 for ion positions
    k=2 separates bright/dark states, k=10 locates the ten ions; values are chosen by the known ion number and two-state setup (Sec. 4).
  • QSVM training set fraction = 5% (10% speculated)
    Sized down due to computational resources; the paper claims 100% would be feasible at 10% without showing it (Sec. 5).
assumptions (4)
  • domain assumption The auto-generated brightness-threshold labels are the correct ion quantum states
    The paper uses these labels as ground truth for all fidelity assessments (Sec. 4, labeling paragraph). This is the load-bearing assumption that the review flags as unverified.
  • domain assumption Bright fluorescence under 369.5 nm readout corresponds to state |0> and darkness to |1>
    Electron shelving model stated in Sec. 1 and Sec. 3; relies on established atomic physics but the mapping is assumed exact in the images.
  • domain assumption Ion equilibrium positions follow the Coulomb-plus-harmonic-trap model of James (1998)
    Used to locate ions before feature extraction (Sec. 4, Eq. (1)-(3)). This is prior literature, not derived in the paper.
  • domain assumption The QUBO ground state found by SimCIM or pyqiopt corresponds to the correct classification
    The Quant algorithm assumes the optimized binary vector gives the ion state (Sec. 4.4.1, Sec. 5). No proof that the 2x2 QUBO objective faithfully encodes state detection beyond thresholding.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Detection states of ions in a Paul trap via conventional and quantum machine learning algorithms." pith.science (2026). https://pith.science/paper/D7H3AVBE

@misc{pith2026241210934,
  author       = {Pith},
  title        = {Pith review of: Detection states of ions in a Paul trap via conventional and quantum machine learning algorithms},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D7H3AVBE}},
  note         = {Machine review of arXiv:2412.10934}
}
abstract

Trapped ions are among the leading platforms for quantum technologies, particularly in the field of quantum computing. Detecting states of trapped ions is essential for ensuring high-fidelity readouts of quantum states. In this work, we develop and benchmark a set of methods for ion quantum state detection using images obtained by a highly sensitive camera. By transforming the images from the camera and applying conventional and quantum machine learning methods, including convolution, support vector machine (classical and quantum), and quantum annealing, we demonstrate a possibility to detect the positions and quantum states of ytterbium ions in a Paul trap. Quantum state detection is performed with an electron shelving technique: depending on the quantum state of the ion its fluorescence under the influence of a 369.5 nm laser beam is either suppressed or not. We estimate fidelities for conventional and quantum detection techniques. In particular, conventional algorithms for detecting $^{171}$Yb$^{+}$, such as the support vector machine and photon statistics-based method,as well as our quantum annealing-based approach, have achieved perfect fidelity, which is beneficial compared to standard techniques. This result may pave the way for ultrahigh-fidelity detection of trapped ions via conventional and quantum machine learning techniques.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

55 extracted references · 29 canonical work pages

  1. [1]

    , Zalivako , I.V

    barticle Aksenov , M.A. , Zalivako , I.V. , Semerikov , I.A. , Borisenko , A.S. , Semenin , N.V. , Sidorov , P.L. , Fedorov , A.K. , Khabarova , K.Y. , Kolachevsky , N.N. : Realizing quantum gates with optically addressable ^ 171 Yb^ + ion qudits . Phys. Rev. A 107 , 052612 ( 2023 ) 10.1103/PhysRevA.107.052612 barticle

    work page doi:10.1103/physreva.107.052612 2023

  2. [2]

    , Albash , T

    barticle Boixo , S. , Albash , T. , Spedalieri , F.M. , Chancellor , N. , Lidar , D.A. : Experimental signature of programmable quantum annealing . Nature communications 4 ( 1 ), 2067 ( 2013 ) 10.1038/ncomms3067 barticle

    doi:10.1038/ncomms3067 2013

  3. [3]

    , Chang , M.-S

    barticle Brickman , K.-A. , Chang , M.-S. , Acton , M. , Chew , A. , Matsukevich , D. , Haljan , P. , Bagnato , V.S. , Monroe , C. : Magneto-optical trapping of cadmium . Physical Review A 76 ( 4 ), 043411 ( 2007 ) 10.1103/PhysRevA.76.043411 barticle

    work page doi:10.1103/physreva.76.043411 2007

  4. [4]

    , Kelly , J

    barticle Barends , R. , Kelly , J. , Megrant , A. , Veitia , A. , Sank , D. , Jeffrey , E. , White , T.C. , Mutus , J. , Fowler , A.G. , Campbell , B. , : Superconducting quantum circuits at the surface code threshold for fault tolerance . Nature 508 ( 7497 ), 500 -- 503 ( 2014 ) 10.1038/nature13171 barticle

    doi:10.1038/nature13171 2014

  5. [5]

    : A tutorial on support vector machines for pattern recognition

    barticle Burges , C.J. : A tutorial on support vector machines for pattern recognition . Data mining and knowledge discovery 2 ( 2 ), 121 -- 167 ( 1998 ) 10.1023/A:1009715923555 barticle

    doi:10.1023/a:1009715923555 1998

  6. [6]

    , Magesan , E

    barticle C \'o rcoles , A.D. , Magesan , E. , Srinivasan , S.J. , Cross , A.W. , Steffen , M. , Gambetta , J.M. , Chow , J.M. : Demonstration of a quantum error detection code using a square lattice of four superconducting qubits . Nature communications 6 ( 1 ), 6979 ( 2015 ) 10.1038/ncomms7979 barticle

    doi:10.1038/ncomms7979 2015

  7. [7]

    , Nielsen , E

    botherref Chen , J.-S. , Nielsen , E. , Ebert , M. , Inlek , V. , Wright , K. , Chaplin , V. , Maksymov , A. , P \'a ez , E. , Poudel , A. , Maunz , P. , et al.: Benchmarking a trapped-ion quantum computer with 29 algorithmic qubits. arXiv preprint arXiv:2308.05071 (2023) botherref

    arXiv 2023

  8. [8]

    , Zoller , P

    barticle Cirac , J.I. , Zoller , P. : Quantum computations with cold trapped ions . Physical review letters 74 ( 20 ), 4091 ( 1995 ) 10.1103/PhysRevLett.74.4091 barticle

    doi:10.1103/physrevlett.74.4091 1995

Show all 55 references
  1. [10]

    , Cui , J.-M

    botherref Ding , Z.-H. , Cui , J.-M. , Huang , Y.-F. , Li , C.-F. , Tu , T. , Guo , G.-C. : Fast high-fidelity readout of a single trapped-ion qubit via machine-learning methods. Physical Review Applied 12(1) (2019) 10.1103/physrevapplied.12.014038 botherref

    2019 doi

  2. [11]

    , Linke , N.M

    barticle Debnath , S. , Linke , N.M. , Figgatt , C. , Landsman , K.A. , Wright , K. , Monroe , C. : Demonstration of a small programmable quantum computer with atomic qubits . Nature 536 ( 7614 ), 63 -- 66 ( 2016 ) 10.1038/nature18648 barticle

    2016 doi

  3. [12]

    : Quantum supremacy using a programmable superconducting processor

    barticle al , F.A. : Quantum supremacy using a programmable superconducting processor . Nature 574 , 505 -- 510 ( 2019 ) barticle

    2019

  4. [13]

    , Tan , T

    barticle Edmunds , C. , Tan , T. , Milne , A. , Singh , A. , Biercuk , M. , Hempel , C. : Scalable hyperfine qubit state detection via electron shelving in the ^2 D _ 5/2 and ^2 F _ 7/2 manifolds in 171 Y b ^+ . Physical Review A 104 ( 1 ), 012606 ( 2021 ) 10.1103/PhysRevA.104...

    2021 doi

  5. [14]

    , Goldstone , J

    barticle Farhi , E. , Goldstone , J. , Gutmann , S. , Sipser , M. : Quantum computation by adiabatic evolution . arXiv preprint ( 2000 ) 10.48550/arXiv.quant-ph/0001106 barticle

    doi arXiv

  6. [15]

    , Mathur , A

    barticle Foody , G.M. , Mathur , A. : A relative evaluation of multiclass image classification by support vector machines . IEEE Transactions on Geoscience and Remote Sensing 42 , 1335 -- 1343 ( 2004 ) 10.1109/TGRS.2004.827257 barticle

    2004

  7. [16]

    , Tan , T.R

    barticle Gaebler , J.P. , Tan , T.R. , Lin , Y. , Wan , Y. , Bowler , R. , Keith , A.C. , Glancy , S. , Coakley , K. , Knill , E. , Leibfried , D. , : High-fidelity universal gate set for be 9+ ion qubits . Physical review letters 117 ( 6 ), 060505 ( 2016 ) 10.1103/PhysRevLett...

    2016 doi

  8. [17]

    , Allcock , D

    barticle Harty , T. , Allcock , D. , Ballance , C.J. , Guidoni , L. , Janacek , H. , Linke , N. , Stacey , D. , Lucas , D. : High-fidelity preparation, gates, memory, and readout of a trapped-ion quantum bit . Physical review letters 113 ( 22 ), 220501 ( 2014 ) 10.1103/PhysRev...

    2014 doi

  9. [18]

    , Broughton , M

    barticle Huang , H.-Y. , Broughton , M. , Cotler , J. , Chen , S. , Li , J. , Mohseni , M. , Neven , H. , Babbush , R. , Kueng , R. , Preskill , J. , McClean , J.R. : Quantum advantage in learning from experiments . Science 376 ( 6598 ), 1182 -- 1186 ( 2022 ) 10.1126/science.a...

    2022 doi

  10. [19]

    , Chung , S.K

    bchapter Hoang , T.M. , Chung , S.K. , Le , T. , Prestage , J.D. , Yi , L. , Tjoelker , R.I. , Yu , N. : Performance of micro mercury trapped ion clock . In: 2019 Joint Conference of the IEEE International Frequency Control Symposium and European Frequency and Time Forum (EFTF...

    2019

  11. [20]

    , Okhapkin , M

    barticle Huntemann , N. , Okhapkin , M. , Lipphardt , B. , Weyers , S. , Tamm , C. , Peik , E. : High-accuracy optical clock based on the octupole transition in Y b ^ + 171 . Physical Review Letters 108 ( 9 ), 090801 ( 2012 ) barticle

    2012

  12. [21]

    , Amin , M.H

    barticle Johnson , M.W. , Amin , M.H. , Gildert , S. , Lanting , T. , Hamze , F. , Dickson , N. , Harris , R. , Berkley , A.J. , Johansson , J. , Bunyk , P. , : Quantum annealing with manufactured spins . Nature 473 ( 7346 ), 194 -- 198 ( 2011 ) 10.1038/nature10012 barticle

    2011 doi

  13. [22]

    : Quantum dynamics of cold trapped ions with application to quantum computation

    botherref James , D.F. : Quantum dynamics of cold trapped ions with application to quantum computation. Applied Physics B (6), 181--190 (1998) 10.1007/s003400050373 botherref

    1998 doi

  14. [23]

    , Barends , R

    barticle Kelly , J. , Barends , R. , Fowler , A.G. , Megrant , A. , Jeffrey , E. , White , T.C. , Sank , D. , Mutus , J.Y. , Campbell , B. , Chen , Y. , : State preservation by repetitive error detection in a superconducting quantum circuit . Nature 519 ( 7541 ), 66 -- 69 ( 20...

    2015 doi

  15. [24]

    , Kryuchkov , D

    botherref Khabarova , K. , Kryuchkov , D. , Borisenko , A. , Zalivako , I. , Semerikov , I. , Aksenov , M. , Sherstov , I. , Abbasov , T. , Tausenev , A. , Kolachevsky , N. : Toward a new generation of compact transportable yb+ optical clocks. Symmetry 14(10) (2022) 10.3390/sy...

    2022 doi

  16. [25]

    : High fidelity quantum information processing with trapped ions

    botherref Langer , C.E. : High fidelity quantum information processing with trapped ions. PhD thesis, University of Colorado at Boulder (2006) botherref

    2006

  17. [26]

    , Blatt , R

    barticle Leibfried , D. , Blatt , R. , Monroe , C. , Wineland , D. : Quantum dynamics of single trapped ions . Reviews of Modern Physics 75 ( 1 ), 281 ( 2003 ) barticle

    2003

  18. [27]

    : Ising formulations of many np problems

    barticle Lucas , A. : Ising formulations of many np problems . Frontiers in physics 2 , 74887 ( 2014 ) 10.3389/fphy.2014.00005 barticle

    2014

  19. [28]

    , Baldwin , C.H

    barticle Moses , S.A. , Baldwin , C.H. , Allman , M.S. , Ancona , R. , Ascarrunz , L. , Barnes , C. , Bartolotta , J. , Bjork , B. , Blanchard , P. , Bohn , M. , : A race-track trapped-ion quantum processor . Physical Review X 13 ( 4 ), 041052 ( 2023 ) barticle

    2023

  20. [29]

    , Foody , G.M

    barticle Mathur , A. , Foody , G.M. : Multiclass and binary svm classification: Implications for training and classification users . IEEE Geoscience and Remote Sensing Letters 5 , 241 -- 245 ( 2008 ) 10.1109/LGRS.2008.915597 barticle

    2008

  21. [30]

    , Kim , J

    barticle Monroe , C. , Kim , J. : Scaling the ion trap quantum processor . Science 339 ( 6124 ), 1164 -- 1169 ( 2013 ) 10.1126/science.1231298 barticle

    2013 doi

  22. [31]

    , Nigg , D

    barticle Monz , T. , Nigg , D. , Martinez , E.A. , Brandl , M.F. , Schindler , P. , Rines , R. , Wang , S.X. , Chuang , I.L. , Blatt , R. : Realization of a scalable shor algorithm . Science 351 ( 6277 ), 1068 -- 1070 ( 2016 ) 10.1126/science.aad9480 barticle

    2016 doi

  23. [32]

    , Sankey , J

    barticle Madej , A. , Sankey , J. , Hanes , G. , Siemsen , K. , McKellar , A. : Observation of a midinfrared fine-structure transition for the single trapped barium ion . Physical Review A 45 ( 3 ), 1742 ( 1992 ) 10.1103/PhysRevA.45.1742 barticle

    1992 doi

  24. [33]

    , Szwer , D

    barticle Myerson , A. , Szwer , D. , Webster , S. , Allcock , D. , Curtis , M. , Imreh , G. , Sherman , J. , Stacey , D. , Steane , A. , Lucas , D. : High-fidelity readout of trapped-ion qubits . Physical Review Letters 100 ( 20 ), 200502 ( 2008 ) 10.1103/PhysRevLett.100.20050...

    2008 doi

  25. [34]

    , Chuang , I.L

    bbook Nielsen , M.A. , Chuang , I.L. : Quantum Computation and Quantum Information: 10th Anniversary Edition . Cambridge University Press , ??? ( 2010 ). https://books.google.it/books?id=-s4DEy7o-a0C bbook

    2010

  26. [35]

    , Younge , K.C

    barticle Olmschenk , S. , Younge , K.C. , Moehring , D.L. , Matsukevich , D.N. , Maunz , P. , Monroe , C. : Manipulation and detection of a trapped Y b ^+ hyperfine qubit . Physical Review A 76 ( 5 ), 052314 ( 2007 ) 10.1103/PhysRevA.76.052314 barticle

    2007 doi

  27. [36]

    , Varoquaux , G

    barticle Pedregosa , F. , Varoquaux , G. , Gramfort , A. , Michel , V. , Thirion , B. , Grisel , O. , Blondel , M. , Prettenhofer , P. , Weiss , R. , Dubourg , V. , : Scikit-learn: Machine learning in python . the Journal of machine Learning research 12 , 2825 -- 2830 ( 2011 )...

    2011

  28. [37]

    , Collins , M

    barticle Reens , D. , Collins , M. , Ciampi , J. , Kharas , D. , Aull , B.F. , Donlon , K. , Bruzewicz , C.D. , Felton , B. , Stuart , J. , Niffenegger , R.J. , : High-fidelity ion state detection using trap-integrated avalanche photodiodes . Physical Review Letters 129 ( 10 )...

    2022 doi

  29. [38]

    , Borisenko , A.S

    barticle Semenin , N.V. , Borisenko , A.S. , Zalivako , I.V. , Semerikov , I.A. , Khabarova , K.Y. , Kolachevsky , N.N. : Optimization of the readout fidelity of the quantum state of an optical qubit in the 171 Y b ^+ ion . JETP Letters 114 , 486 -- 492 ( 2021 ) 10.1134/S00213...

    2021 doi

  30. [39]

    , Hasebe , T

    barticle Suzuki , T. , Hasebe , T. , Miyazaki , T. : Quantum support vector machines for classification and regression on a trapped-ion quantum computer . Quantum Machine Intelligence 6 ( 1 ), 31 ( 2024 ) 10.1007/s42484-024-00165-0 barticle

    2024 doi

  31. [40]

    , Landsman , K.A

    barticle Seif , A. , Landsman , K.A. , Linke , N.M. , Figgatt , C. , Monroe , C. , Hafezi , M. : Machine learning assisted readout of trapped-ion qubits . Journal of Physics B: Atomic, Molecular and Optical Physics 51 ( 17 ), 174006 ( 2018 ) 10.1088/1361-6455/aad62b barticle

    2018 doi

  32. [41]

    , Petruccione , F

    bbook Schuld , M. , Petruccione , F. : Machine Learning with Quantum Computers . Springer , ??? ( 2021 ) bbook

    2021

  33. [42]

    , Yang , M.-S

    barticle Sinaga , K.P. , Yang , M.-S. : Unsupervised K -means clustering algorithm . IEEE access 8 , 80716 -- 80727 ( 2020 ) 10.1109/ACCESS.2020.2988796 barticle

    2020

  34. [43]

    , Ulanov , A

    barticle Tiunov , E.S. , Ulanov , A. , Lvovsky , A.I. : Annealing by simulating the coherent ising machine . Optics Express 27 ( 7 ), 10288 -- 10295 ( 2019 ) 10.1364/OE.27.010288 barticle

    2019 doi

  35. [44]

    , Verma , V

    barticle Todaro , S.L. , Verma , V. , McCormick , K.C. , Allcock , D. , Mirin , R. , Wineland , D.J. , Nam , S.W. , Wilson , A.C. , Leibfried , D. , Slichter , D. : State readout of a trapped ion qubit using a trap-integrated superconducting photon detector . Physical review l...

    2021

  36. [45]

    : The Nature of Statistical Learning Theory

    bbook Vapnik , V. : The Nature of Statistical Learning Theory . Springer , ??? ( 2013 ) bbook

    2013

  37. [46]

    , al , B

    barticle Wu , Y. , al , B. : Strong quantum computational advantage using a superconducting quantum processor . Phys. Rev. Lett. 127 , 180501 ( 2021 ) 10.1103/PhysRevLett.127.180501 barticle

    2021 doi

  38. [47]

    , Willsch , M

    barticle Willsch , D. , Willsch , M. , De Raedt , H. , Michielsen , K. : Support vector machines on the D -wave quantum annealer . Computer Physics Communications 248 , 107006 ( 2020 ) 10.1016/j.cpc.2019.107006 barticle

    2020

  39. [48]

    , Song , L

    barticle Yang , L. , Song , L. , Feng , Y. , Cao , M. , Zhang , P. , Zhang , X.-F. , Yao , J. : Zinc ion trapping in a cellulose hydrogel as a solid electrolyte for a safe and flexible supercapacitor . Journal of Materials Chemistry A 8 ( 25 ), 12314 -- 12318 ( 2020 ) 10.1039/...

    2020 doi

  40. [49]

    : Laser-cooled ions of magnesia and ytterbium for metrology and quantum computing

    botherref Zalivako , I.V. : Laser-cooled ions of magnesia and ytterbium for metrology and quantum computing. PhD thesis, P. N. Lebedev Physical Institute (2022) botherref

    2022

  41. [50]

    , Borisenko , A

    botherref Zalivako , I. , Borisenko , A. , Semerikov , I. , Sidorov , P. , Vishnyakov , P. , Khabarova , K. , Kolachevsky , N. : Nonselective paul ion trap loading with a light-emitting diode. Applied Physics Letters 115(10) (2019) 10.1063/1.5115324 botherref

    2019 doi

  42. [51]

    , Gircha , A.I

    botherref Zalivako , I.V. , Gircha , A.I. , Nikolaeva , A.S. , Drozhzhin , D.A. , Borisenko , A.S. , Korolkov , A.E. , Semenin , N.V. , Galstyan , K.P. , Kamenskikh , P.A. , Smirnov , V.N. , Aksenov , M.A. , Sidorov , P.L. , Kiktenko , E.O. , Khabarova , K.Y. , Fedorov , A.K. ...

    2024 arXiv

  43. [52]

    , Nikolaeva , A.S

    botherref Zalivako , I.V. , Nikolaeva , A.S. , Borisenko , A.S. , Korolkov , A.E. , Sidorov , P.L. , Galstyan , K.P. , Semenin , N.V. , Smirnov , V.N. , Aksenov , M.A. , Makushin , K.M. , Kiktenko , E.O. , Fedorov , A.K. , Semerikov , I.A. , Khabarova , K.Y. , Kolachevsky , N....

    2024 arXiv

  44. [53]

    , Semerikov , I.A

    barticle Zalivako , I.V. , Semerikov , I.A. , Borisenko , A.S. , Aksenov , M.D. , Khabarova , K.Y. , Kolachevsky , N.N. : Experimental S tudy of the O ptical Q ubit on the 435-nm Q uadrupole T ransition in the ^ 171 Yb^ + . Jetp Lett. 114 , 59 -- 64 ( 2021 ) 10.1134/S002136402...

    2021 doi

  45. [54]

    , Wang , H

    barticle Zhong , H.-S. , Wang , H. , Deng , Y.-H. , Chen , M.-C. , Peng , L.-C. , Luo , Y.-H. , Qin , J. , Wu , D. , Ding , X. , Hu , Y. , Hu , P. , Yang , X.-Y. , Zhang , W.-J. , Li , H. , Li , Y. , Jiang , X. , Gan , L. , Yang , G. , You , L. , Wang , Z. , Li , L. , Liu , N....

    2020 doi

  46. [55]

    write newline

    " write newline "" before.all 'output.state := FUNCTION string.to.integer 't := t text.length 'k := #1 'char.num := t char.num #1 substring 's := s is.num s "." = or char.num k = not and char.num #1 + 'char.num := while char.num #1 - 'char.num := t #1 char.num substring FUNCTI...

  47. [56]

    write newline

    " write newline "" before.all 'output.state := FUNCTION string.to.integer 't := t text.length 'k := #1 'char.num := t char.num #1 substring 's := s is.num s "." = or char.num k = not and char.num #1 + 'char.num := while char.num #1 - 'char.num := t #1 char.num substring FUNCTI...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.