From Simulation to Experiment: Benchmarking Machine-Learning Models for Quantum-Dot State Recognition

Valeria Díaz Moreno; Ryan P Khalili; Daniel Schug; Patrick J. Walsh; Donovan L. Buterakos; Justyna P. Zwolak

cs.CVNov 27, 2025

From Simulation to Experiment: Benchmarking Machine-Learning Models for Quantum-Dot State Recognition

Authors: Valeria Díaz Moreno, Ryan P Khalili, Daniel Schug, Patrick J. Walsh, Donovan L. Buterakos, Justyna P. Zwolak

Organizations: Department of Physics, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA · Department of Computer Science, University of Maryland, College Park, MD 20742, USA · Department of Applied Physics, Stanford University, Stanford, California 94305, USA · National Institute of Standards and Technology, Gaithersburg, MD 20899, USA ·

Abstract

Machine-learning models for quantum-dot state recognition are commonly trained using synthetic data, where large labeled datasets can be generated efficiently. However, strong performance on simulated data does not guarantee equally strong performance in experiments. Here, we benchmark five neural network architectures for quantum-dot state recognition using a shared synthetic training dataset and independent synthetic and experimental test sets. We compare convolutional and fully connected neural networks, a mixture density network, a vision transformer, and a U-Net across multiple training set sizes and two input normalization schemes. We find that model performance and rankings are closely preserved when moving from held-out synthetic patches to previously unseen simulated devices, but change substantially when the same models are evaluated on experimental data. In particular, the architecture achieving the highest synthetic performance is not the one that performs best experimentally. Input normalization further affects training behavior and, for some architectures, synthetic-to-experimental transfer. These results show that synthetic benchmark performance alone is insufficient to select models for experimental deployment and highlight the need to jointly evaluate architecture, preprocessing, data efficiency, model complexity, and experimental transfer when developing machine-learning tools for automated quantum-dot characterization and control.

Figures & tables

Figure 1: Examples of (a) two large and (b) two small experimentally acquired charge stability diagrams included in the QFlow 2.0: Quantum dot data for machine learning dataset [ 14 ] . The manually assigned fractional state vector labels for the small CSDs are ${\bf{p}}({\mathcal{R}}_{1})=(0,0,0.1,0,0.9)$ and ${\bf{p}}({\mathcal{R}}_{2})=(0,0,1,0,0)$ .

Figure 2: (a) Example synthetic charge stability diagram and (b) the corresponding state map generated using QDFlow [ 18 , 21 ] . The state-map labels correspond to the five states possible for a double-QD device: no dot (ND), single left dot (SL), single central dot (SC), single right dot (SR), and double dot (DD). (c-i) Example patch ${\mathcal{R}}$ sampled from the CSD, highlighted in panel (a) with a white rectangle. (c-ii) Corresponding state-map patch, highlighted in panel (b) with a white rectangle. The fractional state vector assigned to this patch is ${\bf{p}}({\mathcal{R}})=(0,0.1,0.5,0,0.4)$ .

Figure 3: Schematic representations of the five model architectures considered in this work. Each architecture receives the same

30\text{\times}30

preprocessed CSD patch as input and produces five output logits corresponding to the ND, SL, SC, SR, and DD states. The schematics illustrate the principal architectural differences among the models; implementation details are provided in section 2.4 .

Hyperparameter	MLP	CNN	MDN	ViT	U-Net
Learning rate $\eta$	$1\text{\times}{10}^{-3}$	$5\text{\times}{10}^{-4}$	$1\text{\times}{10}^{-3}$	$1\text{\times}{10}^{-4}$	$5\text{\times}{10}^{-4}$
Weight decay	$1\text{\times}{10}^{-4}$	$2\text{\times}{10}^{-4}$	$1\text{\times}{10}^{-4}$	$3\text{\times}{10}^{-4}$	$1\text{\times}{10}^{-4}$
Dropout	$0.3$	$0$	$0.2$	$0.1$	$0.2$
Activation	GELU	ReLU	GELU	GELU	ReLU
LR schedule	Cosine	Fixed	Cosine	Cosine	Cosine

Table 1: AdamW optimizer hyperparameters and learning-rate schedules used for the five model architectures.

Figure 4: MSE score,

S_{\rm MSE}=1-\mathrm{MSE}

, as a function of synthetic training-data budget for models evaluated on (a) the held-out synthetic test set, (b) the device-held-out synthetic test set, and (c) the experimental test set. For panels (a) and (b), synthetic samples are importance-weighted such that the marginal distribution of dominant QD states matches that of the experimental test set; panel (c) shows the experimental data without reweighting. Within each architecture, points correspond to training-data budgets of

20\text{\,}\mathrm{\%}

40\text{\,}\mathrm{\%}

60\text{\,}\mathrm{\%}

80\text{\,}\mathrm{\%}

, and

100\text{\,}\mathrm{\%}

. Colors identify the model architecture, while marker styles distinguish z-score (squares) and min-max (circles) normalization. Points show the mean performance across the

10

trained models for each condition, and error bars indicate one standard deviation across the

10

model-level scores, providing a measure of model-to-model variability.

Baseline	Held-out synthetic	Device-held-out synthetic	Experimental
Uniform	$0.326$	$0.316$	$0.277$
Training dataset mean	$0.337$	$0.333$	$0.286$

Table 2: MSE scores for the image-independent baseline predictors on the three evaluation datasets. The training dataset mean was established based on the full Synthetic test-set scores use the dominant-state importance weighting described in the text; the experimental test set is evaluated without reweighting.

Figure 5: (a) Number of training epochs before early stopping based on the validation KL-divergence loss for the MLP, CNN, MDN, ViT, and U-Net as a function of the synthetic training-data budget. Circles and squares denote min-max and z-score normalization, respectively; colors identify the model architectures and match those used in figure 3 . Points show the mean across the

10

trained models for each condition, and error bars indicate one standard deviation across the

10

model-level values, providing a measure of model-to-model variability in training behavior. (b) Number of trainable parameters for each architecture, reported in millions (M).

Figure 6: Predictive performance as a function of model complexity for (a) the held-out synthetic test set, (b) the device-held-out synthetic test set, and (c) the experimental test set. Mean MSE score,

S_{\rm MSE}

, is shown as a function of the number of trainable parameters for models trained using the full synthetic training-data budget. Circles and stars show the mean across the

10

trained models for each architecture using min-max and z-score normalization, respectively, with error bars indicating one standard deviation. Dashed and dotted lines connect the Pareto-optimal architectures for min-max and z-score normalization, respectively, when the corresponding frontier contains more than one model. The performance values used in the Pareto analysis follow the same evaluation procedure as in figure 4 : synthetic test-set scores are importance-weighted to match the experimental dominant-state distribution, while experimental scores are evaluated without reweighting.

Sim-to-Real Transfer

Simulation-to-reality transfer adapts models and policies trained in simulation for reliable performance in physical or otherwise real-world settings.

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Machine Learning for Charge State Characterization of Isolated Double Quantum Dots

Hyma Vallabhapurapu, Marco Candido, Krishna Choudhary, Paul Steinacker+6

Diraq, Sydney, New South Wales, Australia · School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, New South Wales, Australia

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Scaling semiconductor quantum dot arrays toward fault-tolerant quantum computing requires efficient tuneup of spin qubits, a process that depends on the analysis of charge stability maps (CSMs) and remains largely manual. While machine learning has been widely applied to CSM analysis in reservoir-coupled devices, automated tuning in the increasingly important isolated-mode regime has received limited attention. In isolated-mode CSMs, charge transitions appear as near-vertical lines, making them well suited to compact, task-specific models. We present two convolutional neural networks with fewer than one million parameters, trained on CSMs collected from 32 silicon metal-oxide-semiconductor (SiMOS) double-quantum-dot devices measured at approximately 1 K using an automated cryogenic probing system. Sixteen devices were used for training and sixteen were held out to evaluate cross-device generalization against hand-labeled ground truth. CSMClassifier identifies charge instability and sensor artifacts, achieving 94% macro-averaged accuracy across three quality classes on 2,407 held-out images. ChargeLineNet localizes charge-transition lines and determines electron occupancy, achieving 95.3% exact line-count accuracy on 1,131 held-out images. Combined into a single pipeline, the models correctly determine electron occupancy for 93.8% of clean held-out images. Pre-training on synthetic images substantially improves label efficiency. Fine-tuning the pre-trained model on limited experimental data maintains over 90% accuracy, whereas training from scratch degrades significantly under the same conditions. Together, the two models occupy only 6.5 MB and process images in less than 60 ms on standard laboratory hardware, demonstrating a practical path toward scalable, automated characterization and tuneup of quantum-dot devices.

Abstract

Sim-to-Real Transfer

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