Prediction of Compilation Options for Quantum Circuits via Adaptive Deep Residual Neural Network

Shouli He; Wen Liu; Yangzhi Li; Maoduo Li; Kai Chen; Yaohua Lu

doi:10.2478/qic-2026-0003

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Prediction of Compilation Options for Quantum Circuits via Adaptive Deep Residual Neural Network

Quantum Information & Computation

Volume 26 (2026): Issue 1 (March 2026)

By: Shouli He, Wen Liu, Yangzhi Li, Maoduo Li, Kai Chen and Yaohua Lu

Open Access

|Jun 2026

Figures & Tables

Circuit symbol representation of common quantum gates.

A simple quantum circuit example, including H-gate, X-gate, and CNOT-gate.

Common schematic diagram of quantum computing devices.

The currently available compilation options.

Table 1.

DRAN-Net parameter.

Layer name	Parameters
Liner layer	36×256
Dropout	0.01
Residual Block1	256×256
Attention Residual Block1	256×256
Residual Block2	256×256
Attention Residual Block2	256×256
Residual Block3	256×256
Attention Residual Block3	256×256
Residual Block4	256×256
Dropout	0.01
Liner layer	256×30

Table 2.

Prediction results between the new representation and the traditional model combination.

Model+Representation Set	Accuracy(%)	Top-3(%)	F1(%)
Random Forest+T-Pre	77.39 ± 0.05	95.54 ± 0.12	80.75 ± 0.12
Random Forest+N-Pre	78.88 ± 0.07	95.87 ± 0.16	81.90 ± 0.14
Gradient Boosting+T-Pre	76.63 ± 0.21	94.58 ± 0.17	80.47 ± 0.76
Gradient Boosting+N-Pre	77.31 ± 0.05	95.24 ± 0.61	79.95 ± 0.27
Decision Tree+T-Pre	72.93 ± 0.10	95.17 ± 0.00	78.52 ± 0.20
Decision Tree+N-Pre	73.47 ± 0.34	95.17 ± 0.64	74.35 ± 1.15
Nearest Neighbor+T-Pre	72.48 ± 0.00	93.68 ± 0.00	77.13 ± 0.00
Nearest Neighbor+N-Pre	73.73 ± 0.00	93.34 ± 0.00	78.56 ± 0.00

Performance Comparison of T-Pre and N-Pre.

Table 3.

Prediction results after removal of four lower-importance features.

Model+Representation Set	Accuracy(%)	Top-3(%)	F1(%)
Random Forest+T-Pre+AC	78.13 ± 0.08	95.84 ± 0.11	80.93 ± 0.22
Random Forest+N-Pre	78.88 ± 0.07	95.87 ± 0.16	81.90 ± 0.14
Gradient Boosting+T-Pre+AC	76.84 ± 0.19	94.91 ± 0.17	79.82 ± 0.20
Gradient Boosting+N-Pre	77.31 ± 0.05	95.24 ± 0.61	79.95 ± 0.27
Decision Tree+T-Pre+AC	73.05 ± 0.12	94.64 ± 0.22	76.14 ± 1.46
Decision Tree+N-Pre	73.47 ± 0.34	95.17 ± 0.64	74.35 ± 1.15
Nearest Neighbor+T-Pre+AC	72.48 ± 0.00	93.68 ± 0.00	77.13 ± 0.00
Nearest Neighbor+N-Pre	73.73 ± 0.00	93.34 ± 0.00	78.56 ± 0.00

Performance Comparison of T-Pre+AC and N-Pre.

Table 4.

Prediction results between the new model and the traditional representation combination.

Model+Representation Set	Accuracy (%)	Top-3 (%)	F1 (%)
DRAN-Net+T-Pre	82.32 ± 1.16	97.87 ± 0.48	82.18 ± 1.08
Random Forest+T-Pre	77.39 ± 0.05	95.54 ± 0.12	80.75 ± 0.12
Gradient Boosting+T-Pre	76.63 ± 0.21	94.58 ± 0.17	80.47 ± 0.76
Decision Tree+T-Pre	72.93 ± 0.10	95.17 ± 0.00	78.52 ± 0.20
Nearest Neighbor+T-Pre	72.48 ± 0.00	93.68 ± 0.00	77.13 ± 0.00
Multilayer Perceptron+T-Pre	65.35 ± 0.54	82.06 ± 1.18	58.49 ± 1.57
Support Vector Machine+T-Pre	62.49 ± 0.00	78.70 ± 0.00	69.51 ± 0.00
Naive Bayes+T-Pre	33.60 ± 0.00	54.08 ± 0.00	29.48 ± 0.00

Performance Comparison of model using T-Pre.

Table 5.

Prediction results of various model methods.

Model+Representation Set	Accuracy (%)	Top-3 (%)	F1 (%)
Random Forest+T-Pre	77.39 ± 0.05	95.54 ± 0.12	80.75 ± 0.12
Gradient Boosting+T-Pre	76.63 ± 0.21	94.58 ± 0.17	80.47 ± 0.76
Decision Tree+T-Pre	72.93 ± 0.10	95.17 ± 0.00	78.52 ± 0.20
Nearest Neighbor+T-Pre	72.48 ± 0.00	93.68 ± 0.00	77.13 ± 0.00
Multilayer Perceptron+T-Pre	65.35 ± 0.54	82.06 ± 1.18	58.49 ± 1.57
Support Vector Machine+T-Pre	62.49 ± 0.00	78.70 ± 0.00	69.51 ± 0.00
Naive Bayes+T-Pre	33.60 ± 0.00	54.08 ± 0.00	29.48 ± 0.00
GraphSAGE+TWIG-Pre	71.10 ± 1.02	95.77 ± 0.88	60.41 ± 2.49
GIN+TWIG-Pre	48.28 ± 0.64	84.52 ± 1.57	21.82 ± 1.50
GCN+TWIG-Pre	30.97 ± 0.25	56.54 ± 3.23	3.83 ± 0.49
Ours (DRAN-Net+T-Pre)	82.83 ± 1.63	98.14 ± 0.32	82.80 ± 1.52

Performance Comparison of various model methods.

Table 6.

Prediction results on the device selection task from [13].

Model	Accuracy (%)	Top-3 (%)	F1 (%)
DRAN-Net	98.82 ± 0.41	100 ± 0.00	98.71 ± 0.50
Random Forest	98.15 ± 0.63	100 ± 0.00	97.01 ± 1.00

Performance comparison on the [13] device selection task.

Table 7.

Statistical significance tests.

Ours (DRAN-Net+N-Pre) vs.	Metric	t	p-Value (α = 0.05)
Ours (DRAN-Net+N-Pre) vs. Random Forest+T-Pre	Accuracy	8.4356	0.0009
	Top-3	17.0113	0.0000
	F1	3.0064	0.0391
Ours vs. Gradient Boosting+T-Pre	Accuracy	7.4592	0.0017
	Top-3	21.9686	0.0000
	F1	3.0658	0.0154
Ours vs. Decision Tree+T-Pre	Accuracy	13.5555	0.0002
	Top-3	20.7535	0.0000
	F1	6.2425	0.0030
Ours vs. Nearest Neighbor+T-Pre	Accuracy	14.1983	0.0001
	Top-3	31.1652	0.0000
	F1	8.3411	0.0011
Ours vs. Multilayer Perceptron+T-Pre	Accuracy	22.7628	0.0000
	Top-3	29.4089	0.0000
	F1	24.8754	0.0000
Ours vs. Support Vector Machine+T-Pre	Accuracy	27.9028	0.0000
	Top-3	135.8411	0.0000
	F1	19.5509	0.0000
Ours vs. Naive Bayes+T-Pre	Accuracy	67.5347	0.0000
	Top-3	307.8786	0.0000
	F1	78.4389	0.0000
Ours vs. GraphSAGE+TWIG-Pre	Accuracy	13.6408	0.0000
	Top-3	5.6596	0.0005
	F1	17.1617	0.0000
Ours vs. GIN+TWIG-Pre	Accuracy	44.1176	0.0000
	Top-3	19.0074	0.0000
	F1	63.8515	0.0000
Ours vs. GCN+TWIG-Pre	Accuracy	70.3203	0.0000
	Top-3	28.6586	0.0000
	F1	110.5693	0.0000

Table 8.

Efficiency and effectiveness of different methods.

Method	Average Time(s)	Time Ranges(s)	Accuracy (%)	Top-3 (%)
Exhaustive Search	118	5-243	100 ± 0.00	100 ± 0.00
DRAN-Net Prediction	0.27	0.19-0.51	90.40± 0.80	99.40±0.49

Exhaustive search vs. DRAN-Net prediction.

References

Authors

Metrics

Articles in this issue

DOI: https://doi.org/10.2478/qic-2026-0003 | Journal eISSN: 3106-0544 (formerly 1533-7146) | Journal ISSN: 1533-7146

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Language: English

Page range: 38 - 67

Submitted on: Oct 10, 2025

Accepted on: Dec 16, 2025

Published on: Jun 4, 2026

Published by: Cerebration Science Publishing Co., Limited

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

quantum circuit compilation,

deep residual neural network,

Related subjects:

Computer sciences in the humanities,

Computer sciences in natural sciences,

Mathematics,

Applied mathematics,

Physics,

Quantum physics

© 2026 Shouli He, Wen Liu, Yangzhi Li, Maoduo Li, Kai Chen, Yaohua Lu, published by Cerebration Science Publishing Co., Limited
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 26 (2026): Issue 1 (March 2026)

Prediction of Compilation Options for Quantum Circuits via Adaptive Deep Residual Neural Network

Figures & Tables

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Table 1.

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Table 2.

Figure 16.

Table 3.

Figure 17.

Table 4.

Figure 18.

Table 5.

Figure 19.

Table 6.

Figure 20.

Table 7.

Table 8.

Figure 21.

Paradigm

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