QuantumBoostNet: Hybrid Classical-Quantum Cardiac View Identification

Mihai Udrescu-Milosav; Stefan-Alexandru Jura; Mihai Udrescu; Gerhard-Paul Diller

cs.LGAug 27, 2026

QuantumBoostNet: Hybrid Classical-Quantum Cardiac View Identification

Authors: Mihai Udrescu-Milosav, Stefan-Alexandru Jura, Mihai Udrescu, Gerhard-Paul Diller

Organizations: Department of Computers and Information Technology Politehnica University TimisÇoara TimisÇoara, Romania · Department of Cardiology & Angiology III Adult Congenital & Valvular Heart Disease Center University Hospital Münster Münster, Germany

Abstract

Accurate identification of the correct view or angle in cardiac ultrasound (echocardiogram) is critical for cardiologic imaging, precise anatomical interpretation, and reducing clinical errors. Most state-of-the-art classical models perform well on standard benchmarks but give suboptimal results in specialized medical imaging due to high noise levels. To address these challenges, this work proposes the hybrid classical-quantum architecture QuantumBoostNet, which combines a classical backbone with two heads: one classical and one quantum, a parametrized 10-qubit quantum circuit. The main contribution of this work is training in two stages, with an adaptive transition between heads controlled by a mixing parameter that monitors loss dynamics. Extensive experiments show that QuantumBoostNet outperforms the implemented baselines under matched training conditions. Statistically significant gains appear on FashionMNIST ( $p_t=9.91\!\times\!10^{-6}$ ) and MNIST ( $p_t=1.29\!\times\!10^{-5}$ ), with a less significant improvement on the echocardiography task ( $p_t=0.0711$ ). The model also shows robustness to noise. These findings support continued development of hybrid classical-quantum models for specialized medical imaging applications.

Figures & tables

Fig. 1: QuantumBoostNet overview: Feature vector $\mathbf{f}$ is routed in parallel to a quantum and a classical head, whose predictions are fused via $\alpha$ ; training proceeds in two phases, with the transition triggered by validation-accuracy plateau or exhaustion of the Phase 1 budget $T_{1}$ , whichever occurs first.

#	View class	#	View class
1	PLAX left ventricle	10	A4C zoomed left ventricle
2	PLAX zoomed MV	11	Apical 5 chamber
3	PLAX RV inflow	12	A2C
4	PSAX focus on AV	13	A2C zoomed left ventricle
5	PSAX papillary muscles	14	A3C
6	PSAX apex	15	A3C zoomed left ventricle

TABLE I: The seventeen transthoracic echocardiography view classes retained in the dataset of Wegner et al. [ 7 ] .

Model	Accuracy (%)	Time (s)	Precision (%)	Weighted Precision (%)	Recall (%)	F1 (%)	Weighted F1 (%)
ViewCNN	53.55 $\pm$ 1.61	93.0	45.4	52.8	41.4	41.8	51.7
ViewOptimizedCNN	63.44 $\pm$ 1.39	125.5	56.8	64.6	52.7	52.3	62.1
ViewResNet	75.95 $\pm$ 1.18	124.8	72.9	75.7	68.8	69.5	75.5
ViewOptimizedResNet	75.54 $\pm$ 1.23	124.2	72.7	75.4	69.2	69.9	75.0
MadaniCNN	68.76 $\pm$ 1.35	253.4	61.6	68.1	58.7	59.3	68.2
LiViewNet	75.52 $\pm$ 1.36	635.9	72.2	75.6	69.4	69.7	75.0

TABLE II: Average model accuracy (10-fold CV), average per-epoch training time, and average macros on the View Classification dataset for S1. Best scores, excluding training time, in bold.

Fig. 3: The confusion matrix of QuantumBoostNet V1’s sixth fold.

Model	02	06	08	12	13	14	15	16	18	21	22	23	27	30
ViewCNN	77.0	2.9	47.1	47.8	40.7	0.9	67.5	38.7	3.8	49.2	23.1	55.4	71.7	59.2
ViewOptimizedCNN	82.9	7.3	63.3	56.1	44.5	8.9	76.6	60.7	22.2	62.1	18.0	69.4	87.5	72.4
ViewResNet	92.8	39.3	83.2	63.4	51.6	17.3	88.0	72.1	70.1	76.9	51.3	88.0	90.7	89.1
ViewOptimizedResNet	92.2	48.3	81.8	63.8	49.7	18.7	87.7	70.1	66.9	78.7	52.4	87.4	90.5	91.5
MadaniCNN	86.4	1.1	73.2	52.8	44.4	6.4	84.3	63.0	37.7	78.6	53.0	79.9	87.9	83.9
LiViewNet	89.8	40.2	81.1	62.2	49.5	19.9	88.2	73.8	67.3	83.1	65.0	86.2	87.1	83.8

TABLE III: Average per-class F1 score (%) (10-fold CV) on the View Classification dataset for S1. Best F1 score per class in bold.

Fig. 4: Fold-wise accuracy distributions on the View Classification dataset (10-fold CV) in S1. Blue: classical; orange: hybrid; green: QuantumBoostNet .

Model	Dep.	AD	Avg.
QuantumBoostNet V1 (Simulated NISQ)	77.01	78.25	77.63
QuantumBoostNet V2 (Simulated NISQ)	74.49	72.45	73.47
QuantumBoostNet V3 (Simulated NISQ)	78.18	77.32	77.75
QuantumBoostNet V1 (QPU)	-	-	77.78

TABLE IV: Test accuracy (%) of the QuantumBoostNet models under noisy conditions on the View Classification dataset in S2. Best result per column in bold.

Model	FashionMNIST	MNIST
ViewCNN	88.09 $\pm$ 0.19	98.42 $\pm$ 0.11
ViewOptimizedCNN	92.25 $\pm$ 0.15	99.30 $\pm$ 0.06
ViewResNet	93.03 $\pm$ 0.23	99.36 $\pm$ 0.04
ViewOptimizedResNet	93.08 $\pm$ 0.23	99.41 $\pm$ 0.04
MadaniCNN	92.27 $\pm$ 0.27	99.44 $\pm$ 0.05
LiViewNet	92.15 $\pm$ 0.23	99.43 $\pm$ 0.02

TABLE V: Mean accuracy (%)

\pm

SD on FashionMNIST in S3 and MNIST in S4 (10-fold CV). Best result per column in bold.

Dataset	Best Classical	Best QuantumBoostNet	$n$	$\Delta$ (pp)	$t$	$p_{t}$	$W$	$p_{W}$	Cohen’s $d$
View Classification	ViewResNet	QuantumBoostNet V1	10	$+1.24$	2.05	0.0711	11.0	0.1055	0.65
FashionMNIST	ViewOptimizedResNet	QuantumBoostNet V3	10	$+0.89$	8.84	$9.91\!\times\!10^{-6}$	0.0	0.0020	2.79
MNIST	MadaniCNN	QuantumBoostNet V3	10	$+0.18$	8.56	$1.29\!\times\!10^{-5}$	0.0	0.0020	2.71

TABLE VI: Paired statistical comparison between the best classical model and the best QuantumBoostNet variant per dataset. Positive

\Delta

indicates that the QuantumBoostNet model outperforms the classical baseline.

Dataset	$k$	$\chi^{2}$	$p$ -value
View Classification	15	124.34	$8.86\!\times\!10^{-20}$
FashionMNIST	15	127.89	$1.77\!\times\!10^{-20}$
MNIST	15	127.17	$2.45\!\times\!10^{-20}$

TABLE VII: Friedman omnibus test per dataset. All

p

-values indicate that model identity has a significant effect on fold accuracy.

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Evolving Hybrid Quantum-Classical Architectures for Image Classification

Devroop Kar, Daniel Krutz, Travis Desell

Rochester Institute of Technology Rochester, NY 14623, USA

Hybrid Quantum-Classical ML Variational Quantum Circuits Image Classification

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Hybrid quantum classical neural networks integrate parameterized quantum circuits (PQCs) with established deep learning architectures, but their performance depends strongly on the choice of quantum circuit architecture, a choice that remains largely manual. Most existing approaches rely on hand-designed or fixed circuit ansätze, requiring circuit structure, gate composition, and qubit connectivity to be specified in advance with no guarantee that they suit the task. This limitation is especially acute in image classification, where quantum circuits must transform features extracted by classical networks while remaining compact enough for practical training, requirements that generic, task-agnostic ansätze are unlikely to satisfy simultaneously. We extend EXAQC, an evolutionary framework for automated quantum circuit discovery, to image classification. EXAQC evolves PQCs as intermediate processing modules while retaining classical feature-extraction and prediction layers. On MNIST, Fashion-MNIST, and CIFAR-10, EXAQC achieves 98.42%, 90.62%, and 85.47% accuracy, respectively, while using comparable gate counts to other quantum architecture-search methods. Against classical networks, evolved hybrid models maintain comparable accuracy with substantially fewer trainable parameters, reaching 85.68% on CIFAR-10 with over 25

\times

fewer parameters than a 10-layer CNN. Encoding choice also matters: rotation-based encodings (RX, RY, U3) outperform amplitude encoding by 22-25 points on CIFAR-10. These results demonstrate that automated circuit discovery yields compact quantum modules that can replace larger classical components in vision architectures while retaining competitive accuracy.

Abstract

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