The Role of Quantum in Hybrid Quantum-Classical Neural Networks: An Empirical Assessment

Dominik Freinberger; Philipp Moser

quant-phJan 8, 2026

The Role of Quantum in Hybrid Quantum-Classical Neural Networks: An Empirical Assessment

Authors: Dominik Freinberger, Philipp Moser

Organizations: Research Unit Medical Informatics, RISC Software GmbH, Softwarepark 32a, Hagenberg, 4232, Upper Austria, Austria.

Abstract

Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum processing. Although numerous hybrid architectures have been proposed and demonstrated successfully on benchmark tasks, a significant open question remains regarding the specific contribution of quantum components to the overall performance of these models. In this work, we aim to shed light on the impact of quantum processing within hybrid quantum-classical neural network architectures through a rigorous statistical study under ideal, noise-free simulation. We systematically assess a selection of commonly used HQNN architectures on three binary medical classification tasks covering medical signal data as well as planar and volumetric images. In doing so, we examine the influence attributable to classical and quantum aspects such as encoding schemes, entanglement, and circuit size. Within this experimental setting, we find that in best-case scenarios, hybrid models show performance comparable to their classical counterparts. However, in most cases, performance metrics deteriorate when the investigated QNN components replace matched classical processing layers. Our multi-modal analysis provides empirical insights into the contributions of the tested quantum components and advocates for cautious claims and design choices for hybrid models in near-term applications.

Figures & tables

Panel of Figure 1 — Figure 1: Examples from the three datasets used in this study: 1D ECG (a), 2D breast ultrasound (b), and 3D chest CT (c).

Figure 2: Schematic illustration of the general HQNN architecture. A classical network

L(\boldsymbol{\tilde{x}},\boldsymbol{w})

(with 3, 1 or no convolutional layers) and a fully-connected layer (with or without

\pi\cdot\tanh

activation) first extracts a latent feature vector

\boldsymbol{x}

(16 or 256 features).

\boldsymbol{x}

is then encoded into a quantum circuit

U(\boldsymbol{x},\boldsymbol{\theta})

using either angle or amplitude encoding (with optional entanglement). The expectation value of either a local or global Pauli - Z measurement is finally linearly mapped to the output logit.

Figure 3: Hybrid and classical model performances with different levels of classical pre-processing across data modalities. The influence of the pre-processing depth ( 3conv , 1conv , 0conv ) is statistically compared between hybrid models and classical reference models (*:

p<0.05

, **:

p<0.01

, ***:

p<0.001

Figure 4: Hybrid and classical model performances with different latent space dimensions across data modalities. The influence of the latent space dimension ( 16 vs. 256 features) is statistically compared between hybrid models and classical reference models (*:

p<0.05

, **:

p<0.01

, ***:

p<0.001

Figure 5: ROC-AUC scores achieved by hybrid models, grouped by QNN architecture. Amplitude encoding models ( Amp-Gen , QCNN ) generally outperformed angle encoding models ( Ang-RY , Ang-Arb ).

Figure 6: Influence of quantum-specific hyperparameters on hybrid model performance . The impact of latent layer activation function, effect of entanglement and type of observable is statistically compared for hybrid models (*:

p<0.05

, **:

p<0.01

, ***:

p<0.001

). In the observable comparison, striped boxplots represent angle encoding models, while non-striped boxplots represent amplitude encoding models. For amplitude encoding, this observable comparison includes only Amp-Gen, since QCNN uses a single local measurement by design.

Dimension	Configuration	ROC-AUC	Avg. Precision	Balanced Acc.
	Classical	$0.91_{[0.83,0.94]}$	$0.90_{[0.81,0.93]}$	$0.84_{[0.77,0.86]}$
	Ang-RY	$0.80_{[0.53,0.89]}$	$0.82_{[0.56,0.90]}$	$0.70_{[0.50,0.83]}$
1D	Ang-Arb	$0.69_{[0.52,0.79]}$	$0.70_{[0.55,0.77]}$	$0.59_{[0.50,0.70]}$
	Amp-Gen	$\mathbf{0.93_{[0.91,0.94]}}$	$\mathbf{0.92_{[0.90,0.94]}}$	$\mathbf{0.85_{[0.74,0.87]}}$
	QCNN	$0.92_{[0.92,0.93]}$	$0.92_{[0.91,0.92]}$	$0.79_{[0.78,0.82]}$
	Classical	$\mathbf{0.87_{[0.83,0.94]}}$	$0.94_{[0.91,0.97]}$	$\mathbf{0.83_{[0.77,0.86]}}$

Table 1: Performance metrics for classical and hybrid models. Classical models are grouped together, hybrid models are split by QNN type. For each group the median score achieved is shown as well as minima and maxima in brackets. Bold highlights best median scores within each data modality.

Hybrid Quantum-Classical ML

Hybrid quantum-classical machine learning combines quantum circuits or annealers with classical models for learning, prediction, generation, and optimization.

See all 179 papers

Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
Aug 2, 2026Léo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin +4Hybrid Quantum-Classical ML Variational Quantum Circuits
Terra Quantum AG, Kornhausstrasse 25, St. Gallen, 9000, Switzerland · Département de Physique Appliquée, Université de Genève, 24 Quai Ernest-Ansermet, Geneva, 1211, Switzerland
Do emulated quantum circuits change what CNNs look at? Performance and explainability comparison in medical image classification
Jul 23, 2026Guillermo Rubiños Rodríguez, Martín Ottavianelli, Mateo Alonso +4Quantum Convolutional Neural Networks Quantum-Inspired ML
Instituto Tecnol´ogico de Galicia, Cant´on Grande 9, Planta 3, 15003 A Coru˜na, Spain · Centro Singular de Investigaci´on en Tecnolox´ıas Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain · Department of Neuroradiology, LMU University Hospital, Ludwig Maximilian University of Munich, Marchioninistraße 15, 81377 Munich, Germany +1
Hybrid Quantum-Classical Neural Architecture Search
May 18, 2026Alberto Marchisio, Muhammad Kashif, Nouhaila Innan +1Quantum Machine Learning Hybrid Quantum-Classical ML
eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, United Arab Emirates · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, United Arab Emirates

Aug 2, 2026J/K move · Enter open · S save

Hybrid Quantum Neural Networks: Theory, Implementations, and Applications

Léo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin, Pavel Sekatski+3

Terra Quantum AG, Kornhausstrasse 25, St. Gallen, 9000, Switzerland · Département de Physique Appliquée, Université de Genève, 24 Quai Ernest-Ansermet, Geneva, 1211, Switzerland

Hybrid Quantum-Classical ML Variational Quantum Circuits Quantum Neural Networks

Open paper AskPDF arXiv

Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, and hybrid quantum neural networks, which combine classical neural-network components with quantum information processing units, have emerged as a practical framework for near-term quantum technologies. However, the rapid development of the field across diverse architectures, benchmarks and hardware assumptions makes it difficult to assess the utility of various proposals, identify where genuine advantages may arise, and determine how practitioners can use these models. While recent benchmarks caution that such gains have not yet been demonstrated at scale, theoretical work has identified tasks on which quantum models hold provable advantages, and hybrid approaches have delivered promising results on practical problems using deliberately compact quantum components and substantially fewer trainable parameters. Here, we review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities. We summarize their main theoretical and methodological foundations, survey some of the most promising architectures developed so far, and examine their implementation challenges and reported performance. By consolidating these perspectives, this review provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.

Abstract

Hybrid Quantum-Classical ML

Explore similar work

Hybrid Quantum Neural Networks: Theory, Implementations, and Applications

Do emulated quantum circuits change what CNNs look at? Performance and explainability comparison in medical image classification

Hybrid Quantum-Classical Neural Architecture Search