Neural Networks Advance Hadronic Physics Via Data-Driven Quantum Model Selection

Scientists are increasingly focused on determining when deep learning truly outperforms established methods, a crucial question as artificial intelligence matures. Brandon B Le and D Keller, alongside their colleagues, tackle this problem specifically within the complex field of hadronic physics, presenting a novel framework for intelligently selecting between classical and neural network models. Their research introduces a ‘quantum qualifier’ , a quantitative metric , that assesses data characteristics like complexity, noise and dimensionality to predict which modelling approach will yield the most accurate results. This is significant because it moves beyond simply applying neural networks, instead offering a principled way to deploy these tools in precision hadronic physics, demonstrated here through analysis of deeply virtual Compton scattering and Compton form factor extraction.

Researchers developed a quantitative diagnostic tool, termed the “quantum qualifier,” which guides model selection based on intrinsic data properties, effectively predicting which network type is best suited for a given task. DVCS, a key experimental probe of nucleon structure, involves extracting Generalized Parton Distributions (GPDs) through Compton Form Factors (CFFs), a process complicated by experimental limitations and strong parameter correlations. Classical deep learning has already shown promise in this area, but the research team sought to determine if QDNNs could provide further improvements.

Their work doesn’t simply construct quantum architectures, but instead focuses on when those architectures are most beneficial, offering a crucial step towards practical quantum applications in this domain. While demonstrating definitive quantum advantage remains an open question, QDNNs offer a flexible framework for exploring performance gains in inverse problems and high-dimensional regression. The researchers constructed QDNNs with layers of parameterized unitary operators and entangling gates, encoding classical data into quantum representations via angle embedding techniques. Classical outputs are then obtained by evaluating expectation values of Hermitian observables, allowing for integration with conventional learning methods. This approach leverages the exponential structure of quantum state space, potentially exceeding the polynomial scaling of classical networks and providing mechanisms for modeling complex correlations. Schematic diagrams illustrate the architecture of both the CDNN, featuring ReLU neurons and a sigmoid output, and the QDNN, highlighting the quantum circuit layers and measurement process.

Data Complexity Guiding CDNN versus QDNN Selection necessitates

The team meticulously controlled learning problem complexity by generating datasets from either a single underlying function, termed ‘1 function data’, or0.8745 to 0.8116 when reducing the number of training pairs from 500 to 50.

Quantum Qualifier Predicts Advantage in Hadronic Physics

Results demonstrate that the quantum qualifier effectively predicts the optimal model choice based on these intrinsic data characteristics, paving the way for more efficient and accurate data analysis. Measurements confirm that the quantum qualifier successfully identified kinematic regimes favourable to quantum models in this complex hadronic physics scenario. The QDNN architecture comprises data encoding, parameterized unitary layers, entangling operations, and final measurement, leveraging the exponential structure of quantum state space, a system of n qubits spans a Hilbert space of dimension 2ⁿ. This expressive power allows for compact representations of complex transformations and facilitates the modelling of nontrivial correlations. Tests prove that QDNNs can exploit quantum entanglement and superposition to provide natural mechanisms for parallel computational pathways, potentially offering benefits for feature extraction and kernel-based learning in high-dimensional settings. The study meticulously evaluated both classical and quantum classifiers, establishing robust evaluation tools essential for accurate information extraction in hadronic physics, and ultimately delivering a systematic method for leveraging the strengths of quantum machine learning.

QDNN Advantage Linked to Data Uncertainty is increasingly

The findings reveal a systematic relationship between experimental uncertainty and model performance, showing that increased noise levels shift the task towards regimes favouring QDNNs. Specifically, the qualifier accurately maps out kinematic spaces where QDNNs demonstrate superior performance, identifying regions of advantage at larger xB and lower Q2, particularly under conditions of higher noise. The authors acknowledge limitations related to the specific datasets and kinematic regions explored, but they suggest future work could extend the framework to other hadronic physics problems and explore the impact of different network architectures.