We report on a gate-based variational quantum classifier implemented with single photons and probabilistic gates, to emulate the standard quantum circuit model framework. We evaluate the expressive power of two deployable quantum neural networks (QNNs) by computing their effective dimension, a capacity measure grounded in a proven generalization-error bound, and compare them with classical artificial neural networks (ANNs) of equivalent trainable-parameter count. Supervised binary classification tasks are used to benchmark performance across photonic and superconducting QNNs, both of which exhibit superior converged (lower) cross-entropy loss and (higher) prediction accuracy relative to matched-parameter ANNs. For a nonlinearly separable task, our photonic QNN with a single pair of trainable parameters successfully converged (loss 0.04 and accuracy 100%), whereas the equivalent ANN failed to learn the decision boundary, saturating at random-guessing performance. We simulate photonic quantum circuits, training them on the XOR problem and a two-class Iris subset using gradient-free optimization, and assess their robustness to sampling errors under realistic noise processes including photon loss and phase-shifter imperfections. Circuits with comparatively high effective dimension were deployed remotely on a six-qubit photonic quantum processor, achieving classification accuracies of up to 100% in both online and offline learning settings. Notably, even the simplest QNN deployed, with just two trainable parameters, successfully solved tasks that classically require ANNs with at least quadruple the number of parameters, suggesting an emergent algorithmic advantage. Overall, these results demonstrate a clear proof-of-principle that gate-based QNNs can be realized and trained effectively on current photonic hardware, providing proof of algorithmic advantage on a gate-based photonic QNN.We report on a gate-based variational quantum classifier implemented with single photons and probabilistic gates, to emulate the standard quantum circuit model framework. We evaluate the expressive power of two deployable quantum neural networks (QNNs) by computing their effective dimension, a capacity measure grounded in a proven generalization-error bound, and compare them with classical artificial neural networks (ANNs) of equivalent trainable-parameter count. Supervised binary classification tasks are used to benchmark performance across photonic and superconducting QNNs, both of which exhibit superior converged (lower) cross-entropy loss and (higher) prediction accuracy relative to matched-parameter ANNs. For a nonlinearly separable task, our photonic QNN with a single pair of trainable parameters successfully converged (loss 0.04 and accuracy 100%), whereas the equivalent ANN failed to learn the decision boundary, saturating at random-guessing performance. We simulate photonic quantum circuits, training them on the XOR problem and a two-class Iris subset using gradient-free optimization, and assess their robustness to sampling errors under realistic noise processes including photon loss and phase-shifter imperfections. Circuits with comparatively high effective dimension were deployed remotely on a six-qubit photonic quantum processor, achieving classification accuracies of up to 100% in both online and offline learning settings. Notably, even the simplest QNN deployed, with just two trainable parameters, successfully solved tasks that classically require ANNs with at least quadruple the number of parameters, suggesting an emergent algorithmic advantage. Overall, these results demonstrate a clear proof-of-principle that gate-based QNNs can be realized and trained effectively on current photonic hardware, providing proof of algorithmic advantage on a gate-based photonic QNN.

quant-pharXiv:2605.10801May 12, 20260 citations

Quantum Neural Networks: Paving the Way for Next-Generation Machine Learning

Bheema Shanker Neyigapula

Quantum Neural Networks (QNNs) have emerged as a promising avenue in the quest for next-generation machine learning capabilities.By harnessing the principles of quantum mechanics and quantum computing, QNNs offer the potential for exponential speedup and enhanced computational power compared to classical machine learning algorithms.This paper presents a comprehensive exploration of Quantum Neural Networks, covering their foundational concepts, various architectures, novel quantum-enhanced machine learning algorithms, practical implementations, advantages, and challenges.We begin with an overview of related work in Quantum Machine Learning, highlighting the achievements and progress in the field.Subsequently, we delve into the fundamental principles of Quantum Mechanics and Quantum Computing to provide a solid grounding for understanding QNNs.We then introduce the basics of Quantum Neural Networks, including quantum neurons, quantum gates, and quantum circuits for machine learning tasks.Various QNN architectures, such as Variational Quantum Circuits and Quantum Convolutional Neural Networks, are explored along with their potential applications.Additionally, quantum-enhanced machine learning algorithms, like Quantum Support Vector Machines and Quantum Generative Adversarial Networks, are discussed for a deeper understanding of QNN capabilities.Practical aspects, including hardware implementations, noise mitigation, and experimental results, are presented to shed light on the feasibility of QNNs in real-world scenarios.The paper concludes by discussing the advantages and challenges of Quantum Neural Networks and their potential implications for the future of machine learning and artificial intelligence.Quantum Neural Networks (QNNs) have emerged as a promising avenue in the quest for next-generation machine learning capabilities.By harnessing the principles of quantum mechanics and quantum computing, QNNs offer the potential for exponential speedup and enhanced computational power compared to classical machine learning algorithms.This paper presents a comprehensive exploration of Quantum Neural Networks, covering their foundational concepts, various architectures, novel quantum-enhanced machine learning algorithms, practical implementations, advantages, and challenges.We begin with an overview of related work in Quantum Machine Learning, highlighting the achievements and progress in the field.Subsequently, we delve into the fundamental principles of Quantum Mechanics and Quantum Computing to provide a solid grounding for understanding QNNs.We then introduce the basics of Quantum Neural Networks, including quantum neurons, quantum gates, and quantum circuits for machine learning tasks.Various QNN architectures, such as Variational Quantum Circuits and Quantum Convolutional Neural Networks, are explored along with their potential applications.Additionally, quantum-enhanced machine learning algorithms, like Quantum Support Vector Machines and Quantum Generative Adversarial Networks, are discussed for a deeper understanding of QNN capabilities.Practical aspects, including hardware implementations, noise mitigation, and experimental results, are presented to shed light on the feasibility of QNNs in real-world scenarios.The paper concludes by discussing the advantages and challenges of Quantum Neural Networks and their potential implications for the future of machine learning and artificial intelligence.

International Journal of Artificial Intelligence and Machine LearningJul 5, 20241 citation

Quantum Neural Networks and Quantum Intelligence

M. V. Altaĭsky, N. E. Kaputkina

Studies in Rhythm EngineeringOct 29, 20200 citations

Investigating Quantum Artificial Neural Networks for Singularity Avoidance in Robotic Manipulators

Mehdi Fazilat, Nadjet Zioui

This research explores the application of quantum-inspired neural networks (QNNs) to address the inverse kinematics problem in robotic arms, explicitly focusing on the ABB IRB140, an articulated robot arm with six degrees of freedom. The primary objective is to develop a quantum-inspired activation function for multilayer perceptron (MLP) neural networks. The study evaluates their performance in avoiding singularities by comparing artificial neural networks (ANNs) with quantum neural networks (QNNs). The findings demonstrate that QNNs outperform ANNs in terms of mean absolute error (MAE), achieving a 15.60% lower MAE in the model without singularities and a 16.67% reduction in the Jacobian-based MAE in the model designed to avoid singularities. The study demonstrates that QNNs offer superior accuracy in predicting the robot arm's inverse kinematics, achieving a position error of 1.64 mm and an orientation error of 0.00179 radians while effectively avoiding singularities. These outcomes underscore the potential of quantum-inspired neural networks to enhance robotic arm manipulations' precision, efficiency, and performance.This research explores the application of quantum-inspired neural networks (QNNs) to address the inverse kinematics problem in robotic arms, explicitly focusing on the ABB IRB140, an articulated robot arm with six degrees of freedom. The primary objective is to develop a quantum-inspired activation function for multilayer perceptron (MLP) neural networks. The study evaluates their performance in avoiding singularities by comparing artificial neural networks (ANNs) with quantum neural networks (QNNs). The findings demonstrate that QNNs outperform ANNs in terms of mean absolute error (MAE), achieving a 15.60% lower MAE in the model without singularities and a 16.67% reduction in the Jacobian-based MAE in the model designed to avoid singularities. The study demonstrates that QNNs offer superior accuracy in predicting the robot arm's inverse kinematics, achieving a position error of 1.64 mm and an orientation error of 0.00179 radians while effectively avoiding singularities. These outcomes underscore the potential of quantum-inspired neural networks to enhance robotic arm manipulations' precision, efficiency, and performance.

International Conference on Systems and Control (ICSC)Nov 3, 202411 citations

Quantum-Enhanced Neural Contextual Bandit Algorithms

Yuqi Huang, Vincent Y. F Tan, Sharu Theresa Jose

Stochastic contextual bandits are fundamental for sequential decision-making but pose significant challenges for existing neural network-based algorithms, particularly when scaling to quantum neural networks (QNNs) due to issues such as massive over-parameterization, computational instability, and the barren plateau phenomenon. This paper introduces the Quantum Neural Tangent Kernel-Upper Confidence Bound (QNTK-UCB) algorithm, a novel algorithm that leverages the Quantum Neural Tangent Kernel (QNTK) to address these limitations. By freezing the QNN at a random initialization and utilizing its static QNTK as a kernel for ridge regression, QNTK-UCB bypasses the unstable training dynamics inherent in explicit parameterized quantum circuit training while fully exploiting the unique quantum inductive bias. For a time horizon $T$ and $K$ actions, our theoretical analysis reveals a significantly improved parameter scaling of $Ω((TK)^3)$ for QNTK-UCB, a substantial reduction compared to $Ω((TK)^8)$ required by classical NeuralUCB algorithms for similar regret guarantees. Empirical evaluations on non-linear synthetic benchmarks and quantum-native variational quantum eigensolver tasks demonstrate QNTK-UCB's superior sample efficiency in low-data regimes. This work highlights how the inherent properties of QNTK provide implicit regularization and a sharper spectral decay, paving the way for achieving ``quantum advantage'' in online learning.Stochastic contextual bandits are fundamental for sequential decision-making but pose significant challenges for existing neural network-based algorithms, particularly when scaling to quantum neural networks (QNNs) due to issues such as massive over-parameterization, computational instability, and the barren plateau phenomenon. This paper introduces the Quantum Neural Tangent Kernel-Upper Confidence Bound (QNTK-UCB) algorithm, a novel algorithm that leverages the Quantum Neural Tangent Kernel (QNTK) to address these limitations. By freezing the QNN at a random initialization and utilizing its static QNTK as a kernel for ridge regression, QNTK-UCB bypasses the unstable training dynamics inherent in explicit parameterized quantum circuit training while fully exploiting the unique quantum inductive bias. For a time horizon $T$ and $K$ actions, our theoretical analysis reveals a significantly improved parameter scaling of $Ω((TK)^3)$ for QNTK-UCB, a substantial reduction compared to $Ω((TK)^8)$ required by classical NeuralUCB algorithms for similar regret guarantees. Empirical evaluations on non-linear synthetic benchmarks and quantum-native variational quantum eigensolver tasks demonstrate QNTK-UCB's superior sample efficiency in low-data regimes. This work highlights how the inherent properties of QNTK provide implicit regularization and a sharper spectral decay, paving the way for achieving ``quantum advantage'' in online learning.

cs.LGCross-listedarXiv:2601.02870Jan 7, 20260 citations

T

and

K

actions, our theoretical analysis reveals a significantly improved parameter scaling of

Ω((TK)^3)

for QNTK-UCB, a substantial reduction compared to

Ω((TK)^8)

required by classical NeuralUCB algorithms for similar regret guarantees. Empirical evaluations on non-linear synthetic benchmarks and quantum-native variational quantum eigensolver tasks demonstrate QNTK-UCB's superior sample efficiency in low-data regimes. This work highlights how the inherent properties of QNTK provide implicit regularization and a sharper spectral decay, paving the way for achieving ``quantum advantage'' in online learning.