Artificial intelligence (AI) and Quantum Computing are two rapidly advancing technological areas, which can present new opportunities for designing intelligent systems with the ability to perform adaptive learning, especially when considering real-time applications that require significant computational capacity. With the advent of hybrid quantum–classical deep learning models: a completely new paradigm that combines well performing classical neural networks with computational advantages given by quantum mechanics phenomena such as superposition, entanglement and parallelism into one model type—models strong on both supervised a unsupervised tasks. Abstract here, we address architectural design, optimization methods and real-time implementation of hybrid quantum classical systems. Read more...

Quantum computing has the potential to revolutionize modern computation and solve problems that classical computing systems are unable of solving, at least within a reasonable time frame. Nevertheless, for current quantum hardware whose performance and reliability are heavily impacted by noise. The subject of this research is the use of machine learning algorithms with quantum circuits to alleviate noise and enhance computational fidelity. Using data driven models, the proposed method detects patterns of quantum errors and adjusts circuit structures, gate sequences, and quit mappings accordingly. These include supervised, reinforcement, and deep learning techniques in improving circuit accuracy as well as error rates. It proposes a hybrid optimization framework that combines classical ML algorithms with quantum execution and provides feedback mechanisms for continuous sampling of improved performance. Read more...

Quantum kernel methods have become a viable option for high-dimensional data classification with the computation power of quantum systems. Fundamentally, classical kernel methods (e.g., support vector machines) have been shown to be effective by mapping data into higher-dimensional, explicitly defined feature spaces where linear reparability is achieved. But when data dimensionality becomes large, classical methods encounter issues of computational feasibility and scalability. Addressing these challenges, quantum kernel methods involve mapping the data onto quantum feature spaces by encoding them in terms of quantum states processed on quantum circuits. This allows for efficient representation and manipulation of complicated data distributions. In this paper, we study the theory, practice and advantages of quantum kernel methods for high-dimensional classification. Read more...

Quantum Machine Learning (QML) is a new discipline that combines quantum computing and machine learning algorithms in order to take advantage of the computational benefits which are provided by quantum computing for performing complex problems. While similar to classical machine learning systems, quantum-models tend to be prone for adversarial attacks—craft perturbations designed purposely for causing model mispredictions. The intrinsic noise and probabilistic behavior of quantum systems— in particular the (Noisy Intermediate-Scale Quantum) NISQ—is only worsening these vulnerabilities. This paper provides an overview of adversarial threats in QML and illustrates how the quantum nature of any specific system to be attacked shapes both attack and defence strategies. We study on prevalent adversarial methods including gradient based perturbations, optimization attacks and transfer attacks among others, for their resonance in the quantum settings.Read more...

Generative AI is one of the modern humankind's most transformative fields, paving a path for machines to generate realistic information in many forms (e.g., images/text/audio/video/scientific data). Diffusion models are the system that recently showed how to learn to generate them step by step by reversing stochastic noise processes through normalisation layers. Such models outperformed previous techniques including Generative Adversarial Networks (GANs) in terms of stability, diversity and controllability. While, despite those successes, classical diffusion models have major limitations such as expensive computations, slow sampling times or great training pipeline requirements and some challenges when modelling ultra-high-dimensional probability distributions. Emerging computational paradigms to accelerate and enhance generative intelligence systems have come when data complexity is becoming more challenging in domains like healthcare, finance, cybersecurity, climate science, industrial automation.Read more...