This paper presents a modular hardware / software architecture suited to scalable, configurable quantum processors based upon room-temperature quantum materials and control devices. The principle of operation and salient features of the quantum materials and devices are discussed. Further, we propose to apply this architecture for implementation in a quantum probabilistic learning machine as well as a quantum probabilistic decision engine, both of which are well suited to learning unknown correlations in uncertain or stochastic dynamic processes/systems from operational data and support decision-making under uncertainty. As an example, case studies of quantum algorithms are presented to illustrate the modular software design options and demonstrate the potential of moving quantum algorithms to quantum processors. A plan for future work is also identified.
This paper presents a modular hardware / software architecture suited to scalable, configurable quantum processors based upon room-temperature quantum materials and control devices. The principle of operation and salient features of the quantum materials and devices are discussed. Further, we propose to apply this architecture for implementation in a quantum probabilistic learning machine as well as a quantum probabilistic decision engine, both of which are well suited to learning unknown correlations in uncertain or stochastic dynamic processes/systems from operational data and support decision-making under uncertainty. As an example, case studies of quantum algorithms are presented to illustrate the modular software design options and demonstrate the potential of moving quantum algorithms to quantum processors. A plan for future work is also identified.
This paper addresses the critical limitations of existing quantum machine learning (QML) frameworks concerning scalability, specifically their inability to efficiently handle large-scale quantum datasets. We propose a novel, scalable QML framework designed to effectively process these datasets and accelerate the training and inference of quantum machine learning algorithms. The core mechanism leverages quantum parallelism, constructing a framework capable of handling increased complexity. This work represents a significant advancement by directly tackling the scalability bottleneck in QML, establishing a foundational element for future developments in the field. The framework utilizes a modular architecture incorporating optimized quantum circuits and classical processing units for data preparation, model training, and result evaluation. The key innovation lies in the dynamic partitioning of quantum computations across multiple quantum processors, coupled with sophisticated data encoding strategies to mitigate the effects of qubit decoherence and limited qubit counts. This approach allows for the parallel execution of complex quantum algorithms, drastically reducing computational overhead and enabling the training of larger and more sophisticated models. We demonstrate the potential of this framework through theoretical analysis and algorithmic design, highlighting its advantages over traditional, monolithic QML implementations.
This paper presents a novel Quantum Probabilistic Learning Machine (QuProbLeM) paradigm that is fully based on Bayesian inference and well suited to directly learning the patterns or correlations inherent in dynamic processes from operation data by using a probabilistic neural network. In the QuProbLeM, the respective probability distributions of the weights capturing the input-to-output relationships are learnable parameters that can be learned from data samples. This revolutionary machine learning (ML) paradigm is inspired by the philosophy of Parts and the Total (e.g., each part, or data sample, in a closed system affects the total in such a way that the whole system will thereafter influence every single part). This first-of-the-type probabilistic learning machine can be effectively implemented on a quantum computing system that may also share the same philosophy naturally. The proposed QuProbLeM solver can take as its input a set of quantum-encoded data samples comprising pairs of cause-effect variables, and its output may include the updated probability distributions of the model's parameters (i.e., weights) and the predicted effect (output) variables and their distributions when given new cause (input) variables or their distributions. Alternatively, classical data can be first fed into a front-end Bayesian learning network to initially learn the probability distributions of the cause-effect variable pairs and then encoded into coherent quantum states as the input. Uniquely, the QuProbLeM solver directly computes probability distributions of the learning network's weights in an inference-based, entangled manner (but not iteratively as in traditional ML), by synthesizing the quantum training datasets, inferring about the model fidelity and updating the weight's probabilities based on inverse quantum operations.
Quantum Machine Learning (QML) represents a transformative intersection of quantum computing and artificial intelligence, promising exponential speed-ups and new capabilities for data-driven tasks. However, the practical realization of QML is hindered by the current limitations of quantum hardware, including scalability constraints, qubit decoherence, and high error rates. Hybrid quantum-classical architectures have emerged as a pragmatic approach, leveraging classical resources alongside quantum processors to mitigate these hardware challenges. A promising direction for advancing QML is the integration of hybrid architectures into cloud computing platforms and mobile-edge environments. This approach enables scalable access to quantum resources, facilitates real-time data processing, and brings quantum-enhanced intelligence closer to end-users and devices. This article reviews the state-of-the-art in QML, focusing on how hybrid models and quantum algorithms are addressing the dual challenges of hardware scalability and architectural integration within cloud and mobile-edge computing contexts. Recent advances are analyzed, benchmark results are highlighted, and future directions for deploying robust, scalable QML systems in distributed computing environments are discussed.
We introduce Extreme Quantum Cognition Machines, a class of quantum learning architectures for deliberative decision making designed to operate under coarse-grained, ambiguous, and potentially contradictory information. Inspired by the quantum cognition paradigm, Extreme Quantum Cognition Machines are closely related to quantum extreme learning and quantum reservoir computing, where fixed quantum dynamics generates a nonlinear feature map and learning is confined to a linear readout. A dynamical attention mechanism, implemented through an input-dependent interaction term in the Hamiltonian, modulates the quantum evolution and biases the resulting feature embedding toward task-relevant correlations. The approach is validated on linguistic classification tasks, which serve as paradigmatic examples of deliberative inference. Hardware-compatible quantum implementations of the proposed framework are discussed, together with potential applications in symbolic inference, sequence analysis, anomaly detection, and automatic diagnosis, with direct relevance to domains such as biology, forensics, and cybersecurity.
Quantum Machine Learning (QML) holds immense promise for revolutionizing various fields, particularly in complex data analysis and optimization. Traditional machine learning approaches often struggle to effectively handle data variations and complex relationships. This paper introduces an Adaptive Architecture for Quantum Machine Learning (A-QML) – a novel algorithm framework designed to dynamically adjust quantum model parameters and structures based on the input data's characteristics. The core mechanism centers around leveraging quantum machine learning's inherent adaptability to improve model generalization and efficiency. We present a detailed explanation of the architecture, its operational principles, and preliminary results demonstrating its effectiveness in a specific, yet illustrative, QML task. The paper concludes with a discussion of future research directions and potential applications.
Quantum Machine Learning (QML) is rapidly emerging as a frontier field, combining quantum computing and artificial intelligence to address fundamental challenges in learning scalability and adaptability. This paper explores how the principles of quantum mechanics can be harnessed-particularly through variational quantum circuits (VQCs)-to design efficient QML architectures suitable for noisy intermediate-scale quantum (NISQ) devices. We present recent advancements including Quantum Federated Learning (QFL) for privacy-preserving distributed intelligence, Quantum Long Short-Term Memory (QL-STM) networks for sequential pattern modeling, and Quantum Reinforcement Learning (QRL) for dynamic decision-making. Furthermore, we introduce the Quantum Fast Weight Programmer (QFWP), a mechanism for rapid, context-driven parameter adaptation to support meta-learning in quantum systems. We also discuss differentiable quantum architecture search (DiffQAS) as a means of automatically optimizing quantum circuit structures. Despite limitations in quantum hardware-such as decoherence, noise, and limited qubit counts-we review mitigation strategies including hybrid quantum-classical training, error-aware optimization, and scalable architectural design. This work outlines a forward-looking framework for building scalable, adaptive, and trustworthy Quantum AI systems.