Abstract Machine learning has been presented as one of the key applications for near-term quantum technologies, given its high commercial value and wide range of applicability. In this work, we introduce the quantum-assisted Helmholtz machine: a hybrid quantum–classical framework with the potential of tackling high-dimensional real-world machine learning datasets on continuous variables. Instead of using quantum computers only to assist deep learning, as previous approaches have suggested, we use deep learning to extract a low-dimensional binary representation of data, suitable for processing on relatively small quantum computers. Then, the quantum hardware and deep learning architecture work together to train an unsupervised generative model. We demonstrate this concept using 1644 quantum bits of a D-Wave 2000Q quantum device to model a sub-sampled version of the MNIST handwritten digit dataset with 16 × 16 continuous valued pixels. Although we illustrate this concept on a quantum annealer, adaptations to other quantum platforms, such as ion-trap technologies or superconducting gate-model architectures, could be explored within this flexible framework.
Abstract Machine learning has been presented as one of the key applications for near-term quantum technologies, given its high commercial value and wide range of applicability. In this work, we introduce the quantum-assisted Helmholtz machine: a hybrid quantum–classical framework with the potential of tackling high-dimensional real-world machine learning datasets on continuous variables. Instead of using quantum computers only to assist deep learning, as previous approaches have suggested, we use deep learning to extract a low-dimensional binary representation of data, suitable for processing on relatively small quantum computers. Then, the quantum hardware and deep learning architecture work together to train an unsupervised generative model. We demonstrate this concept using 1644 quantum bits of a D-Wave 2000Q quantum device to model a sub-sampled version of the MNIST handwritten digit dataset with 16 × 16 continuous valued pixels. Although we illustrate this concept on a quantum annealer, adaptations to other quantum platforms, such as ion-trap technologies or superconducting gate-model architectures, could be explored within this flexible framework.
Abstract With quantum computing technologies nearing the era of commercialization and quantum supremacy, machine learning (ML) appears as one of the promising ‘killer’ applications. Despite significant effort, there has been a disconnect between most quantum ML proposals, the needs of ML practitioners, and the capabilities of near-term quantum devices to demonstrate quantum enhancement in the near future. In this contribution to the focus collection ‘What would you do with 1000 qubits?’, we provide concrete examples of intractable ML tasks that could be enhanced with near-term devices. We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning techniques. We also highlight the case of classical datasets with potential quantum-like statistical correlations where quantum models could be more suitable. We focus on hybrid quantum–classical approaches and illustrate some of the key challenges we foresee for near-term implementations. Finally, we introduce the quantum-assisted Helmholtz machine (QAHM), an attempt to use near-term quantum devices to tackle high-dimensional datasets of continuous variables. Instead of using quantum computers to assist deep learning, as previous approaches do, the QAHM uses deep learning to extract a low-dimensional binary representation of data, suitable for relatively small quantum processors which can assist the training of an unsupervised generative model. Although we illustrate this concept on a quantum annealer, other quantum platforms could benefit as well from this hybrid quantum–classical framework.
The combination of quantum and classical computational resources towards more effective algorithms is one of the most promising research directions in computer science. In such a hybrid framework, existing quantum computers can be used to their fullest extent and for practical applications. Generative modeling is one of the applications that could benefit the most, either by speeding up the underlying sampling methods or by unlocking more general models. In this work, we design a number of hybrid generative models and validate them on real hardware and datasets. The quantum-assisted Boltzmann machine is trained to generate realistic artificial images on quantum annealers. Several challenges in state-of-the-art annealers shall be overcome before one can assess their actual performance. We attack some of the most pressing challenges such as the sparse qubit-to-qubit connectivity, the unknown effective-temperature, and the noise on the control parameters. In order to handle datasets of realistic size and complexity, we include latent variables and obtain a more general model called the quantum-assisted Helmholtz machine. In the context of gate-based computers, the quantum circuit Born machine is trained to encode a target probability distribution in the wavefunction of a set of qubits. We implement this model on a trapped ion computer using low-depth circuits and native gates. We use the generative modeling performance on the canonical Bars-and-Stripes dataset to design a benchmark for hybrid systems. It is reasonable to expect that quantum data, i.e., datasets of wavefunctions, will become available in the future. We derive a quantum generative adversarial network that works with quantum data. Here, two circuits are optimized in tandem: one tries to generate suitable quantum states, the other tries to distinguish between target and generated states.
Quantum machine learning is an emerging Technology that is evolving rapidly and aims to translate classical machine learning models into a quantum context to be extended with the properties and applications of quantum mechanics. Despite the obvious advantage of being able to process large amounts of data in a short time, this integration between quantum and machine learning is not without challenges, especially when it comes to real datasets. In this paper, we aim to analyze the practical applicability of quantum machine learning approaches and to perform different experiments on generated and dedicated datasets that respect the quantum kernel training pattern, respectively, on real datasets, and to determine whether these can be processed to be suitable for quantum operations. To fully see the performance and potential advantage brought by the quantum component, a series of tests will be performed on both simulators and real hardware and it remains to be seen whether there is an improvement related to time complexity. Another goal based on the experiments carried out is to see on which types of datasets it is worth or justifiable to use quantum hardware at the expense of classical one. The novelty of this work lies in the use of a real-world dataset, and the experiments demonstrate that integrating quantum methods with classical processing yields higher accuracy compared to purely classical approaches.
The new era of artificial intelligence (AI) aims to entangle the relationships among models (characterizations), algorithms, and implementations toward the high-level intelligence with general cognitive ability, strong robustness, and interpretability, which is intractable for machine learning (ML). Quantum computer provides a new computing paradigm for ML. Although universal quantum computers are still in infancy, special-purpose D-Wave machine hopefully becomes the breaking point of commercialized quantum computing. The core principle, quantum annealing (QA), enables the quantum system to naturally evolve toward the low-energy states. D-Wave's quantum computer has developed some applications of quantum ML based on quantum-assisted ML algorithms, quantum Boltzmann machine, etc. Additionally, working with CPUs, quantum processing units is likely to advance ML in a quantum-inspired way. Thus, a new advanced computing architecture, quantum-classical hybrid approach consisting of QA, classical computing, and brain-inspired cognitive science, is required to explore its superiority to universal quantum algorithms and classical ML algorithms. It is important to explore hybrid quantum/classical approaches to overcome the defects of ML such as high dependence on training data, low robustness to the noises, and cognitive impairment. The new framework is expected to gradually form a highly effective, accurate, and adaptive intelligent computing architecture for the next generation of AI.
Abstract This research presents a comprehensive hybrid quantum classical machine learning framework designed to overcome the computational limitations of classical algorithms when processing high dimensional data. The study integrates parameterized quantum circuits within classical neural network architectures and demonstrates their application across optimization tasks and supervised learning problems. The framework was evaluated on combinatorial optimization benchmarks including Max Cut and Job Shop Scheduling problems alongside classification tasks using quantum annealing based feature selection. Results demonstrate that hybrid quantum classical models achieve faster convergence rates and improved accuracy in high dimensional spaces compared to classical counterparts with the quantum algorithm for Max Cut optimization outperforming complete solution searches on larger problem instances. The findings provide machine learning practitioners and quantum computing researchers with validated hybrid architectures for near term quantum device deployment.
Abstract Quantum Machine Learning (QML) has emerged as a promising paradigm that leverages the principles of quantum mechanics—such as superposition and entanglement—to overcome the limitations of classical machine learning in handling complex, high-dimensional data. However, practical challenges such as limited coherence times of qubits and the integration of quantum circuits with classical architectures have hindered the full realization of QML’s potential. To address these challenges, this paper proposes a novel hybrid architecture, termed the Classical-Quantum Hybrid Convolutional Neural Network (CQHCNN), which integrates quantum-inspired layers into conventional deep learning frameworks. These quantum layers utilize quantum information processing to extract complex features, while the classical components perform feature extraction and classification. This paper evaluates the performance of our model on several benchmark datasets, including MNIST, Fashion-MNIST, CIFAR-10 and COVID-19 Radiography, demonstrating its ability to outperform some other Quantum Convolutional Neural Networks (QCNNs) in terms of many metrics. Experimental results indicate that the hybrid model achieves significant improvements in classification accuracy and computational efficiency, highlighting the potential of quantum-inspired techniques to enhance classical machine learning models.