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Tag: Intelligent Computing

  • Quantum-classical partnership enhances performance in parallel hybrid network.

    Quantum-classical partnership enhances performance in parallel hybrid network.

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    Newswise — Building efficient quantum neural networks is a promising direction for research at the intersection of quantum computing and machine learning. A team at Terra Quantum AG designed a parallel hybrid quantum neural network and demonstrated that their model is “a powerful tool for quantum machine learning.” This research was published Oct. 9 in Intelligent Computing, a Science Partner Journal.

    Hybrid quantum neural networks typically consist of both a quantum layer — a variational quantum circuit — and a classical layer — a deep learning neural network called a multi-layered perceptron. This special architecture enables them to learn complicated patterns and relationships from data inputs more easily than traditional machine learning methods.

    In this paper, the authors focus on parallel hybrid quantum neural networks. In such networks, the quantum layer and the classical layer process the same input at the same time and then produce a joint output — a linear combination of the outputs from both layers. A parallel network could avoid the information bottleneck that often affects sequential networks, where the quantum layer and the classical layer feed data into each other and process data alternately.

    The training results demonstrate that the authors’ parallel hybrid network can outperform either its quantum layer or its classical layer. Trained on two periodic datasets with high-frequency noise added, the hybrid model shows lower training loss, produces better predictions, and is found to be more adaptable to complex problems and new datasets.

    The quantum and classical layers both contribute to this effective quantum-classical interplay. The quantum layer, specifically, a variational quantum circuit, maps the smooth periodical parts, while the classical multi-layered perceptron fills in the irregular additions of noise. Both variational quantum circuits and multi-layered perceptrons are considered “universal approximators.” To maximize output during training, variational quantum circuits adjust the parameters of quantum gates that control the status of qubits, and multi-layered perceptrons mainly tune the strength of the connections, or so-called weights, between neurons.

    At the same time, the success of a parallel hybrid network rides on the setting and tuning of the learning rate and other hyperparameters, such as the number of layers and number of neurons in each layer in the multi-layered perceptron.

    Given that the quantum and classical layers learn at different speeds, the authors discussed how the contribution ratio of each layer affects the performance of the hybrid model and found that adjusting the learning rate is important in keeping a balanced contribution ratio. Therefore, they point out that building a custom learning rate scheduler is a future research direction because such a scheduler could enhance the speed and performance of the hybrid model.

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  • Intelligent programmable meta-imagers: A timely approach to task-specific, noise-adaptive sensing

    Intelligent programmable meta-imagers: A timely approach to task-specific, noise-adaptive sensing

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    Newswise — Sensing systems are becoming prevalent in many areas of our lives, such as in ambient-assisted health care, autonomous vehicles, and touchless human-computer interaction. However, these systems often lack intelligence: they tend to gather all available information, even if it is not relevant. This can lead not only to privacy infringements but also to wasted time, energy, and computational resources during data processing.

    To address this problem, researchers from the French CNRS came up with a concept for intelligent electromagnetic sensing, which uses machine-learning techniques to generate learned illumination patterns so as to pre-select relevant details during the measurement process. A programmable metasurface is configured to generate the learned patterns, performing high-accuracy sensing (e.g., posture recognition) with a remarkably reduced number of measurements (see References).

    But measurement processes in realistic applications are inevitably subject to a variety of noise. Noise fundamentally accompanies any measurement. The signal-to-noise ratio can be particularly low in indoor environments where the radiated electromagnetic signals must be kept weak. Therefore, Chenqi Qian and Philipp del Hougne now furthered the previous research and presented an intelligent programmable computational meta-imager that not only tailors its illumination pattern to a specific information-extraction task like object recognition but also adapts to different types and levels of noise. They published an invited research article on their results in Intelligent Computing on Dec. 2, 2022.

    “We hypothesize that the optimal coherent illumination patterns to be used by an intelligent programmable meta-imager to efficiently extract task-specific information from a scene will profoundly depend on the type and level of noise,” the researchers motivated their work, pointing out that noise may profoundly impact the optimal meta-imager configurations because, besides latency constraints which limits the number of allowed measurements, noise also limits the amount of information that can be extracted from the scene.

    “In this paper, we systematically explore how the combination of latency constraints and noise impacts intelligent multi-shot programmable meta-imagers,” the researchers said. To evaluate their hypothesis, the researchers considered a prototypical object-recognition problem, for which they proposed a microwave computational programmable meta-imager system. Such systems could be deployed in indoor surveillance, earth observation, etc.

    In their considered system, one microwave dynamic metasurface antenna (DMA) radiated a sequence of coherent wavefronts to the scene using a single transmitter, and a second DMA coherently captures the reflected waves using a single detector. A differentiable end-to-end information-flow pipeline was formulated, which comprised the programmable physical measurement process including noise as well as the subsequent digital processing layers.

    The essential elements of this pipeline are the same for all wave-based information-extraction problems, including imaging, sensing, localization, and object recognition. “The only significant difference lies in the task-specific cost function that is to be optimized for good performance,” they explained. The same approach that the authors applied to object recognition can hence also be used in parameter-estimation problems such as localization. “This pipeline allows us to jointly inverse-design the programmable physical weights (DMA configurations that determine the coherent scene illuminations) and the trainable digital weights.”

    It is this joint optimization — task-specific end-to-end joint optimization of the trainable physical parameters and trainable digital parameters — that endows the measurement process with task awareness, such that it could discriminate between task-relevant and task-irrelevant information over the air in the analog domain.

    The researchers tested the performance of this programmable meta-imager that generates a sequence of task-specific and noise-specific scene illuminations and found it advantageous over conventional compressed sensing with random configurations when the information that can be extracted from the scene is limited through latency constraints and/or noise. The performance gains for a signal-independent and a signal-dependent additive noise type were both demonstrated. The “macroscopic” features of the learned illumination patterns, namely, their mutual overlaps and intensities, were found to be intuitively understandable despite the “black-box” nature of the approach.

    According to the researchers, the transition toward a system that self-adaptively detects the type and level of noise and updates accordingly its utilized sequence of DMA configurations without additional human input is straightforward. “We faithfully expect that our results can be transposed to information-extraction problems based on other wave phenomena (e.g., optics, acoustics, elastics, and quantum mechanics) and/or with other types of in-situ programmable measurement hardware,” they concluded.

     

    References:

    [1] C. Saigre-Tardif, R. Faqiri, H. Zhao, L. Li, and P. del Hougne, “Intelligent meta-imagers: From compressed to learned sensing,”Applied Physics Reviews, vol. 9, no. 1, p. 011314, 2022.

    [2] P. del Hougne, M. F. Imani, A. V. Diebold, R. Horstmeyer, and D. R. Smith, “Learned Integrated Sensing Pipeline: Reconfigurable Metasurface Transceivers as Trainable Physical Layer in an Artificial Neural Network,”Advanced Science, vol. 7, no. 3, p. 1901913, 2019.

    [3] H.-Y. Li, H.-T. Zhao, M.-L. Wei et al., “Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing,”Patterns, vol. 1, no. 1, p. 100006, 2020.

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