Hybrid quantum ResNet for car classification and its hyperparameter optimization

Sagingalieva, Asel; Kordzanganeh, Mo; Kurkin, Andrii; Melnikov, Artem; Kuhmistrov, Daniil; Perelshtein, Michael; Melnikov, Alexey; Skolik, Andrea; Dollen, David Von

doi:10.1007/s42484-023-00123-2

Cited by 9 publications

(4 citation statements)

References 62 publications

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“…The QNN architecture we have characterized is the first model of its kind ever proposed [6]. This design is the cornerstone over which rapidly growing and vibrant research is being carried out [22]- [25], [41]- [43]. In particular, the structure of the Hardware Efficient Ansatz we have deeply investigated is being used to implement quanvolution in the vast majority of QNNs models.…”

Section: Discussion and Projectionsmentioning

confidence: 99%

“…The sequence of these elements produces an output tensor of size comparable to a classical convolution and pooling operator on 2 × 2 subgrids with a stride of 2. The qLayer is not a direct quantum translation of the convolution operation for CNNs, but rather it is the standard quantum dual of a convolution kernel for QNNs, as per the works of [22]- [25], [41]- [43]. Each of the 4 qubits calculates one of the 4 channels of the feature map.…”

Section: A Quanvolutional Layermentioning

confidence: 99%

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Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

Vallero,

Dri,

Giusto

et al. 2024

IEEE Trans. Quantum Eng.

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Quanvolutional Neural Networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of the traditional convolution. Unfortunately, the qubit's reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this paper we aim at investigating the propagation of logical-shift errors (i.e. the unexpected modification of the qubit state) in QNNs. We propose a bottom-up evaluation reporting data from 13, 322, 547, 200 logical-shift injections. We characterize the error propagation in the quantum circuit implementing a single convolution and then in various designs of the same QNN, varying the dataset and the network depth. We track the logicalshift error propagation through the qubits, channels, and subgrids identifying the faults that are more likely to cause misclassifications. We found that up to 10% of the injections in the quanvolutional layer cause misclassification and even logical-shifts of small magnitude can be sufficient to disturb the network functionality. Our detailed analysis shows that corruptions in the qubits' state that alter their probability amplitude are more critical than the ones altering their phase, that some object classes are more likely than others to be corrupted, that the criticality of subgrids depends on the dataset, and that the control qubits, once corrupted, are more likely to modify the QNN output than the target qubits.

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Section: Discussion and Projectionsmentioning

confidence: 99%

Section: A Quanvolutional Layermentioning

confidence: 99%

Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

Vallero,

Dri,

Giusto

et al. 2024

IEEE Trans. Quantum Eng.

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“…In the context of image classification, QML algorithms can process large datasets of images more efficiently than classical algorithms, leading to faster and more accurate classification [24]. Recent studies have also explored hybrid quantum-classical CNNs and demonstrated the classification [18,[25][26][27][28][29][30] and generation [31][32][33][34] of images.…”

Section: Introductionmentioning

confidence: 99%

Quantum machine learning for image classification

Senokosov,

Sedykh,

Sagingalieva

et al. 2024

Mach. Learn.: Sci. Technol.

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Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two Quantum Machine Learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a Hybrid Quantum Neural Network with parallel quantum circuits, enables the execution of computations even in the Noisy Intermediate-Scale Quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum-classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a Hybrid Quantum Neural Network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.

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“…Quantum computing models can improve the learning process of existing classical models [18][19][20][21][22][23][24], allowing for better target function prediction accuracy with fewer iterations [25]. In many industries, including the pharmaceutical [26,27], aerospace [28], automotive [29], logistics [30] and financial [31][32][33][34][35] sector quantum technologies can provide unique advantages over classical computing. Many traditionally important machine learning domains are also getting potential benefits from utilizing quantum technologies, e.g.…”

Section: Introductionmentioning

confidence: 99%

Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes

Sedykh,

Podapaka,

Sagingalieva

et al. 2024

Mach. Learn.: Sci. Technol.

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Finding the distribution of the velocities and pressures of a fluid by solving the Navier-Stokes equations is a principal task in the chemical, energy, and pharmaceutical industries, as well as in mechanical engineering and the design of pipeline systems. With existing solvers, such as OpenFOAM and Ansys, simulations of fluid dynamics in intricate geometries are computationally expensive and require re-simulation whenever the geometric parameters or the initial and boundary conditions are altered. Physics-informed neural networks are a promising tool for simulating fluid flows in complex geometries, as they can adapt to changes in the geometry and mesh definitions, allowing for generalization across fluid parameters and transfer learning across different shapes. We present a hybrid quantum physics-informed neural network that simulates laminar fluid flows in 3D $Y$-shaped mixers. Our approach combines the expressive power of a quantum model with the flexibility of a physics-informed neural network, resulting in a 21\% higher accuracy compared to a purely classical neural network. Our findings highlight the potential of machine learning approaches, and in particular hybrid quantum physics-informed neural network, for complex shape optimization tasks in computational fluid dynamics. By improving the accuracy of fluid simulations in complex geometries, our research using hybrid quantum models contributes to the development of more efficient and reliable fluid dynamics solvers.

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Hybrid quantum ResNet for car classification and its hyperparameter optimization

Cited by 9 publications

References 62 publications

Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

Quantum machine learning for image classification

Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes

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