Practical advantage of quantum machine learning in ghost imaging

Abstract

Demonstrating the practical advantage of quantum computation remains a long-standing challenge whereas quantum machine learning becomes a promising application that can be resorted to. In this work, we investigate the practical advantage of quantum machine learning in ghost imaging by overcoming the limitations of classical methods in blind object identification and imaging. We propose two hybrid quantum-classical machine learning algorithms and a physical-inspired patch strategy to allow distributed quantum learning with parallel variational circuits. In light of the algorithm, we conduct experiments for imaging-free object identification and blind ghost imaging under different physical sampling rates. We further quantitatively analyze the advantage through the lens of information geometry and generalization capability. The numerical results showcase that quantum machine learning can restore high-quality images but classical machine learning fails. The advantage of identification rate are up to 10% via fair comparison with the classical machine learning methods. Our work explores a physics-related application capable of practical quantum advantage, which highlights the prospect of quantum computation in the machine learning field.

Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits

“…In contrast, our study involved preprocessing to extract brain morphometry features, and the limited number of qubits required for VQCs hindered us from training the model using all the features, potentially leading to information loss. To address these challenges, hybrid approaches that combine classical and quantum machine learning [25,26] and employ techniques such as quanvolutional neural networks [37,38] or data reuploading [39] could potentially yield better results. In addition, our study did not demonstrate the clinical utility of age prediction and gender classification, which may require disease-specific or atypical data.…”

“…Quantum machine learning has emerged as a promising tool to enhance classical machine learning techniques [24]. Research indicates that both quantum and quantuminspired computing models have the potential to optimize the training process of conventional models, resulting in improved prediction accuracy for target functions with reduced iteration requirements [25,26]. Several studies have highlighted the practical advantages of quantum machine learning algorithms, demonstrating their superior performance over classical counterparts in predicting complex medical outcomes [25] and image restoration [26].…”

“…Research indicates that both quantum and quantuminspired computing models have the potential to optimize the training process of conventional models, resulting in improved prediction accuracy for target functions with reduced iteration requirements [25,26]. Several studies have highlighted the practical advantages of quantum machine learning algorithms, demonstrating their superior performance over classical counterparts in predicting complex medical outcomes [25] and image restoration [26]. Among various quantum machine learning methods, parameterized quantum circuits (PQCs), variational quantum circuits (VQCs), or quantum neural networks (QNNs) stand out as particularly promising.…”

“…Among various quantum machine learning methods, parameterized quantum circuits (PQCs), variational quantum circuits (VQCs), or quantum neural networks (QNNs) stand out as particularly promising. For instance, researchers have utilized hybrid quantum neural networks to discover drug molecules [25] and recover contaminated ghost images [26], showcasing superior performance compared to classical counterparts with fewer iterations and higher accuracy, especially when dealing with limited datasets. This suggests their potential for addressing pharmacological and medical challenges, such as predicting patient responses to different medications or evaluating patient prognosis and diagnosis.…”

Universal adversarial perturbations for multiple classification tasks with quantum classifiers

“…The interplay between quantum computing and machine learning gives rise to a research frontier of quantum machine learning [1,2]. The attempting quantum computing characteristics hold the intriguing potential to trigger a revolution in traditional machine learning study [3][4][5][6][7][8][9][10]. Along this direction, a series of careful investigations have been conducted and various kinds of quantum classifiers have been introduced in [11][12][13][14].…”