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Optica Sensing Congress

Quantum-enhanced learning with a controllable bosonic variational sensor network

Pengcheng Liao, Bingzhi Zhang, Quntao Zhuang

Published Jan 1, 2025• 0 citations
Publisher

Abstract

We propose a generalized SLAEN using cavity-QED to enable nonlinear clas-sification. Training quantum probes reveals a threshold phenomenon, dramatically reducing error compared to Gaussian SLAEN.
DOI
10.1364/qsm.2025.qw3b.1

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Abstract The emergence of quantum sensor networks has presented opportunities for enhancing complex sensing tasks, while simultaneously introducing significant challenges in designing and analyzing quantum sensing protocols due to the intricate nature of entanglement and physical processes. Supervised learning assisted by an entangled sensor network (SLAEN) (Zhuang and Zhang 2019Phys. Rev.X9041023) represents a promising paradigm for automating sensor-network design through variational quantum machine learning. However, the original SLAEN, constrained by the Gaussian nature of quantum circuits, is limited to learning linearly separable data. Leveraging the universal quantum control available in cavity quantum electrodynamics experiments, we propose a generalized SLAEN capable of handling nonlinear data classification tasks. We establish a theoretical framework for physical-layer data classification to underpin our approach. Through training quantum probes and measurements, we uncover a threshold phenomenon in classification error across various tasks—when the energy of probes exceeds a certain threshold, the error drastically diminishes to zero, providing a significant improvement over the Gaussian SLAEN. Despite the non-Gaussian nature of the problem, we offer analytical insights into determining the threshold and residual error in the presence of noise. Our findings carry implications for radio-frequency photonic sensors and microwave dark matter haloscopes.
Variational quantum circuits (VQCs) built upon noisy intermediate-scale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning.However, the required VQC depth to demonstrate a quantum advantage over classical schemes is beyond the reach of available NISQ devices.Supervised learning assisted by an entangled sensor network (SLAEN) is a distinct paradigm that harnesses VQCs trained by classical machine-learning algorithms to tailor multipartite entanglement shared by sensors for solving practically useful data-processing problems.Here, we report the first experimental demonstration of SLAEN and show an entanglement-enabled reduction in the error probability for classification of multidimensional radio-frequency signals.Our work paves a new route for quantum-enhanced data processing and its applications in the NISQ era.
Efficient quantum error correction is essential for the advancement of quantum computing. We propose a quantum neural network with a global structure that reduces the number of unitary matrices required in quantum circuits. This approach resulted in a 97% reduction in training time and up to a 25% improvement in the training completion rate, ultimately achieving a 100% success rate in training while surpassing the error correction performance reported in previous studies. In addition, we demonstrated the enhanced robustness of quantum error correction against internal network noise. Moreover, the fidelity of quantum error correction under internal network noise increased by up to 15% due to the reduced computational load.
Many existing quantum supervised learning (SL) schemes consider data given a priori in a classical description. With only noisy intermediate-scale quantum (NISQ) devices available in the near future, their quantum speedup awaits the development of quantum random access memories (qRAMs) and fault-tolerant quantum computing. There, however, also exist a multitude of SL tasks whose data are acquired by sensors, e.g., pattern classification based on data produced by imaging sensors. Solving such SL tasks naturally requires an integrated approach harnessing tools from both quantum sensing and quantum computing. We introduce supervised learning assisted by an entangled sensor network (SLAEN) as a means to carry out SL tasks at the physical layer. The entanglement shared by the sensors in SLAEN boosts the performance of extracting global features of the object under investigation. We leverage SLAEN to construct an entanglement-assisted support-vector machine for data classification and entanglement-assisted principal component analyzer for data compression. In both schemes, variational circuits are employed to seek the optimum entangled probe states and measurement settings to maximize the entanglement-enabled {enhancement}. We observe that SLAEN enjoys an appreciable entanglement-enabled performance gain, even in the presence of loss, over conventional strategies in which classical data are acquired by separable sensors and subsequently processed by classical SL algorithms. SLAEN is realizable with available technology, opening a viable route toward building NISQ devices that offer unmatched performance beyond what the optimum classical device is able to afford.
Quantum-Enhanced Gradient Descent (QED) is a novel approach to machine learning that integrates quantum computing principles with gradient descent algorithms. The core idea is to leverage the unique capabilities of quantum mechanics, specifically quantum entanglement and superposition, to accelerate the training process of machine learning models, particularly deep neural networks. This paper explores the potential of QED by investigating how quantum-enhanced gradient descent can significantly reduce training time and improve model parameter accuracy compared to classical gradient descent. We present a detailed theoretical framework and preliminary experimental results demonstrating the efficacy of QED in optimizing specific machine learning models. The paper also discusses the challenges and future directions of this research.
Trainability and generalization are two key issues in quantum machine learning on the basis of variational algorithms. It has been demonstrated in [1] that with the reduced-domain initialization strategy, the trainability of variational parameters can be greatly enhanced for complex objectives and general deep circuits. In this paper, we further explore the generalization of variational quantum learning under the reduced-domain initialization. We theoretically derive the upper bound of the generalization error and demonstrate the enhancement as compared to that under the usual parameter initialization methods. Thus, our results clearly show that the reduced-domain initialization can push the boundary of applicability of deep variational quantum learning toward practical applications.