Error-Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis

Jose, Sharu Theresa; Simeone, Osvaldo

doi:10.1109/tqe.2022.3229747

Cited by 2 publications

(2 citation statements)

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“…APPENDIX B: PARAMETERIZED QUANTUM CIRCUIT PQC [48] functions in a way that resembles a conventional DNN, featuring adjustable parameters embedded within the circuits. Just as a traditional DNN consists of numerous layers, a PQC can be constructed by repeating a unit layer several times.…”

Section: Discussionmentioning

confidence: 99%

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Fingerprinting-Based Indoor Localization With Hybrid Quantum-Deep Neural Network

Jeong,

Park

et al. 2023

IEEE Access

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This paper presents an approach for enhancing indoor localization accuracy using a hybrid quantum deep neural network model (H-QDNN). To improve the accuracy of indoor localization based on contemporary techniques, we employ the combined strengths of quantum computing (QC) and deep neural networks (DNN). The strengths of QC, which accelerates the training process and enables efficient handling of complex data representations through quantum superposition and entanglement, were combined with DNN, known for its ability to extract meaningful features and learn complex patterns from data. The proposed model can be trained using small datasets, reducing the need for extensive data, particularly useful in indoor localization, where data collection can be time-consuming and resourceintensive. To evaluate the effectiveness of our proposed approach, we conduct extensive experiments and comparisons with existing state-of-the-art methods. The results demonstrate that the H-QDNN model significantly improves indoor localization accuracy compared to traditional techniques. Additionally, we provide insights into the factors contributing to enhanced performance, such as the quantum-inspired algorithms utilized and the integration of mixed fingerprints.

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Section: Discussionmentioning

confidence: 99%

“…As gradient-focused optimization methods are used for traditional DNNs, we introduced an SGD algorithm [48] tailored for our H-QDNN model. Similar to adjusting weights in standard DNNs, the quantum gate parameters within the H-QDNN model need tuning.…”

Section: Appendix C: Sgd Optimizationmentioning

confidence: 99%