arXiv subjects

Daniela A. Zaidenberg

Publications and source records attributed to Daniela A. Zaidenberg.

3 recordsLinked to original sources

arXiv2025

High-Efficiency, Low-Loss Floquet-mode Traveling Wave Parametric Amplifier

Advancing fault-tolerant quantum computing and fundamental science necessitates quantum-limited amplifiers with near-ideal quantum efficiency and multiplexing capability. However, existing solutions typically achieve one at the expense of the other. In this work, we experimentally demonstrate the first Floquet-mode traveling-wave parametric amplifier (Floquet TWPA), which achieves nearly quantum-limited noise performance, minimal dissipation, and broadband operation, breaking the presumption that broadband amplifiers introduce higher noise. We achieve a system measurement efficiency of $65.1\pm5.8\%$ when measuring a superconducting qubit, which to our knowledge is the highest-reported in a superconducting qubit readout experiment utilizing phase-preserving amplifiers. Our device exhibits $>20$-dB amplification over a $3$-GHz instantaneous bandwidth, $<\!0.5\,$-dB average in-band insertion loss, and the highest reported intrinsic quantum efficiency for a TWPA of $92.1\pm7.6\%$, relative to an ideal phase-preserving amplifier. Fabricated in a superconducting qubit process, these general-purpose Floquet TWPAs are suitable for fast, high-fidelity multiplexed readout in large-scale quantum systems and future monolithic integration with quantum processors.

quant-ph↗

arXiv2021

On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

This article aims to investigate how circuit-based hybrid Quantum Convolutional Neural Networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case, and tested on the EuroSAT dataset used as reference benchmark. The results of the multiclass classification prove the effectiveness of the presented approach, by demonstrating that the QCNN performances are higher than the classical counterparts. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for futures investigations.

eess.IV↗

arXiv2021

Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Initial results demonstrate feasibility. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.

quant-ph↗