Quantum Machine Learning for Optical and SAR Classification

Miller, Leslie W.; Uehara, Glen; Sharma, Aradhita; Spanias, Andreas

doi:10.1109/dsp58604.2023.10167979

Cited by 3 publications

(1 citation statement)

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“…Scalability may be a crucial obstruction in quantum computing since existing quantum frameworks have a restricted number of qubits, making complicated errands requiring a tall number of qubits incomprehensible to fathom. Mistake recti cation approaches as often as possible require a critical number of additional qubits and modern forms, which is declined by the adaptability issue [24].…”

Section: Advantages and Disadvantages Of Current Researchesmentioning

confidence: 99%

Quantum Neural Networks: A Novel Architecture for Hybrid Quantum-Classical Algorithms

Gundu

2024

Preprint

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The capacity of quantum computing to tackle complex problems faster than general computers might lead to industry revolutions. However, actual implementation is problematic due to limited qubit coherence and inherent noise. Using Quantum Neural Networks (QNNs), hybrid quantum-classical algorithms successfully address optimization problems by combining the benefits of both quantum and traditional computer paradigms. The interface layer, the classical layer, and the quantum layer make up the three fundamental parts of the suggested design. The architecture's performance is contrasted with existing methods to demonstrate its advantages in terms of speed, accuracy, and scalability. With the help of this innovative design, difficult issues that are outside the capabilities of conventional computers can now be tackled, offering a workable solution for issues with finance, logistics, and medication development.

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Section: Advantages and Disadvantages Of Current Researchesmentioning

confidence: 99%