Titlebook: Quantum Computing: A Shift from Bits to Qubits; Rajiv Pandey,Nidhi Srivastava,Kanishka Tyagi Book 2023 The Editor(s) (if applicable) and T

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書目名稱Quantum Computing: A Shift from Bits to Qubits影響因子(影響力)




書目名稱Quantum Computing: A Shift from Bits to Qubits影響因子(影響力)學(xué)科排名




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書目名稱Quantum Computing: A Shift from Bits to Qubits網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書目名稱Quantum Computing: A Shift from Bits to Qubits被引頻次




書目名稱Quantum Computing: A Shift from Bits to Qubits被引頻次學(xué)科排名




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書目名稱Quantum Computing: A Shift from Bits to Qubits年度引用學(xué)科排名




書目名稱Quantum Computing: A Shift from Bits to Qubits讀者反饋




書目名稱Quantum Computing: A Shift from Bits to Qubits讀者反饋學(xué)科排名

異端邪說(shuō)下

屈尊

Efficient Quantum Circuit for Karatsuba Multipliere been proved to have an improvement in qubits, garbage outputs, and ancilla inputs when it comes to comparison with recent research that have been done concerning this field. Bennett’s garbage removal strategy with the SWAP gate is used to remove garbage output from existing works in order to estab

苦笑

Quantum Conceptstum gates. Output of these gates when applied to various states such as . are discussed. The last part of this chapter will discuss the code to generate Bell states also called as quantum entanglement, the output of quantum entanglement using various visualizations supported by Qiskit such as qspher

conjunctivitis

Tincture

Quantum Neural Network for Image Classification Using TensorFlow Quantumork on digit classification. Also, the chapter discussed the advantages and limitations of quantum neural networks in image classification. The first section of the chapter introduced the quantum neural network models and the TensorFlow quantum library. Afterward, the data preparation steps such as

腐敗

Quantum Computing-Enabled Machine Learning for an Enhanced Model Training Approachantum machine learning takes these aspirations further by looking at the subatomic level to aid learning. Machine learning-based algorithms minimize a constrained multivariate function. Different algorithms have different hyper-parameters that need to be tuned for the trained model to generalize wel

DEAF

Numerical Modeling of the Major Temporal Arcade Using a Quantum Genetic Algorithm of the Mean distance to the closest point and Hausdorff distance obtaining the average values of 9.91 and 53.32, respectively, using a test set of images. Finally, in terms of computational time, the proposed method achieved an average of 7.51 s per image, which makes it suitable for computer-aided

cravat

Entangled Quantum Neural Networkg the output layer of MLP with a quantum measurement operation (ECA); or by reconstructing the neurons in NN using regularizer to constrain state vectors to entangled states (QNN). With extensive experiments on the three most frequently used machine learning datasets from UCI, Abalone, Wine Quality

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