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Neural Networks Enhance Quantum Error Correction - MSN
AlphaQubit, a recurrent-transformer neural network, outperforms state-of-the-art decoders in quantum error correction, enhancing surface code decoding accuracy.
They are developing a Quantum Convolutional Neural Network (QCNN) architecture to enhance the performance of traditional computer vision tasks using quantum mechanics principles.
One thing to note is that the 150 lines quoted includes comments, so the actual code bulk is even less. The majority of the program handles the quantum register and a generic way to apply a matrix ...
Quantum machine learning is a highly promising application for quantum computing. The hybrid quantum-classical convolutional neural networks (QCCNN) employs parameter quantum circuit to enhance ...
This simply means that neural networks running on quantum systems could, potentially, be exponentially more robust than those running on classical systems.
Quantum computing establishes its fundamental distinctions from classical computing through multiple essential characteristics. The basic information unit in classical computing exists as a bit ...
Quantum resources for artificial intelligence Memristors are thought to be valuable in neural networks, which typically require large amounts of training data to operate effectively. An architecture ...
With this in mind, a team of researchers at Harvard University recently developed a quantum circuit-based algorithm inspired by convolutional neural networks (CNNs), a popular machine learning ...
For example, when using the quantum processor to reconstruct lightning data, they found it did a better job at lower altitudes but was generally comparable to the classical neural network.
Although neural networks have been studied for decades, over the past couple of years there have been many small but significant changes in the default techniques used. For example, ReLU (rectified ...
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