Machine Learning

Mysterious Quantum Links Could Help Lead To Exponential Scale-up

Machine learning, which now powers speech recognition, computer vision, and more, could prove even more powerful when run on quantum computers. Now scientists find the strange quantum phenomenon known as entanglement, which Einstein dubbed “spooky action at a distance,” might help remove a major potential roadblock to implementing quantum machine learning, a new study finds.

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Entanglement Is a Key Component of Quantum Computing and Unlocking Scaling Needed for Quantum Machine Learning

The field of machine learning on quantum computers got a boost from new research removing a potential roadblock to the practical implementation of quantum neural networks. While theorists had previously believed an exponentially large training set would be required to train a quantum neural network, the quantum No-Free-Lunch theorem developed by Los Alamos National Laboratory shows that quantum entanglement eliminates this exponential overhead.

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Rigetti Sets Stage for Quantum Advantage on a High-Impact, Operationally Relevant Challenge

Rigetti Computing, a pioneer in hybrid quantum-classical computing, announced it has developed an effective solution to a weather modeling problem using quantum computers. Building on existing machine learning workflows, the company applied a combination of classical and quantum machine learning techniques to produce high-quality synthetic weather radar data and improve classical models for storm prediction. The work was performed on Rigetti’s 32-qubit system, demonstrating that practical applications are within reach for near-term quantum hardware.

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