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Harnessing Quantum Computing for Hybrid Machine Learning Models

https://medium.com/@zbabar/harnessing-quantum-computing-for-hybrid-machine-le…

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quera.com news

Top Applications Of Quantum Computing for Machine Learning

https://www.quera.com/blog-posts/applications-of-quantum-computing-for-machin…

# Top Applications Of Quantum Computing for Machine Learning. Machine Learning has two roles within quantum computing. On the receiving side, quantum computers use classical machine learning to optimize hardware operations, control systems, and user interfaces. ## **What is Quantum Machine Learning?**. ## **Quantum Advantage in Machine Learning**. ## **Quantum Machine Learning Applications**. Quantum machine learning (QML) use cases overlap two other major classifications of quantum computing applications: quantum simulation and quantum optimization. And anywhere you find a classical neural network, is a potential application of quantum machine learning, as well:. # Top Applications Of Quantum Computing for Machine Learning. Machine Learning has two roles within quantum computing. ## **What is Quantum Machine Learning?**. ## **Quantum Advantage in Machine Learning**. ## **Quantum Machine Learning Applications**. Quantum machine learning (QML) use cases overlap two other major classifications of quantum computing applications: quantum simulation and quantum optimization. And anywhere you find a classical neural network, is a potential application of quantum machine learning, as well:.

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en.wikipedia.org article

Quantum machine learning - Wikipedia

https://en.wikipedia.org/wiki/Quantum_machine_learning

A number of quantum algorithms for machine learning are based on the idea of amplitude encoding, that is, to associate the [amplitudes](/wiki/Probability_amplitude "Probability amplitude") of a quantum state with the inputs and outputs of computations.[[30]](#cite_note-Patrick_Rebentrost_2014-30)[[31]](#cite_note-Nathan_Wiebe_2012-31)[[32]](#cite_note-Maria_Schuld_2016-32) Since a state of n {\displaystyle n} ![{\displaystyle n}](https://wikimedia.org/api/rest_v1/media/math/render/svg/a601995d55609f2d9f5e233e36fbe9ea26011b3b) qubits is described by 2 n {\displaystyle 2^{n}} ![{\displaystyle 2^{n}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/8226f30650ee4fe4e640c6d2798127e80e9c160d) complex amplitudes, this information encoding can allow for an exponentially compact representation. One of these conditions is that a [Hamiltonian](/wiki/Hamiltonian_(quantum_mechanics) "Hamiltonian (quantum mechanics)") which entry-wise corresponds to the matrix can be simulated efficiently, which is known to be possible if the matrix is sparse[[34]](#cite_note-34) or low rank.[[35]](#cite_note-35) For reference, any known classical algorithm for [matrix inversion](/wiki/Matrix_inversion "Matrix inversion") requires a number of operations that grows [more than quadratically in the dimension of the matrix](/wiki/Computational_complexity_of_mathematical_operations#Matrix_algebra "Computational complexity of mathematical operations") (e.g. O ( n 2.373 ) {\displaystyle O{\mathord {\left(n^{2.373}\right)}}} ![{\displaystyle O{\mathord {\left(n^{2.373}\right)}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/1aae64872678cfd84c60f52b8c789d8d67141fb0)), but they are not restricted to sparse matrices. **[^](#cite_ref-118)** ["Can quantum machine learning move beyond its own hype?"](https://www.quantamagazine.org/job-one-for-quantum-computers-boost-artificial-intelligence-20180129/).

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