Quantum Machine Learning
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This article mainly introduces the basic concepts of quantum machine learning, including the basic concepts of quantum computing, the basic concepts of machine learning, and the basic concepts of quantum machine learning.
Quantum machine learning is a new interdisciplinary research field that combines quantum physics and machine learning. It is a new research direction that uses quantum computing to solve machine learning problems.
Quantum computing is a new type of computing method that uses the principles of quantum mechanics to perform calculations. It is a new type of computing method that uses the principles of quantum mechanics to perform calculations.
All the benefits of quantum computing than the traditional computing are due to the superposition and entanglement of quantum states. The superposition and entanglement of quantum states are the two most important features of quantum computing.
The superposition of quantum states is a phenomenon in which a quantum system is in multiple states at the same time.
It is helpful on the traditinal computing. For example, if we want to calculate the probability of a coin landing on the head, we need to flip the coin many times and calculate the probability. But if we use quantum computing, we can flip the coin only once and calculate the probability.
Entanglement is a phenomenon in which the quantum states of two or more particles are correlated with each other.
The quantum state of a single qubit can be expressed as:
where $\alpha$ and $\beta$ are complex numbers, and $|0\rangle$ and $|1\rangle$ are the basis states of the qubit.
$\rangle$ and $\langle$ are called bra and ket, respectivel. Both are vectors in the Hilbert space. These are the base notation for quantum computing. bra and ket are the same vector, but bra is the conjugate transpose of ket.
Here, we can see that the quantum state can be represented as $\alpha$ times the state 0 plus $\beta$ times state 1, which models the probability. And according to (Matheus's article), quantum computing is based on probabilistic events on its core, which is on the core of ML models.