# Introduction to Quantum Machine Learning

This is the general blog of quantum machine learning. Subsequent articles will be on specific terms and terminology of quantum computing, mechanics, and other things.

## Machine Learning with Quantum computers

**wikipedia source**

Quantum-enhanced machine learning refers to quantum algorithms that solve task in machine learning, thereby improving and often expediting classical machine learning techniques. Such algorithms typically require one to encode the given classical data set into a quantum computer to make it accessible for quantum information processing.

Subsequently, quantum **information science **routines are applied **and therefore the results of **the quantum computation is read out by measuring the quantum system. **as an example **, **the result **of the measurement of a qubit reveals the **results of **a binary classification task.

While many proposals of quantum machine learning algorithms are still purely theoretical **and need **a full-scale universal quantum computer to be tested, others **are **implemented on small-scale or special purpose quantum devices.

## Quantum Data

**TensorFlow official website source**

Quantum data is any data source **that happens during a **natural or artificial quantum system. Quantum data exhibits superposition and entanglement, **resulting in probability **distributions **that would **require an exponential amount of classical computational resources to represent or store. The quantum supremacy experiment showed **it’s **possible to sample from **a particularly **complex **probability **distribution of ²⁵³ **Hilbert space**.

Example of quantum data:

- Chemical simulation
- Quantum matter simulation
- Quantum control
- Quantum communication networks
- Quantum metrology

## Linear Algebra simulation with quantum amplitudes

wikipedia source

A number of quantum algorithm for machine learning are based on the idea of amplitude encoding. that is to associate the amplitudes of a quantum state with the input and outputs of computations. Since a state of n qubits is described by 2^n complex amplitudes, this information encoding can allow for an exponentially compact representation.

Intuitively, this corresponds to associating a discrete probability distribution over binary random variables with classical vector. The goal of algorithm **supported **amplitude encoding is to formulate quantum algorithms whose resources grow polynomially **within the **number of qubits n, which amounts to a logarithmic growth **within the **number of amplitudes and thereby the dimension of the input.

Quantum matrix inversion can be applied to machine learning methods in which the training reduces to solving a linear system of equations, for instance, in least-squares linear regression. The least squares version of support vector machine and Gaussian processes. Now sounds familiar :).

A crucial bottleneck of methods that simulate **algebra **computations with the amplitudes of quantum states is state preparation, **which frequently **requires one to initialise a quantum system **during a **state whose amplitudes reflect the features of **the whole **dataset. Although efficient methods for state preparation are known for specific case. This step easily hides the complexity of the task.

## Quantum Neural Network

wikipedia source

Quantum analogues or generalisations of classical neural nets are often referred to as quantum neural networks. The implementation and extension of neural networks using photons, layered variational circuits or quantum Ising-type modules.

Quantum neural networks are often defined as an expansion of Deutsch’s model of a quantum computational network. Within this model, nonlinear and irreversible gates, dissimilar to the Hamiltonian operation, are deployed **to take a position **the given dataset. Such gates **make sure **phases unable to be observed and generate specific oscillations.

Quantum neural networks apply the principals quantum information and quantum computation to classical neurocomputing. Current research shows that QNN can exponentially increase **the quantity **of computing power **and therefore the **degrees of freedom for a computer, which **is restricted **for a classical computer to its size.

A quantum neural network has computational capabilities to decrease the number of steps, qubits used and computation time. The wave function to quantum mechanics is the **Neurons** for neural networks. To test quantum applications ina neural network quantum dot molecules are deposited on a substrate of GaAs or similar record how they communicate with one another. Each quantum dot can be referred as an island of electric activity, and when such dots are close enough electrons can tunnel underneath the islands. An even distribution across the substrate in sets of two create dispoles and ultimately two spin states, up or down. These states are commonaly known as qubits with corresponding states of |0> na d|1> dirac notation.