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QFT is applied in quantum ML for analyzing periodicities in the data, encoding and estimating quantum states' amplitudes which can be useful for pattern recognition tasks.

QPCA is an algorithm that extends PCA to quantum computing, allowing for the potential exponential speedup in dimensionality reduction tasks by preparing and measuring quantum states.

Quantum discriminative learning algorithms aim to classify new inputs based on a learned decision boundary, taking advantage of quantum parallelism and entanglement to improve performance.

In quantum ML, adiabatic quantum computing can be harnessed to encode and solve optimization problems by slowly evolving the state of the quantum system to stay in its lowest energy state.

In quantum ML, the DMRG method is extended to efficiently deal with quantum data and extract features or train models that are otherwise intractable with classical methods.

Quantum reinforcement learning leverages quantum computing to enhance the decision-making process, which may lead to speedups in learning optimal policies for complex environments in ML applications.

In quantum ML, amplitude amplification can be used to enhance the probability of successful outcomes of quantum measurements, thereby potentially improving algorithms' effectiveness in statistical learning.

Quantum feature mapping involves encoding classical data into the high-dimensional Hilbert space of a quantum system to reveal complex structures and relationships, potentially leading to more powerful ML models.

Quantum kernel methods use the Hilbert space of a quantum computer to compute similarity measures (kernels) between data points, which can lead to more complex feature mappings and potential speedups in ML tasks.

Quantum Boltzmann machines exploit quantum computation to solve problems in generative modeling and feature learning, potentially enhancing the efficiency and capacity of classical Boltzmann machines.

Quantum circuit learning applies parametrized quantum circuits to learn a model based on given data and measurement outcomes, posing as a powerful method for classifications and regression tasks.

Quantum clustering employs quantum algorithms to perform clustering analysis on data, potentially allowing for enhanced pattern recognition and segmentation in large feature spaces.

Quantum annealing is used in quantum ML to solve optimization problems by finding the ground state of a quantum system which encodes the solution to the optimization problem.

VQE is used to approximate the ground state energy of a Hamiltonian, which can be applied to molecular simulations in quantum ML for creating new materials or drugs.

QSVM leverages quantum computing to perform classical support vector machine tasks more efficiently, offering speedups for large datasets with complex decision boundaries.

QNNs represent an extension of classical neural networks into the quantum domain, promising to exponentially increase the computational power for complex problem solving and feature detection in large datasets.

The swap test is used in quantum ML to estimate the fidelity between two quantum states, valuable for tasks like clustering or measuring similarities between quantum-encoded data points.

Quantum genetic algorithms apply principles of quantum mechanics to improve traditional genetic algorithms, potentially leading to faster convergence and better exploration of problem's solution space.

Quantum feature selection utilizes quantum computing to select the most relevant features for a given machine learning model, aiming to improve the efficiency and accuracy of the learning process.