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Furthermore, quantum algorithms can be used to analyze quantum states instead of classical data. [16] [17] Beyond quantum computing, the term "quantum machine learning" is also associated with classical machine learning methods applied to data generated from quantum experiments (i.e. machine learning of quantum systems), such as learning the ...
Real data is always finite, and so its study requires us to take stochasticity into account. Statistical analysis gives us the ability to separate true features of the data from artifacts introduced by random noise. Persistent homology has no inherent mechanism to distinguish between low-probability features and high-probability features.
In quantum computing, a quantum algorithm is an algorithm that runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation. [ 1 ] [ 2 ] A classical (or non-quantum) algorithm is a finite sequence of instructions, or a step-by-step procedure for solving a problem, where each step ...
However, the quantum version of this problem can be solved with just one call to the oracle plus some extra quantum gates. Although Deutsch–Jozsa itself is considered more of a “teaching example” than a practical application, it demonstrates one of the key ideas of quantum algorithms: leveraging superposition and interference to reduce ...
Quantum state tomography is a process by which, given a set of data representing the results of quantum measurements, a quantum state consistent with those measurement results is computed. [50] It is named by analogy with tomography , the reconstruction of three-dimensional images from slices taken through them, as in a CT scan .
Circuit implementing the swap test between two states | and | The swap test is a procedure in quantum computation that is used to check how much two quantum states differ, appearing first in the work of Barenco et al. [ 1 ] and later rediscovered by Harry Buhrman , Richard Cleve , John Watrous , and Ronald de Wolf . [ 2 ]
Quantum Clustering (QC) is a class of data-clustering algorithms that use conceptual and mathematical tools from quantum mechanics. QC belongs to the family of density-based clustering algorithms, where clusters are defined by regions of higher density of data points.
In quantum computing, Grover's algorithm, also known as the quantum search algorithm, is a quantum algorithm for unstructured search that finds with high probability the unique input to a black box function that produces a particular output value, using just () evaluations of the function, where is the size of the function's domain.