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The ability to experimentally control and prepare increasingly complex quantum systems brings with it a growing need to turn large and noisy data sets into meaningful information. This is a problem that has already been studied extensively in the classical setting, and consequently, many existing machine learning techniques can be naturally ...
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
The need for models that can be understood by humans emerges in quantum machine learning in analogy to classical machine learning and drives the research field of explainable quantum machine learning (or XQML [95] in analogy to XAI/XML). These efforts are often also referred to as Interpretable Machine Learning (IML, and by extension IQML). [96]
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce. [2] [3] [4] Many organizations, including governments, publish and share their datasets. The datasets are classified, based on the licenses, as Open data and Non-Open data.
Padilla et al. recommend in "Do we Need M&S Science" to distinguish between M&S Science, Engineering, and Applications. [10] M&S Science contributes to the Theory of M&S, defining the academic foundations of the discipline. M&S Engineering is rooted in Theory but looks for applicable solution patterns. The focus is general methods that can be ...
Computing Research at MIT began with Vannevar Bush's research into a differential analyzer and Claude Shannon's electronic Boolean algebra in the 1930s, the wartime MIT Radiation Laboratory, the post-war Project Whirlwind and Research Laboratory of Electronics (RLE), and MIT Lincoln Laboratory's SAGE in the early 1950s. At MIT, research in the ...
Scientific modelling is an activity that produces models representing empirical objects, phenomena, and physical processes, to make a particular part or feature of the world easier to understand, define, quantify, visualize, or simulate.