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CRC Press eBooks

Wave Functions and Experimental Outcomes

Steven A. Adelman

Published May 31, 2021• 0 citations
Publisher

Abstract

In this chapter, we develop some of the most central principles of quantum mechanics. We especially develop the quantum probability rules that predict from the wave function of a quantum system the likelihoods of each of the possible outcomes of experiments which measure the values of the observables of the system.
DOI
10.1201/9780429155741-4

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Quantum mechanics has been enormously successful in explaining the behavior of microsystems such as molecules, atoms, and nuclei. We have seen that quantum mechanics describes a quantum system by the wave function ψ, which is the solution of the Schro¨dinger equation. The wave function determines the probability amplitudes (whose modulus squared gives the probability) for all possible outcomes of a measurement corresponding to an observable on the system. In general, the wave function does not uniquely predict the result of a measurement; it only provides a probability distribution for all possible results of the measurement on a quantum system. Only if the system happens to be in an eigenstate corresponding to the observable being measured, is the result of a measurement of this observable completely predictable. On the other hand, the results of measurements corresponding to a conjugate or non-commuting observable, on the same system in the same state, are indeterminate. Quantum mechanical description of physical systems raises profound questions: How are the wave function and the probabilities for different outcomes of a measurement to be interpreted? Does the wave function represent a single system or an ensemble of systems? Does the physical system actually have the attribute corresponding to the measurement in question or does the measurement itself create the attribute? These questions led to different interpretations of quantum mechanics, which were vigorously debated during the years of its discovery as also in the following decades. The new idea of indeterminacy was especially difficult to reconcile with the long-held view that a physical theory should aim at predicting accurately, and without ambiguity, the result of any measurement on any system, big or small. These debates not only gave us a deeper understanding of quantum mechanics but also changed profoundly the way we view the micro-world.
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Abstract Measurement in the quantum realm is not deterministic as in the classical one, and many of the quantum principles delineated in Chapter 1 are expanded. The results of individual quantum measurements cannot be predicted; quantum mechanics gives us only the probabilities of observing each of the possible measurement outcomes. Anchored in the concrete example of light polarization, Chapter 5 takes on the Born rule or the probability principle of quantum mechanics involving the square of state amplitudes. What is known before a measurement and what is known after (measurement principle) explains the no-cloning theorem. The standard terminology of eigenvectors and eigenstates is also introduced.
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The objective of Chapter 4 is to give an overview of the basic concepts of quantum mechanics and their interpretation. Thus this chapter functions as a theoretical preparation for a better understanding of the quantum world and of the knowledge measurement process. First, the terminology of the word “quantum” and its impact on the derivation of the term “knowledge quantum” is explained in Chapter 4.1. Then, the discussion moves to the historical development of quantum mechanics. The key issues and current research field for quantum theory will be given in an overview. The following chapters show some insights into key quantum mechanical concepts such as slit-experiments, wave-particle duality, the Copenhagener interpretation, and quantum probability. Chapter 4 closes with an explanation of the Uncertainty Principle of Werner Heisenberg, especially since the Uncertainty Principle is used to derive the knowledge measurement process through the method of analogical reasoning. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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