Quantum Probabilities and the Born Rule in the Intuitionistic
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On the Lattice Structure of Quantum Logic
BULL. AUSTRAL. MATH. SOC. MOS 8106, *8IOI, 0242 VOL. I (1969), 333-340 On the lattice structure of quantum logic P. D. Finch A weak logical structure is defined as a set of boolean propositional logics in which one can define common operations of negation and implication. The set union of the boolean components of a weak logical structure is a logic of propositions which is an orthocomplemented poset, where orthocomplementation is interpreted as negation and the partial order as implication. It is shown that if one can define on this logic an operation of logical conjunction which has certain plausible properties, then the logic has the structure of an orthomodular lattice. Conversely, if the logic is an orthomodular lattice then the conjunction operation may be defined on it. 1. Introduction The axiomatic development of non-relativistic quantum mechanics leads to a quantum logic which has the structure of an orthomodular poset. Such a structure can be derived from physical considerations in a number of ways, for example, as in Gunson [7], Mackey [77], Piron [72], Varadarajan [73] and Zierler [74]. Mackey [77] has given heuristic arguments indicating that this quantum logic is, in fact, not just a poset but a lattice and that, in particular, it is isomorphic to the lattice of closed subspaces of a separable infinite dimensional Hilbert space. If one assumes that the quantum logic does have the structure of a lattice, and not just that of a poset, it is not difficult to ascertain what sort of further assumptions lead to a "coordinatisation" of the logic as the lattice of closed subspaces of Hilbert space, details will be found in Jauch [8], Piron [72], Varadarajan [73] and Zierler [74], Received 13 May 1969. -
Relational Quantum Mechanics
Relational Quantum Mechanics Matteo Smerlak† September 17, 2006 †Ecole normale sup´erieure de Lyon, F-69364 Lyon, EU E-mail: [email protected] Abstract In this internship report, we present Carlo Rovelli’s relational interpretation of quantum mechanics, focusing on its historical and conceptual roots. A critical analysis of the Einstein-Podolsky-Rosen argument is then put forward, which suggests that the phenomenon of ‘quantum non-locality’ is an artifact of the orthodox interpretation, and not a physical effect. A speculative discussion of the potential import of the relational view for quantum-logic is finally proposed. Figure 0.1: Composition X, W. Kandinski (1939) 1 Acknowledgements Beyond its strictly scientific value, this Master 1 internship has been rich of encounters. Let me express hereupon my gratitude to the great people I have met. First, and foremost, I want to thank Carlo Rovelli1 for his warm welcome in Marseille, and for the unexpected trust he showed me during these six months. Thanks to his rare openness, I have had the opportunity to humbly but truly take part in active research and, what is more, to glimpse the vivid landscape of scientific creativity. One more thing: I have an immense respect for Carlo’s plainness, unaltered in spite of his renown achievements in physics. I am very grateful to Antony Valentini2, who invited me, together with Frank Hellmann, to the Perimeter Institute for Theoretical Physics, in Canada. We spent there an incredible week, meeting world-class physicists such as Lee Smolin, Jeffrey Bub or John Baez, and enthusiastic postdocs such as Etera Livine or Simone Speziale. -
Probability and Statistics Lecture Notes
Probability and Statistics Lecture Notes Antonio Jiménez-Martínez Chapter 1 Probability spaces In this chapter we introduce the theoretical structures that will allow us to assign proba- bilities in a wide range of probability problems. 1.1. Examples of random phenomena Science attempts to formulate general laws on the basis of observation and experiment. The simplest and most used scheme of such laws is: if a set of conditions B is satisfied =) event A occurs. Examples of such laws are the law of gravity, the law of conservation of mass, and many other instances in chemistry, physics, biology... If event A occurs inevitably whenever the set of conditions B is satisfied, we say that A is certain or sure (under the set of conditions B). If A can never occur whenever B is satisfied, we say that A is impossible (under the set of conditions B). If A may or may not occur whenever B is satisfied, then A is said to be a random phenomenon. Random phenomena is our subject matter. Unlike certain and impossible events, the presence of randomness implies that the set of conditions B do not reflect all the necessary and sufficient conditions for the event A to occur. It might seem them impossible to make any worthwhile statements about random phenomena. However, experience has shown that many random phenomena exhibit a statistical regularity that makes them subject to study. For such random phenomena it is possible to estimate the chance of occurrence of the random event. This estimate can be obtained from laws, called probabilistic or stochastic, with the form: if a set of conditions B is satisfied event A occurs m times =) repeatedly n times out of the n repetitions. -
Why Feynman Path Integration?
Journal of Uncertain Systems Vol.5, No.x, pp.xx-xx, 2011 Online at: www.jus.org.uk Why Feynman Path Integration? Jaime Nava1;∗, Juan Ferret2, Vladik Kreinovich1, Gloria Berumen1, Sandra Griffin1, and Edgar Padilla1 1Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA 2Department of Philosophy, University of Texas at El Paso, El Paso, TX 79968, USA Received 19 December 2009; Revised 23 February 2010 Abstract To describe physics properly, we need to take into account quantum effects. Thus, for every non- quantum physical theory, we must come up with an appropriate quantum theory. A traditional approach is to replace all the scalars in the classical description of this theory by the corresponding operators. The problem with the above approach is that due to non-commutativity of the quantum operators, two math- ematically equivalent formulations of the classical theory can lead to different (non-equivalent) quantum theories. An alternative quantization approach that directly transforms the non-quantum action functional into the appropriate quantum theory, was indeed proposed by the Nobelist Richard Feynman, under the name of path integration. Feynman path integration is not just a foundational idea, it is actually an efficient computing tool (Feynman diagrams). From the pragmatic viewpoint, Feynman path integral is a great success. However, from the founda- tional viewpoint, we still face an important question: why the Feynman's path integration formula? In this paper, we provide a natural explanation for Feynman's path integration formula. ⃝c 2010 World Academic Press, UK. All rights reserved. Keywords: Feynman path integration, independence, foundations of quantum physics 1 Why Feynman Path Integration: Formulation of the Problem Need for quantization. -
High-Fidelity Quantum Logic in Ca+
University of Oxford Department of Physics High-Fidelity Quantum Logic in Ca+ Christopher J. Ballance −1 10 0.9 Photon scattering Motional error Off−resonant lightshift Spin−dephasing error Total error budget Data −2 10 0.99 Gate error Gate fidelity −3 10 0.999 1 2 3 10 10 10 Gate time (µs) A thesis submitted for the degree of Doctor of Philosophy Hertford College Michaelmas term, 2014 Abstract High-Fidelity Quantum Logic in Ca+ Christopher J. Ballance A thesis submitted for the degree of Doctor of Philosophy Michaelmas term 2014 Hertford College, Oxford Trapped atomic ions are one of the most promising systems for building a quantum computer – all of the fundamental operations needed to build a quan- tum computer have been demonstrated in such systems. The challenge now is to understand and reduce the operation errors to below the ‘fault-tolerant thresh- old’ (the level below which quantum error correction works), and to scale up the current few-qubit experiments to many qubits. This thesis describes experimen- tal work concentrated primarily on the first of these challenges. We demonstrate high-fidelity single-qubit and two-qubit (entangling) gates with errors at or be- low the fault-tolerant threshold. We also implement an entangling gate between two different species of ions, a tool which may be useful for certain scalable architectures. We study the speed/fidelity trade-off for a two-qubit phase gate implemented in 43Ca+ hyperfine trapped-ion qubits. We develop an error model which de- scribes the fundamental and technical imperfections / limitations that contribute to the measured gate error. -
Introduction to Stochastic Processes - Lecture Notes (With 33 Illustrations)
Introduction to Stochastic Processes - Lecture Notes (with 33 illustrations) Gordan Žitković Department of Mathematics The University of Texas at Austin Contents 1 Probability review 4 1.1 Random variables . 4 1.2 Countable sets . 5 1.3 Discrete random variables . 5 1.4 Expectation . 7 1.5 Events and probability . 8 1.6 Dependence and independence . 9 1.7 Conditional probability . 10 1.8 Examples . 12 2 Mathematica in 15 min 15 2.1 Basic Syntax . 15 2.2 Numerical Approximation . 16 2.3 Expression Manipulation . 16 2.4 Lists and Functions . 17 2.5 Linear Algebra . 19 2.6 Predefined Constants . 20 2.7 Calculus . 20 2.8 Solving Equations . 22 2.9 Graphics . 22 2.10 Probability Distributions and Simulation . 23 2.11 Help Commands . 24 2.12 Common Mistakes . 25 3 Stochastic Processes 26 3.1 The canonical probability space . 27 3.2 Constructing the Random Walk . 28 3.3 Simulation . 29 3.3.1 Random number generation . 29 3.3.2 Simulation of Random Variables . 30 3.4 Monte Carlo Integration . 33 4 The Simple Random Walk 35 4.1 Construction . 35 4.2 The maximum . 36 1 CONTENTS 5 Generating functions 40 5.1 Definition and first properties . 40 5.2 Convolution and moments . 42 5.3 Random sums and Wald’s identity . 44 6 Random walks - advanced methods 48 6.1 Stopping times . 48 6.2 Wald’s identity II . 50 6.3 The distribution of the first hitting time T1 .......................... 52 6.3.1 A recursive formula . 52 6.3.2 Generating-function approach . -
Analysis of Nonlinear Dynamics in a Classical Transmon Circuit
Analysis of Nonlinear Dynamics in a Classical Transmon Circuit Sasu Tuohino B. Sc. Thesis Department of Physical Sciences Theoretical Physics University of Oulu 2017 Contents 1 Introduction2 2 Classical network theory4 2.1 From electromagnetic fields to circuit elements.........4 2.2 Generalized flux and charge....................6 2.3 Node variables as degrees of freedom...............7 3 Hamiltonians for electric circuits8 3.1 LC Circuit and DC voltage source................8 3.2 Cooper-Pair Box.......................... 10 3.2.1 Josephson junction.................... 10 3.2.2 Dynamics of the Cooper-pair box............. 11 3.3 Transmon qubit.......................... 12 3.3.1 Cavity resonator...................... 12 3.3.2 Shunt capacitance CB .................. 12 3.3.3 Transmon Lagrangian................... 13 3.3.4 Matrix notation in the Legendre transformation..... 14 3.3.5 Hamiltonian of transmon................. 15 4 Classical dynamics of transmon qubit 16 4.1 Equations of motion for transmon................ 16 4.1.1 Relations with voltages.................. 17 4.1.2 Shunt resistances..................... 17 4.1.3 Linearized Josephson inductance............. 18 4.1.4 Relation with currents................... 18 4.2 Control and read-out signals................... 18 4.2.1 Transmission line model.................. 18 4.2.2 Equations of motion for coupled transmission line.... 20 4.3 Quantum notation......................... 22 5 Numerical solutions for equations of motion 23 5.1 Design parameters of the transmon................ 23 5.2 Resonance shift at nonlinear regime............... 24 6 Conclusions 27 1 Abstract The focus of this thesis is on classical dynamics of a transmon qubit. First we introduce the basic concepts of the classical circuit analysis and use this knowledge to derive the Lagrangians and Hamiltonians of an LC circuit, a Cooper-pair box, and ultimately we derive Hamiltonian for a transmon qubit. -
Topic 1: Basic Probability Definition of Sets
Topic 1: Basic probability ² Review of sets ² Sample space and probability measure ² Probability axioms ² Basic probability laws ² Conditional probability ² Bayes' rules ² Independence ² Counting ES150 { Harvard SEAS 1 De¯nition of Sets ² A set S is a collection of objects, which are the elements of the set. { The number of elements in a set S can be ¯nite S = fx1; x2; : : : ; xng or in¯nite but countable S = fx1; x2; : : :g or uncountably in¯nite. { S can also contain elements with a certain property S = fx j x satis¯es P g ² S is a subset of T if every element of S also belongs to T S ½ T or T S If S ½ T and T ½ S then S = T . ² The universal set is the set of all objects within a context. We then consider all sets S ½ . ES150 { Harvard SEAS 2 Set Operations and Properties ² Set operations { Complement Ac: set of all elements not in A { Union A \ B: set of all elements in A or B or both { Intersection A [ B: set of all elements common in both A and B { Di®erence A ¡ B: set containing all elements in A but not in B. ² Properties of set operations { Commutative: A \ B = B \ A and A [ B = B [ A. (But A ¡ B 6= B ¡ A). { Associative: (A \ B) \ C = A \ (B \ C) = A \ B \ C. (also for [) { Distributive: A \ (B [ C) = (A \ B) [ (A \ C) A [ (B \ C) = (A [ B) \ (A [ C) { DeMorgan's laws: (A \ B)c = Ac [ Bc (A [ B)c = Ac \ Bc ES150 { Harvard SEAS 3 Elements of probability theory A probabilistic model includes ² The sample space of an experiment { set of all possible outcomes { ¯nite or in¯nite { discrete or continuous { possibly multi-dimensional ² An event A is a set of outcomes { a subset of the sample space, A ½ . -
Theoretical Physics Group Decoherent Histories Approach: a Quantum Description of Closed Systems
Theoretical Physics Group Department of Physics Decoherent Histories Approach: A Quantum Description of Closed Systems Author: Supervisor: Pak To Cheung Prof. Jonathan J. Halliwell CID: 01830314 A thesis submitted for the degree of MSc Quantum Fields and Fundamental Forces Contents 1 Introduction2 2 Mathematical Formalism9 2.1 General Idea...................................9 2.2 Operator Formulation............................. 10 2.3 Path Integral Formulation........................... 18 3 Interpretation 20 3.1 Decoherent Family............................... 20 3.1a. Logical Conclusions........................... 20 3.1b. Probabilities of Histories........................ 21 3.1c. Causality Paradox........................... 22 3.1d. Approximate Decoherence....................... 24 3.2 Incompatible Sets................................ 25 3.2a. Contradictory Conclusions....................... 25 3.2b. Logic................................... 28 3.2c. Single-Family Rule........................... 30 3.3 Quasiclassical Domains............................. 32 3.4 Many History Interpretation.......................... 34 3.5 Unknown Set Interpretation.......................... 36 4 Applications 36 4.1 EPR Paradox.................................. 36 4.2 Hydrodynamic Variables............................ 41 4.3 Arrival Time Problem............................. 43 4.4 Quantum Fields and Quantum Cosmology.................. 45 5 Summary 48 6 References 51 Appendices 56 A Boolean Algebra 56 B Derivation of Path Integral Method From Operator -
Negative Probability in the Framework of Combined Probability
Negative probability in the framework of combined probability Mark Burgin University of California, Los Angeles 405 Hilgard Ave. Los Angeles, CA 90095 Abstract Negative probability has found diverse applications in theoretical physics. Thus, construction of sound and rigorous mathematical foundations for negative probability is important for physics. There are different axiomatizations of conventional probability. So, it is natural that negative probability also has different axiomatic frameworks. In the previous publications (Burgin, 2009; 2010), negative probability was mathematically formalized and rigorously interpreted in the context of extended probability. In this work, axiomatic system that synthesizes conventional probability and negative probability is constructed in the form of combined probability. Both theoretical concepts – extended probability and combined probability – stretch conventional probability to negative values in a mathematically rigorous way. Here we obtain various properties of combined probability. In particular, relations between combined probability, extended probability and conventional probability are explicated. It is demonstrated (Theorems 3.1, 3.3 and 3.4) that extended probability and conventional probability are special cases of combined probability. 1 1. Introduction All students are taught that probability takes values only in the interval [0,1]. All conventional interpretations of probability support this assumption, while all popular formal descriptions, e.g., axioms for probability, such as Kolmogorov’s -
Are Non-Boolean Event Structures the Precedence Or Consequence of Quantum Probability?
Are Non-Boolean Event Structures the Precedence or Consequence of Quantum Probability? Christopher A. Fuchs1 and Blake C. Stacey1 1Physics Department, University of Massachusetts Boston (Dated: December 24, 2019) In the last five years of his life Itamar Pitowsky developed the idea that the formal structure of quantum theory should be thought of as a Bayesian probability theory adapted to the empirical situation that Nature’s events just so happen to conform to a non-Boolean algebra. QBism too takes a Bayesian stance on the probabilities of quantum theory, but its probabilities are the personal degrees of belief a sufficiently- schooled agent holds for the consequences of her actions on the external world. Thus QBism has two levels of the personal where the Pitowskyan view has one. The differences go further. Most important for the technical side of both views is the quantum mechanical Born Rule, but in the Pitowskyan development it is a theorem, not a postulate, arising in the way of Gleason from the primary empirical assumption of a non-Boolean algebra. QBism on the other hand strives to develop a way to think of the Born Rule in a pre-algebraic setting, so that it itself may be taken as the primary empirical statement of the theory. In other words, the hope in QBism is that, suitably understood, the Born Rule is quantum theory’s most fundamental postulate, with the Hilbert space formalism (along with its perceived connection to a non-Boolean event structure) arising only secondarily. This paper will avail of Pitowsky’s program, along with its extensions in the work of Jeffrey Bub and William Demopoulos, to better explicate QBism’s aims and goals. -
Chapter 1 Probability, Random Variables and Expectations
Chapter 1 Probability, Random Variables and Expectations Note: The primary reference for these notes is Mittelhammer (1999). Other treatments of proba- bility theory include Gallant (1997), Casella and Berger (2001) and Grimmett and Stirzaker (2001). This chapter provides an overview of probability theory as it applied to both discrete and continuous random variables. The material covered in this chap- ter serves as a foundation of the econometric sequence and is useful through- out financial economics. The chapter begins with a discussion of the axiomatic foundations of probability theory and then proceeds to describe properties of univariate random variables. Attention then turns to multivariate random vari- ables and important difference from univariate random variables. Finally, the chapter discusses the expectations operator and moments. 1.1 Axiomatic Probability Probability theory is derived from a small set of axioms – a minimal set of essential assumptions. A deep understanding of axiomatic probability theory is not essential to financial econometrics or to the use of probability and statistics in general, although understanding these core concepts does provide additional insight. The first concept in probability theory is the sample space, which is an abstract concept con- taining primitive probability events. Definition 1.1 (Sample Space). The sample space is a set, Ω, that contains all possible outcomes. Example 1.1. Suppose interest is on a standard 6-sided die. The sample space is 1-dot, 2-dots, . ., 6-dots. Example 1.2. Suppose interest is in a standard 52-card deck. The sample space is then A|, 2|, 3|,..., J |, Q|, K |, A},..., K }, A~,..., K ~, A♠,..., K ♠.