Quantum Measurement Trees, II: Quantum Observables as Ortho-Measurable Functions and Density Matrices as Ortho-Probability Measures

Peter J Hammond
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Peter J Hammond: University of Warwick

The Warwick Economics Research Paper Series (TWERPS) from University of Warwick, Department of Economics

Abstract: Given a quantum state in the finite-dimensional Hilbert space Cn, the range of possible values of a quantum observable is usually identified with the discrete spectrum of eigenvalues of a corresponding Hermitian matrix. Here any such observable is identified with : (i) an ortho-measurable function de ned on the Boolean ortho-algebra generated by the eigenspaces that form an orthogonal decomposition of Cn ; (ii) a numerically identified orthogonal decomposition of Cn. The latter means that each subspace of the orthogonal decomposition can be uniquely identified by its own attached real number, just as each eigenspace of a Hermitian matrix can be uniquely identified by the corresponding eigenvalue. Furthermore, any density matrix on Cn is identified with a Bayesian prior ortho-probability measure defined on the linear subspaces that make up the Boolean ortho-algebra induced by its eigenspaces. Then any pure quantum state is identified with a degenerate density matrix, and any mixed state with a probability measure on a set of orthogonal pure states. Finally, given any quantum observable, the relevant Bayesian posterior probabilities of measured outcomes can be found by the usual trace formula that extends Born's rule

Keywords: Quantum measurement trees; quantum contexts; numerically identified orthogonal decompositions; ortho-measurable functions; density matrices; ortho-probability measures. (search for similar items in EconPapers)
Date: 2025
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