How statement and proof provenance work
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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Qm Math Composites — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Analyticity of Holomorphic Functions; Liouville and Morera
- Approximation and Compactness in C(K)
- Arc Length and Rectifiable Curves
- Banach Alaoglu Goldstine and Krein Milman
- Banach Algebras Spectrum and Holomorphic Functional Calculus
- Banach Valued Integration and the Radon Nikodym Property
- Binary Operations, Monoids, Groups and Subgroups
- Bounded Linear Operators and Quotient Spaces
- Bounded Variation and the Riemann–Stieltjes Integral
- Compact Operators and Riesz Schauder Theory
- Compact Self Adjoint Hilbert Schmidt and Trace Class Operators
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Complex Differentiability and the Cauchy–Riemann Equations
- Complex Power Series and Analytic Functions
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Connectedness
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Continuous Functional Calculus for Self Adjoint and Normal Operators
- Contour Integration
- Convergence: Nets and Filters
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces Adjoint Operators and Annihilators
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite Dimensional Normed Spaces and Riesz Lemma
- Foundations of the Real Numbers for Analysis
- Free Modules, Exact Sequences, Projective and Injective Modules
- Function Space Topologies and the Exponential Law
- Fundamental Trigonometric Identities
- Gelfand Theory and Commutative C Star Algebras
- Goursat's Theorem and Cauchy's Theorem in a Convex Domain
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Hausdorff via the Diagonal
- Hilbert Space Geometry and Riesz Representation
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Line Integrals and the Gradient Theorem
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Metric Spaces
- Modules, Submodules, Quotient Modules and the Isomorphism Theorems
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Normed and Banach Spaces
- Norming and Separation under Hahn–Banach
- Order, Zorn's Lemma, and the Axiom of Choice
- Orthonormal Bases, Parseval and Fourier Series
- Outer Measure and the Caratheodory Extension Theorem
- Partitions of Unity and Paracompactness
- pi: the Equivalent Characterizations
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Properties of the Integral and the Working FTC
- Qm Math Composites
- Radon Measures and the Riesz Markov Kakutani Theorem
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Separation Axioms: the Hierarchy
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Signed and Complex Measures Hahn and Jordan
- Simple Field Extensions and the Construction of the Complex Numbers
- Sine, Cosine, and the Definition of Pi
- Spectral Measures and Borel Functional Calculus
- Splitting Fields
- Square-Integrable Kernels and Hilbert–Schmidt Compactness
- Stone–Weierstrass in General
- Subspaces, Products, and Quotients
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- Tensor Products of Modules
- The Analytic Hahn Banach Theorem
- The Baire Principles of Functional Analysis
- The Cantor Set, Baire Category, and Measure Zero in ℝ
- The Complex Exponential and Euler's Formula
- The Derivative and the Mean Value Theorems
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Exponential Function
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Fundamental Theorems of Calculus
- The Galois Correspondence
- The Lebesgue Integral and the Convergence Theorems
- The Lᵖ Spaces Holder Minkowski and Riesz Fischer
- The Riemann Integral: Definition and Integrability
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The Topology of Euclidean Space
- The Total Derivative in ℝᵐ → ℝⁿ
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Unbounded Self Adjoint Operators and Stones Theorem
- Urysohn's Lemma and the Tietze Extension Theorem
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
The companion page to qm-math-composites records three explicit
mathematical checks behind its tensor, density and instrument results. All
three items are mathematics: they carry domain: mathematics, depend only on
the mathematical items of the parent page and on mathematical suppliers, and
make no physical or empirical claim.
Bell reduced state computes the reduced states of the unit vector in : the coefficient matrix is , both partial traces are , the reduced purity is while , the Schmidt coefficients are , and is not a product vector. Nonunique mixed ensemble shows that the maximally mixed operator on has the two distinct orthonormal decompositions and , so this density operator has two distinct orthonormal convex decompositions into rank-one density operators. Selective-conditioning counterexample refutes the selective claim that local conditioning preserves the opposite reduced state: on the same Bell state, the Lüders outcome of the second factor with has probability and leaves the first factor in , which differs from the unconditional , while the nonselective sum over both outcomes does return as in Local trace-preserving Kraus maps do not change the opposite reduced state — the counterexample therefore separates the nonselective identity from its selective counterpart rather than contradicting the former.
3 · Logical flowchart
4 · Definitions, postulates, theorems and proofs
None yet.
5 · Examples, experiments and counterexamples
Bell reduced state
Example
Let with the standard inner product and orthonormal basis , (Hilbert tensor construction: inner product, completion and product bases), and let
Here a product vector means an elementary tensor with and ; is the rank-one density operator determined by the unit vector .
Then:
(a) , and the coefficient matrix of in the bases is ;
(b) the partial traces satisfy and , so both reduced states are the maximally mixed matrix ;
(c) the reduced density has eigenvalues and trace-square , while ; the Schmidt coefficients of are , and is not a product vector.
Facts & Assumptions
Given: with the standard inner product; the orthonormal basis ; the vector above; the operators on the finite-dimensional spaces.
On the pairing is ; the four elementary tensors form an orthonormal basis, each has a unique expansion with , and the basis-free conventions (linearity in the first argument, conjugate symmetry) are those of Hilbert tensor construction: inner product, completion and product bases.
For endomorphisms of there is a unique in with for all ; it is linear and positive, where is the coefficient map determined by , and is defined symmetrically; the Schmidt decomposition has orthonormal families and positive equal to the positive singular values of , with , and for the reduced density satisfies , which equals exactly when (Finite partial trace, Schmidt decomposition and finite no-signalling(a),(b),(c)).
is trace class with , and is linear (Rank-one operator calculus).
In an orthonormal basis of a finite-dimensional inner product space the matrix of is the column-times-row matrix , so ; for rank-one operators.
Verification
[Coefficient matrix and norm] By [A1], is the expansion with , and the other coefficients , so the coefficient map is the matrix in the bases (columns indexed by ), and .
[Partial traces] Computing and , [A2] gives and, by symmetry, ; both are positive with trace , as they must be for the reduction of a unit vector.
[Purity and Schmidt coefficients] The matrix has the single eigenvalue with multiplicity (in any basis its matrix is diagonal with entries ), so for ; the positive singular values of are the square roots of the eigenvalues of , namely , so the Schmidt decomposition of has terms and , in agreement with the norm formula of [A2]; since , is not a product vector. Finally because by [A4] and , so by [A3].
Nonunique mixed ensemble
Example
On with the standard inner product and orthonormal basis , , put (Density operators and scalar spectral probability). Then the maximally mixed density operator admits the two orthonormal convex decompositions
where and are distinct orthonormal bases of . Thus the same density operator has two distinct orthonormal convex decompositions into rank-one density operators.
Facts & Assumptions
Given: with the standard inner product; the orthonormal basis ; the unit vectors ; the operators .
A density operator is a self-adjoint positive trace-class operator with trace (Density operators and scalar spectral probability(a), definition only). For a finite family of unit vectors and weights with , set . This is a finite matrix and hence trace class; it is self-adjoint, , and its finite trace is . Thus it is a density operator even without orthogonality. This is a local finite-matrix calculation, not an invocation of the supplier's Countable Choice expansion.
is trace class with and ; the trace is linear on trace-class operators (Rank-one operator calculus). In the present these facts follow directly from : the diagonal sum is , and has at most one nonzero eigenvalue, , so its singular-value sum is . Finite diagonal sums are linear. No choice principle is used in these finite computations.
In an orthonormal basis of the matrix of is the column-times-row matrix ; and therefore have matrices and .
Verification
[Computational basis decomposition] By [A3], has matrix , and by [A2] it is a trace-class operator of trace ; by [A1] it is a density operator coming from the orthonormal family with weights .
[Fixed basis decomposition] The vectors are unit and orthogonal since ; their matrices are and , so again, with weights on an orthonormal family; thus the same operator has a second decomposition.
[The decompositions are different] The two orthonormal families are distinct: , so is not a scalar multiple of and is not one of (their matrices of step 2.1 differ from ); hence the two lists of rank-one density operators are not the same up to reordering, while both convex combinations equal the single operator by steps 1.1 and 2.1. Therefore this density operator has a nonunique orthonormal convex decomposition into rank-one density operators, consistent with the expansion in [A1].
Remarks
- The operator identity is basis-independent. Both decompositions define the same matrix , so for every endomorphism of .
- Nonuniqueness needs no optimization. The two decompositions here are already orthonormal and vary only in the choice of basis; no operational preparation procedure is specified.
Selective-conditioning counterexample
Statement refuted
False claim: for every density operator on a tensor product of finite-dimensional complex inner product spaces and every finite local family on with , the conditional reduced state on after the outcome is again whenever its probability is positive.
The claim confuses the nonselective identity of Local trace-preserving Kraus maps do not change the opposite reduced state with the corresponding statement for one selected outcome. The displayed unit vector in and one outcome of the explicitly defined projection instrument below refute the selective claim.
Facts & Assumptions
Given: with orthonormal bases ; the unit vector ; the density operator with ; the rank-one projections and on .
On the pairing is ; the four tensors form an orthonormal basis, , and is a density operator with (Hilbert tensor construction: inner product, completion and product bases, Density operators and scalar spectral probability(a)); a partial trace is the unique linear map with , it is linear, and for the coefficient map of (Finite partial trace, Schmidt decomposition and finite no-signalling(a),(b)).
is linear, and for endomorphisms of with the nonselective sum satisfies for every endomorphism ; the lemma covers only the nonselective sum and says nothing about one selected summand (Finite partial trace, Schmidt decomposition and finite no-signalling(d), Local trace-preserving Kraus maps do not change the opposite reduced state).
The refuted claim: for every unit and every outcome of a local family with , the conditional reduced state equals whenever the probability is positive.
Counterexample
[The instrument] On take the self-adjoint projections and , so that for and : this is the stipulated Lüders instrument for the sharp observable . Its outcome- non-normalized conditional operator on is .
[The selected outcome] Let . Its coefficient map in the chosen bases has matrix . The outcome probability is , and the non-normalized conditional operator is . After normalization the conditional state is .
[The conditional reduced state differs] The coefficient map of the unit vector has one nonzero matrix entry, equal to in the row and column, so its product with its adjoint is . By [L1], the conditional reduced state on is therefore . The unconditional reduced state is for the coefficient matrix of , namely . These differ, since . Hence the selective conditional reduced state is not the unconditional one, and the claim of [L3] fails for this unit vector and instrument.
[The nonselective identity still holds] The outcome- summand is , and , so this summand equals . The coefficient map of the unit vector has one nonzero entry in the row, so its reduced state on is by [L1]. Adding both summands and using the linearity of [L2] gives , in agreement with the nonselective identity of [L2]. The refuted claim is thus exactly the illegitimate replacement of that nonselective identity by its selective counterpart.
Sources
- MIT 8.05 Quantum Physics II (Fall 2013), Chapter 8 'Multiparticle States and Tensor Products', §§1-3
- J. Binney and D. Skinner, The Physics of Quantum Mechanics, §6.1 pp. 105-115
- J. Binney and D. Skinner, The Physics of Quantum Mechanics, §6.3 pp. 121-128 (density operators and their nonunique decompositions)
- D. Skinner, Principles of Quantum Mechanics (Cambridge Part II, Michaelmas 2021), Chapter 10 'Interpreting Quantum Mechanics', pp. 1-12
- J. Binney and D. Skinner, The Physics of Quantum Mechanics, §6.3 pp. 121-128
- MIT 8.05 Quantum Physics II (Fall 2013), Chapter 4 'Dirac's Bra and Ket Notation'