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Pure-State Entanglement Across a Tensor Cut
Definition
Let be nonzero finite-dimensional complex inner-product spaces, with the ordered tensor product . A product ray is a ray containing a nonzero decomposable vector with and . A pure state on this cut is entangled when its ray is not a product ray.
For a nonzero vector , its Schmidt rank is the matrix rank of its coefficient matrix in any ordered orthonormal product basis; the verification proves this is independent of that basis. It also proves that this rank equals the number of positive terms in every Schmidt decomposition. Thus a pure ray is entangled exactly when its Schmidt rank exceeds one.
Facts & Assumptions
Given: Nonzero finite-dimensional complex inner-product spaces , their product inner product and ordered tensor convention, and a unit vector .
Pure state and ray defines a pure state as the equivalence class of a nonzero vector under multiplication by a nonzero complex scalar. Its current draft definition body was inspected; only this ray convention is used below, not its separate projector assertion.
Ordered tensor registers, their inner product, and wire permutations defines the ordered product basis and product inner product on elementary tensors. Its current draft proof was read; the basis construction, pairing, basis independence and permutation claims are derived from the listed tensor-product and coordinate interfaces.
Schmidt decomposition states that every tensor has a Schmidt expansion with positive coefficients and orthonormal factor families, whose coefficients are the positive singular values of the product-basis coefficient matrix. Its full current draft proof was read, along with the current tensor-register and Dirac-conversion definition bodies; this records body inspection, not acceptance of those draft suppliers.
Row space, column space, nullspace, row rank, column rank and matrix rank: the matrix rank is the dimension of the row space, and row rank equals column rank by Row rank equals column rank, and both equal the number of pivots.
The rank of a matrix equals the rank of the linear map : a matrix has the same rank as its associated coordinate linear map.
Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition: every finite-dimensional map has an SVD with exactly its positive singular values.
The rank of a linear map is the number of its nonzero singular values: the number of positive singular values of a finite-dimensional map equals its rank.
Every finite-dimensional real or complex inner product space has an orthonormal basis: every finite-dimensional inner-product space has an orthonormal basis.
Verification
Choose ordered orthonormal bases of using [F5], and let be the coefficient matrix of in their product basis [L2]. For any other ordered orthonormal bases, let have the new basis vectors as columns in the old coordinates. Their columns are orthonormal, so the finite row-by-column identities hold; because they are basis changes, both matrices are invertible and hence unitary. Expanding the new product basis vectors in the old one and using conjugate-linearity in the second argument gives , so the new coefficient matrix is . The factors and are invertible, so the map represented by is , where is the map represented by . Since is onto, its image is ; since is injective, this image has the same dimension as . By [F2], the matrix rank is therefore unchanged, so it is basis-independent. Multiplying by any nonzero scalar multiplies by that scalar and also preserves its rank.
In any Schmidt decomposition with all and orthonormal factor families, tensor coordinates multiply, so its coefficient matrix is , where the columns of are the coordinates of the and . The orthonormal columns give , so is injective; conjugating gives , so is surjective; and is invertible. Thus . Conversely, [L3] constructs such a Schmidt decomposition from the SVD, and [F3] and [F4] show that its number of positive coefficients is the rank of . Therefore the number of terms is the same for every Schmidt decomposition and equals the defined Schmidt rank.
If for nonzero , its coefficient matrix is for the coordinate columns , so its rank is one. Conversely, if has rank one, choose a nonzero column ; every column is a scalar multiple , so with . The coefficient expansion then gives for the vectors with coordinates . Thus the ray is a product ray exactly when the Schmidt rank is one.
Since , its coefficient matrix is nonzero and has positive rank. By the product-ray equivalence in step 2.1, failure to be a product ray is equivalent to Schmidt rank greater than one, which is precisely entanglement by the Definition.
Depends on
- Ordered tensor registers, their inner product, and wire permutations
- Schmidt decomposition
- Pure state and ray
- Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition
- The rank of a matrix equals the rank of the linear map $x\mapsto Ax$
- The rank of a linear map is the number of its nonzero singular values
- Row space, column space, nullspace, row rank, column rank and matrix rank
- Row rank equals column rank, and both equal the number of pivots
- Every finite-dimensional real or complex inner product space has an orthonormal basis
Used by
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Sources
- Ronald de Wolf, Quantum Computing: Lecture Notes, arXiv:1907.09415v6 (standard reference, not scraped)
- John Preskill, Quantum Computation, Chapter 2: Foundations I: States and Ensembles (standard reference, not scraped)