Schmidt decomposition

{{Short description|Process in linear algebra}}

{{Use American English|date = February 2019}}

{{Use mdy dates|date = February 2019}}

In linear algebra, the Schmidt decomposition (named after its originator Erhard Schmidt) refers to a particular way of expressing a vector in the tensor product of two inner product spaces. It has numerous applications in quantum information theory, for example in entanglement characterization and in state purification, and plasticity.

Theorem

Let H_1 and H_2 be Hilbert spaces of dimensions n and m respectively. Assume n \geq m. For any vector w in the tensor product H_1 \otimes H_2, there exist orthonormal sets \{ u_1, \ldots, u_m \} \subset H_1 and \{ v_1, \ldots, v_m \} \subset H_2 such that w= \sum_{i =1} ^m \alpha _i u_i \otimes v_i, where the scalars \alpha_i are real, non-negative, and unique up to re-ordering.

=Proof=

The Schmidt decomposition is essentially a restatement of the singular value decomposition in a different context. Fix orthonormal bases \{ e_1, \ldots, e_n \} \subset H_1 and \{ f_1, \ldots, f_m \} \subset H_2. We can identify an elementary tensor e_i \otimes f_j with the matrix e_i f_j ^\mathsf{T}, where f_j ^\mathsf{T} is the transpose of f_j. A general element of the tensor product

:w = \sum _{1 \leq i \leq n, 1 \leq j \leq m} \beta _{ij} e_i \otimes f_j

can then be viewed as the n × m matrix

:\; M_w = (\beta_{ij}) .

By the singular value decomposition, there exist an n × n unitary U, m × m unitary V, and a positive semidefinite diagonal m × m matrix Σ such that

:M_w = U \begin{bmatrix} \Sigma \\ 0 \end{bmatrix} V^* .

Write U =\begin{bmatrix} U_1 & U_2 \end{bmatrix} where U_1 is n × m and we have

:\; M_w = U_1 \Sigma V^* .

Let \{ u_1, \ldots, u_m \} be the m column vectors of U_1, \{ v_1, \ldots, v_m \} the column vectors of \overline{V}, and \alpha_1, \ldots, \alpha_m the diagonal elements of Σ. The previous expression is then

:M_w = \sum _{k=1} ^m \alpha_k u_k v_k ^\mathsf{T} ,

Then

:w = \sum _{k=1} ^m \alpha_k u_k \otimes v_k ,

which proves the claim.

Some observations

Some properties of the Schmidt decomposition are of physical interest.

=Spectrum of reduced states=

Consider a vector w of the tensor product

:H_1 \otimes H_2

in the form of Schmidt decomposition

:w = \sum_{i =1} ^m \alpha _i u_i \otimes v_i.

Form the rank 1 matrix \rho = w w^* . Then the partial trace of \rho , with respect to either system A or B, is a diagonal matrix whose non-zero diagonal elements are | \alpha_i|^2 . In other words, the Schmidt decomposition shows that the reduced states of \rho on either subsystem have the same spectrum.

=Schmidt rank and entanglement=

The strictly positive values \alpha_i in the Schmidt decomposition of w are its Schmidt coefficients, or Schmidt numbers. The total number of Schmidt coefficients of w, counted with multiplicity, is called its Schmidt rank.

If w can be expressed as a product

:u \otimes v

then w is called a separable state. Otherwise, w is said to be an entangled state. From the Schmidt decomposition, we can see that w is entangled if and only if w has Schmidt rank strictly greater than 1. Therefore, two subsystems that partition a pure state are entangled if and only if their reduced states are mixed states.

=Von Neumann entropy=

A consequence of the above comments is that, for pure states, the von Neumann entropy of the reduced states is a well-defined measure of entanglement. For the von Neumann entropy of both reduced states of \rho is -\sum_i |\alpha_i|^2 \log\left(|\alpha_i|^2\right), and this is zero if and only if \rho is a product state (not entangled).

Schmidt-rank vector

The Schmidt rank is defined for bipartite systems, namely quantum states

|\psi\rangle \in H_A \otimes H_B

The concept of Schmidt rank can be extended to quantum systems made up of more than two subsystems.{{Cite journal|last1=Huber|first1=Marcus|last2=de Vicente|first2=Julio I.|date=2013-01-14|title=Structure of Multidimensional Entanglement in Multipartite Systems|url=https://link.aps.org/doi/10.1103/PhysRevLett.110.030501|journal=Physical Review Letters|language=en|volume=110|issue=3|pages=030501|doi=10.1103/PhysRevLett.110.030501|pmid=23373906|arxiv=1210.6876|bibcode=2013PhRvL.110c0501H|s2cid=44848143|issn=0031-9007}}

Consider the tripartite quantum system:

|\psi\rangle \in H_A \otimes H_B \otimes H_C

There are three ways to reduce this to a bipartite system by performing the partial trace with respect to H_A, H_B or H_C

\begin{cases}

\hat{\rho}_A = Tr_A(|\psi\rangle\langle\psi|)\\

\hat{\rho}_B = Tr_B(|\psi\rangle\langle\psi|)\\

\hat{\rho}_C = Tr_C(|\psi\rangle\langle\psi|)

\end{cases}

Each of the systems obtained is a bipartite system and therefore can be characterized by one number (its Schmidt rank), respectively r_A, r_B and r_C. These numbers capture the "amount of entanglement" in the bipartite system when respectively A, B or C are discarded. For these reasons the tripartite system can be described by a vector, namely the Schmidt-rank vector

\vec{r} = (r_A, r_B, r_C)

= [[Multipartite entanglement|Multipartite systems]] =

The concept of Schmidt-rank vector can be likewise extended to systems made up of more than three subsystems through the use of tensors.

See also

References

{{Reflist}}

Further reading

  • {{cite book |first=Anirban |last=Pathak |title=Elements of Quantum Computation and Quantum Communication |location=London |publisher=Taylor & Francis |year=2013 |isbn=978-1-4665-1791-2 |url={{Google books |plainurl=yes |id=cEPSBQAAQBAJ |page=92}} |pages=92–98 }}

{{DEFAULTSORT:Schmidt Decomposition}}

Category:Linear algebra

Category:Singular value decomposition

Category:Quantum information theory

Category:Articles containing proofs