Symplectic Matrix
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Symplectic Matrix

In mathematics, a symplectic matrix is a ${\displaystyle 2n\times 2n}$ matrix ${\displaystyle M}$ with real entries that satisfies the condition

where ${\displaystyle M^{T}}$ denotes the transpose of ${\displaystyle M}$ and ${\displaystyle \Omega }$ is a fixed ${\displaystyle 2n\times 2n}$ nonsingular, skew-symmetric matrix. This definition can be extended to ${\displaystyle 2n\times 2n}$ matrices with entries in other fields, such as the complex numbers, finite fields, p-adic numbers, and function fields.

Typically ${\displaystyle \Omega }$ is chosen to be the block matrix

${\displaystyle \Omega ={\begin{bmatrix}0&I_{n}\\-I_{n}&0\\\end{bmatrix}},}$
where ${\displaystyle I_{n}}$ is the ${\displaystyle n\times n}$ identity matrix. The matrix ${\displaystyle \Omega }$ has determinant ${\displaystyle +1}$ and its inverse is ${\displaystyle \Omega ^{-1}=\Omega ^{T}=-\Omega }$.

## Properties

### Generators for symplectic matrices

Every symplectic matrix has determinant ${\displaystyle +1}$, and the ${\displaystyle 2n\times 2n}$ symplectic matrices with real entries form a subgroup of the general linear group ${\displaystyle GL(2n;\mathbb {R} )}$ under matrix multiplication since being symplectic is a property stable under matrix multiplication. Topologically, this symplectic group is a connected noncompact real Lie group of real dimension ${\displaystyle n(2n+1)}$, and is denoted ${\displaystyle Sp(2n;\mathbb {R} )}$. The symplectic group can be defined as the set of linear transformations that preserve the symplectic form of a real symplectic vector space.

This symplectic group has a distinguished set of generators, which can be used to find all possible symplectic matrices. This includes the following sets

{\displaystyle {\begin{aligned}D(n)=&\left\{{\begin{pmatrix}A&0\\0&(A^{T})^{-1}\end{pmatrix}}:A\in {\text{GL}}(n;\mathbb {R} )\right\}\\N(n)=&\left\{{\begin{pmatrix}I_{n}&B\\0&I_{n}\end{pmatrix}}:B\in {\text{Sym}}(n;\mathbb {R} )\right\}\end{aligned}}}
where ${\displaystyle {\text{Sym}}(n;\mathbb {R} )}$ is the set of ${\displaystyle n\times n}$ symmetric matrices. Then, ${\displaystyle Sp(2n;\mathbb {R} )}$ is generated by the set[1]pg 2
${\displaystyle \{\Omega \}\cup D(n)\cup N(n)}$
of matrices. In other words, any symplectic matrix can be constructed by multiplying matrices in ${\displaystyle D(n)}$ and ${\displaystyle N(n)}$ together, along with some power of ${\displaystyle \Omega }$.

### Inverse matrix

Every symplectic matrix is invertible with the inverse matrix given by

${\displaystyle M^{-1}=\Omega ^{-1}M^{\text{T}}\Omega .}$
Furthermore, the product of two symplectic matrices is, again, a symplectic matrix. This gives the set of all symplectic matrices the structure of a group. There exists a natural manifold structure on this group which makes it into a (real or complex) Lie group called the symplectic group.

### Determinantal properties

It follows easily from the definition that the determinant of any symplectic matrix is ±1. Actually, it turns out that the determinant is always +1 for any field. One way to see this is through the use of the Pfaffian and the identity

${\displaystyle {\mbox{Pf}}(M^{\text{T}}\Omega M)=\det(M){\mbox{Pf}}(\Omega ).}$
Since ${\displaystyle M^{\text{T}}\Omega M=\Omega }$ and ${\displaystyle {\mbox{Pf}}(\Omega )\neq 0}$ we have that ${\displaystyle \det(M)=1}$.

When the underlying field is real or complex, one can also show this by factoring the inequality ${\displaystyle \det(M^{\text{T}}M+I)\geq 1}$.[2]

### Block form of symplectic matrices

Suppose ? is given in the standard form and let ${\displaystyle M}$ be a ${\displaystyle 2n\times 2n}$ block matrix given by

${\displaystyle M={\begin{pmatrix}A&B\\C&D\end{pmatrix}}}$

where ${\displaystyle A,B,C,D}$ are ${\displaystyle n\times n}$ matrices. The condition for ${\displaystyle M}$ to be symplectic is equivalent to the two following equivalent conditions[3]

${\displaystyle A^{\text{T}}C,B^{\text{T}}D}$ symmetric, and ${\displaystyle A^{\text{T}}D-C^{\text{T}}B=I}$

${\displaystyle AB^{\text{T}},CD^{\text{T}}}$ symmetric, and ${\displaystyle AD^{\text{T}}-BC^{\text{T}}=I}$

When ${\displaystyle n=1}$ these conditions reduce to the single condition ${\displaystyle \det(M)=1}$. Thus a ${\displaystyle 2\times 2}$ matrix is symplectic iff it has unit determinant.

#### Inverse matrix of block matrix

With ${\displaystyle \Omega }$ in standard form, the inverse of ${\displaystyle M}$ is given by

${\displaystyle M^{-1}=\Omega ^{-1}M^{\text{T}}\Omega ={\begin{pmatrix}D^{\text{T}}&-B^{\text{T}}\\-C^{\text{T}}&A^{\text{T}}\end{pmatrix}}.}$
The group has dimension ${\displaystyle n(2n+1)}$. This can be seen by noting that ${\displaystyle (M^{\text{T}}\Omega M)^{\text{T}}=-M^{\text{T}}\Omega M}$ is anti-symmetric. Since the space of anti-symmetric matrices has dimension ${\displaystyle {\binom {2n}{2}},}$ the identity ${\displaystyle M^{\text{T}}\Omega M=\Omega }$ imposes ${\displaystyle 2n \choose 2}$ constraints on the ${\displaystyle (2n)^{2}}$coefficients of ${\displaystyle M}$and leaves ${\displaystyle M}$ with ${\displaystyle n(2n+1)}$ independent coefficients.

## Symplectic transformations

In the abstract formulation of linear algebra, matrices are replaced with linear transformations of finite-dimensional vector spaces. The abstract analog of a symplectic matrix is a symplectic transformation of a symplectic vector space. Briefly, a symplectic vector space ${\displaystyle (V,\omega )}$ is a ${\displaystyle 2n}$-dimensional vector space ${\displaystyle V}$ equipped with a nondegenerate, skew-symmetric bilinear form ${\displaystyle \omega }$ called the symplectic form.

A symplectic transformation is then a linear transformation ${\displaystyle L:V\to V}$ which preserves ${\displaystyle \omega }$, i.e.

${\displaystyle \omega (Lu,Lv)=\omega (u,v).}$

Fixing a basis for ${\displaystyle V}$, ${\displaystyle \omega }$ can be written as a matrix ${\displaystyle \Omega }$ and ${\displaystyle L}$ as a matrix ${\displaystyle M}$. The condition that ${\displaystyle L}$ be a symplectic transformation is precisely the condition that M be a symplectic matrix:

${\displaystyle M^{\text{T}}\Omega M=\Omega .}$

Under a change of basis, represented by a matrix A, we have

${\displaystyle \Omega \mapsto A^{\text{T}}\Omega A}$
${\displaystyle M\mapsto A^{-1}MA.}$

One can always bring ${\displaystyle \Omega }$ to either the standard form given in the introduction or the block diagonal form described below by a suitable choice of A.

## The matrix ?

Symplectic matrices are defined relative to a fixed nonsingular, skew-symmetric matrix ${\displaystyle \Omega }$. As explained in the previous section, ${\displaystyle \Omega }$ can be thought of as the coordinate representation of a nondegenerate skew-symmetric bilinear form. It is a basic result in linear algebra that any two such matrices differ from each other by a change of basis.

The most common alternative to the standard ${\displaystyle \Omega }$ given above is the block diagonal form

${\displaystyle \Omega ={\begin{bmatrix}{\begin{matrix}0&1\\-1&0\end{matrix}}&&0\\&\ddots &\\0&&{\begin{matrix}0&1\\-1&0\end{matrix}}\end{bmatrix}}.}$

This choice differs from the previous one by a permutation of basis vectors.

Sometimes the notation ${\displaystyle J}$ is used instead of ${\displaystyle \Omega }$ for the skew-symmetric matrix. This is a particularly unfortunate choice as it leads to confusion with the notion of a complex structure, which often has the same coordinate expression as ${\displaystyle \Omega }$ but represents a very different structure. A complex structure ${\displaystyle J}$ is the coordinate representation of a linear transformation that squares to ${\displaystyle -I_{n}}$, whereas ${\displaystyle \Omega }$ is the coordinate representation of a nondegenerate skew-symmetric bilinear form. One could easily choose bases in which ${\displaystyle J}$ is not skew-symmetric or ${\displaystyle \Omega }$ does not square to ${\displaystyle -I_{n}}$.

Given a hermitian structure on a vector space, ${\displaystyle J}$ and ${\displaystyle \Omega }$ are related via

${\displaystyle \Omega _{ab}=-g_{ac}{J^{c}}_{b}}$

where ${\displaystyle g_{ac}}$ is the metric. That ${\displaystyle J}$ and ${\displaystyle \Omega }$ usually have the same coordinate expression (up to an overall sign) is simply a consequence of the fact that the metric g is usually the identity matrix.

## Diagonalisation and decomposition

• For any positive definite symmetric real symplectic matrix S there exists U in U(2n,R) such that
${\displaystyle S=U^{\text{T}}DU\quad {\text{for}}\quad D=\operatorname {diag} (\lambda _{1},\ldots ,\lambda _{n},\lambda _{1}^{-1},\ldots ,\lambda _{n}^{-1}),}$
where the diagonal elements of D are the eigenvalues of S.[4]
${\displaystyle S=UR\quad {\text{for}}\quad U\in \operatorname {U} (2n,\mathbb {R} ){\text{ and }}R\in \operatorname {Sp} (2n,\mathbb {R} )\cap \operatorname {Sym} _{+}(2n,\mathbb {R} ).}$
• Any real symplectic matrix can be decomposed as a product of three matrices:

such that O and O' are both symplectic and orthogonal and D is positive-definite and diagonal.[5] This decomposition is closely related to the singular value decomposition of a matrix and is known as an 'Euler' or 'Bloch-Messiah' decomposition.

## Complex matrices

If instead M is a 2n×2n matrix with complex entries, the definition is not standard throughout the literature. Many authors [6] adjust the definition above to

where M* denotes the conjugate transpose of M. In this case, the determinant may not be 1, but will have absolute value 1. In the 2×2 case (n=1), M will be the product of a real symplectic matrix and a complex number of absolute value 1.

Other authors [7] retain the definition (1) for complex matrices and call matrices satisfying (3) conjugate symplectic.

## Applications

Transformations described by symplectic matrices play an important role in quantum optics and in continuous-variable quantum information theory. For instance, symplectic matrices can be used to describe Gaussian (Bogoliubov) transformations of a quantum state of light.[8] In turn, the Bloch-Messiah decomposition (2) means that such an arbitrary Gaussian transformation can be represented as a set of two passive linear-optical interferometers (corresponding to orthogonal matrices O and O' ) intermitted by a layer of active non-linear squeezing transformations (given in terms of the matrix D).[9] In fact, one can circumvent the need for such in-line active squeezing transformations if two-mode squeezed vacuum states are available as a prior resource only.[10]

## References

1. ^ Habermann, Katharina, 1966- (2006). Introduction to symplectic Dirac operators. Springer. ISBN 978-3-540-33421-7. OCLC 262692314.CS1 maint: multiple names: authors list (link)
2. ^ Rim, Donsub (2017). "An elementary proof that symplectic matrices have determinant one". Adv. Dyn. Syst. Appl. 12 (1): 15-20. arXiv:1505.04240. Bibcode:2015arXiv150504240R. doi:10.37622/ADSA/12.1.2017.15-20.
3. ^ de Gosson, Maurice. "Introduction to Symplectic Mechanics: Lectures I-II-III" (PDF).
4. ^ a b de Gosson, Maurice A. (2011). Symplectic Methods in Harmonic Analysis and in Mathematical Physics - Springer. doi:10.1007/978-3-7643-9992-4. ISBN 978-3-7643-9991-7.
5. ^ Ferraro et. al. 2005 Section 1.3. ... Title?
6. ^ Xu, H. G. (July 15, 2003). "An SVD-like matrix decomposition and its applications". Linear Algebra and Its Applications. 368: 1-24. doi:10.1016/S0024-3795(03)00370-7. hdl:1808/374.
7. ^ Mackey, D. S.; Mackey, N. (2003). "On the Determinant of Symplectic Matrices". Numerical Analysis Report. 422. Manchester, England: Manchester Centre for Computational Mathematics. Cite journal requires |journal= (help)
8. ^ Weedbrook, Christian; Pirandola, Stefano; García-Patrón, Raúl; Cerf, Nicolas J.; Ralph, Timothy C.; Shapiro, Jeffrey H.; Lloyd, Seth (2012). "Gaussian quantum information". Reviews of Modern Physics. 84 (2): 621-669. arXiv:1110.3234. Bibcode:2012RvMP...84..621W. doi:10.1103/RevModPhys.84.621.
9. ^ Braunstein, Samuel L. (2005). "Squeezing as an irreducible resource". Physical Review A. 71 (5): 055801. arXiv:quant-ph/9904002. Bibcode:2005PhRvA..71e5801B. doi:10.1103/PhysRevA.71.055801.
10. ^ Chakhmakhchyan, Levon; Cerf, Nicolas (2018). "Simulating arbitrary Gaussian circuits with linear optics". Physical Review A. 98 (6): 062314. arXiv:1803.11534. Bibcode:2018PhRvA..98f2314C. doi:10.1103/PhysRevA.98.062314.