1. Introduction
In recent years, quantum systems based on continuous variables have attracted great attention for the development of quantum information transmission and processing [
1,
2,
3]. A large amount of theoretical work has been dedicated to multimode quantum states and transformations, with the prominence of Gaussian states and Gaussian transformations which can be more easily implemented and manipulated. In particular, the multimode case is attracting a lot of interest, since it may exhibit entanglement, which is a key resource for several quantum protocols and applications, such as teleportation, computing, and cryptography.
An efficient approach dealing with Gaussian unitaries and Gaussian states is based on the decomposition of the so-called
fundamental Gaussian unitaries (FGUs), which are given by displacement, rotation, and squeezing, as shown in
Figure 1.
Combination of these three FGUs allows for the implementation of the whole class of multimode Gaussian unitaries and the generation of the whole class of multimode Gaussian states, arriving at explicit closed-form results [
4]. Another important approach is obtained resorting to the Bloch–Messiah reduction [
1,
5] in which the multimode squeezer is decomposed into the parallel of local and real single-mode squeezers, as shown in
Figure 2.
There are several tools to efficiently describe Gaussian unitaries/states. For Gaussian unitaries, the main tools are given by Bogoliubov and symplectic transformations; in the N-mode, the former are specified by two complex matrices of order N, while the latter are specified by a real matrix. For Gaussian states, the main tool is given by the covariance matrix, a symmetric matrix with real elements (the mean vector is often neglected in the analysis).
A key role is played by the
specification of the variables involved, which determine the degree of freedom of Gaussian unitary/states. For the decomposition of
Figure 1, the variables are: the rotation matrix
, the squeeze matrix
, and the displacement vector
; this is the usual specification for a unitary matrix, which we call for convenience the
algebraic specification. For the decomposition of
Figure 2, the variables are: two rotation matrices,
and
, a real diagonal squeeze matrix
, and the displacement vector
which we call
structural or
physical specification. Since a Gaussian state is obtained by processing a multimode thermal state through a Gaussian unitary, in order to characterize a Gaussian state, we have to add the
N single mode thermal states in the specification.
However, with the algebraic specification, the first step is the evaluation of the polar decomposition of the squeeze matrix . The formula contains radicals of radicals, which “propagate” on all the subsequent elaborations, leading to cumbersome formulas. This is completely avoided if we start from the structural specification.
The target of the paper is the implementation of Gaussian unitaries/states developing the cascade of
Figure 2 with
primitive components, starting from the structural specification and using the technique of Bloch–Messiah reduction. The primitive components are rotations, squeezers, displacements in the single mode, and beam splitters [
6]. The architecture thus obtained allows one to obtain an insight into the physical meaning of each component involved. Moreover, following the implementation architecture, it is possible to formulate an (radical–free) easy algebra for the main operations and transformations of Gaussian states.
This paper is organized as follows. In
Section 2, we formulate the Gaussian unitaries and their decompositions in FUGs, according to the Bloch–Messiah (BM) reduction. In
Section 3, we formulate Gaussian states according to Williamson’s theorem.
Section 4 deals with the implementation of Gaussian unitaries with primitive components, where the squeeze matrix is decomposed according to the Takagi factorization [
7]. The implementation could be considered for multimode [
6], but for simplicity, it is carried out for the two-mode one. Continuing with the two-mode, from the architecture with primitive components in
Section 5, we evaluate the symplectic transformations and in
Section 6, the covariance matrix. As we will realize, all the results are simple and radical free (for comparison, the same evaluation is carried out in
Appendix A using the algebraic approach). In the final part of the paper, we focus our attention on the covariance matrix, in which the two-mode depends on 10 real variables. Examples of application are outlined by fixing the variables to specific values. Furthermore, in
Section 8, we consider the so-called
standard form of the covariance, which only depends on four real variables (symplectic invariants) and contains all the relevant information on the Gaussian state, particularly the entanglement property. We outline a simple method for the evaluation of the symplectic invariants of arbitrary Gaussian states in the two mode.
3. Gaussian States
Gaussian states can be obtained from a Gaussian unitary driven by
thermal states; in particular, pure Gaussian states are obtained when the thermal states degenerate into vacuum states. To formalize this statement, it is convenient to recall that a Gaussian state is completely described by the covariance matrix and the mean vector. In particular, the
covariance matrix can always be written in the form (Williamson’s theorem)
where
S is an
N-mode symplectic matrix and
corresponds to a tensor product of
N thermal states with an average number of thermal photons
The quantities are called the symplectic eigenvalues of the CM and the operation performed by the matrix is said to be the symplectic diagonalization of .
With reference to the previous decompositions of the Gaussian unitaries shown in
Figure 1 and
Figure 2, we find that, when the input is driven by
N thermal states, at the output, we obtain the most general
N-mode Gaussian state.
Note that a decomposition into unitaries of the Lie group
has also been used in [
9,
10] for the dynamical symmetry group describing the vibronic transitions in polyatomic molecules.
3.1. Specification and Degree of Freedom
To the specification of the Gaussian unitary, one has to add the specification of the input thermal noise, given by
N real variables, as in (
9). On the other hand, we have a reduction of the degrees of freedom because a rotation operator is irrelevant for the input thermal noise, such as the rotation
of
Figure 1, or the rotation
of
Figure 2. Then, the degree of freedom of an
N-mode Gaussian state results in
3.2. Gaussian States in the Two-Mode
Since we focus on two-mode Gaussian states, we review in detail their specification, which is given by the complex matrices (see
Figure 1)
and two thermal noises
.
From (
8), a two-mode Gaussian
unitary has 14 real variables as degrees of freedom, while from (
12), a two-mode Gaussian
state has 12 real variables degrees of freedom.
We want to evaluate the quantities involved in the two-mode in terms of the parameters given by (
13).
6. Evaluation of the Covariance Matrix (CM)
This is an important topic considered by several authors, e.g., [
13,
14,
15,
16,
17]. In particular, [
15] uses a similar approach to characterize Gaussian states, based on Williamson’s theorem and Bloch-Messiah decomposition, describing the symplectic transformations that correspond to the fundamental operators of squeezing, rotation and beam splitters. However, the final results are only provided for the single-mode and a couple of specific examples of two-mode states. In [
13], the integration within an ordered product (IWOP) is used to express the covariance matrix of N-mode in terms of the squeeze matrix. Note, however, that the hyperbolic tangent of the squeeze matrix must be evaluated. The final derivation is obtained in two specific examples for the two-mode case. Ref. [
14] derives the elements of the covariance matrix of a two-mode Gaussian state, with a generalized squeezing, by using the technique of integration within an ordered product (IWOP). The objective is to characterize the entanglement and non-locality properties of the corresponding state.
Here, we explicitly evaluate the CV
in the two-mode following the structural approach, where
is evaluated from the interlaced SM by adding the information on thermal noise (see (
9))
For the explicit evaluation of the trigonometric matrices in the two-mode appearing in the SM
, we use Proposition 1 and (
16), with
, starting from the exponential
Analogously
where
p is the reflectivity of the second BS and
.
In order to characterize the CM, it is more convenient to express the result partitioned into
blocks. Letting
we obtain:
where
Considering that the CM refers to Gaussian states, as remarked above in
Figure 6, the dependence is on the 10 real parameters
, notwithstanding that the
also depend on the phases
.
A Gaussian state then depends on 10 real complex parameters, namely , , s, , , , , , p, , , .
6.1. Standard Form of the Covariance Matrix
We remind that, for any two-mode Gaussian states, there exists a local symplectic operation
that brings the covariance matrix to the
standard form, Simon2000, [
18]
where the correlation terms
and
are obtained from the ordinary CM
by the four
local symplectic invariantsThe importance of the standard form and of the symplectic invariants lies on the fact that they concentrate all the relevant information on the two-mode Gaussian states, particularly that concerning the entanglement [
19]. This means that the essential degree of freedom is reduced to 4 real variables, instead of the 10 variables listed above.
Evaluation of “invariants” ) from the ordinary CM . The invariants can be obtained from the blocks of the ordinary (nonstandard) covariance matrix (see (
30)).
Proposition 2. From the blocks of the ordinary covariance matrix , evaluate the quantities Then, the invariants result in: , , and We give a numerical example. With the data
6.2. Physical Analysis of the Global Architectures
The architectures of
Figure 5 and
Figure 6 allow us to obtain a physical insight into the dependence of the global performance, as stated by the symplectic matrix and by the covariance matrix, from each primitive component. In the limit, we can remove a single component from the architecture to see the effect on the ordinary covariance matrix
. In a forthcoming paper [
20], we will show that a quantum state, having as ordinary covariance just the standard covariance matrix
, can be obtained by removing all the shifters. This shows that the shifters have less relevance with respect to the other primitive components.
7. Examples
By choosing the 14 parameters listed above, one can evaluate the description of all Gaussian unitaries in the two-mode. Analogously, by choosing the 10 parameters of
Figure 6, one can evaluate the description of all two-mode Gaussian states. Here, we outline a few cases of Gaussian states focusing the attention on the covariance matrix.
Case 1: All-zero phases
A first case is considered with all zero phases, with parameters as in
Table 1.
The squeeze matrix results in
The blocks which constitute the covariance matrix
are given by
where
Case 2: All–zero phases, , and the second BS balanced
We consider another case, with all zero phases, the same squeezing on each mode and balanced BS, as in
Table 2.
The squeeze matrix results in
The blocks of the covariance matrix
are given by
Case 3: EPR state with noise
The EPR unitary is a squeeze with the following matrix
To obtain this unitary, the primitive-component architecture must have the following data
, balanced BSs
and the phases indicated in
Table 3.
The blocks of the covariance matrix
result in
In particular, for
, the block
results in
in agreement with the result indicated in [
19].
9. Conclusions
For the description of Gaussian unitaries/states, we considered the algebraic and structural approaches. We remark that the class of noisy Gaussian states generated by the algebraic specification and by the structural specification coincide, since both approaches generate the whole class of noisy Gaussian states. In other words, one can choose the first class as well as the second class as the definition of noisy Gaussian states. In terms of performance, both the methods and also alternative solutions proposed in [
13,
14,
15] achieve the same covariance matrix, since no approximations are introduced, but at different costs, in terms of complexity and often only in some particular cases. In particular, in [
15], the solution is provided only for a couple of specific examples. In [
13], the covariance matrix is obtained considering only a squeezed state, not a general one, by means of integration within an ordered product (IWOP), starting from the knowledge of the correlation operator. Note also that the hyperbolic tangent of the squeeze matrix must be evaluated. Furthermore, in [
14], only the squeezing is considered, again with the same complexity of IWOP, and the objective is to characterize the entanglement of the corresponding state.
The main target of the paper is to show the advantages of the structural approach in the description of the most generic Gaussian states, namely:
The structural approach is completely radical free compared to the algebraic approach and requires several matrix operations leading to results that contain the radicals of radicals. Note that the key to avoiding radicals is the following: the quantities which exhibit radicals in the algebraic approach become independent variables (data) in the structural approach.
The structural approach is completely general, while the algebraic approach exhibits several degeneracies (mainly coincident eigenvalues) concerning some very important cases (see EPR states). These cases should be treated separately with ad hoc procedures. Such a distinction is not required in the structural approach.
In the structural approach, all the variables have a precise physical meaning, related to the corresponding components of the architecture, i.e., squeezers, beam splitters, phase shifters, and one can choose the specific variables to achieve the desired properties of the covariance matrix, for example, the entanglement.
Finally, we note that the theory, here developed in detail for the two-mode, could be extended to higher modes, of course with the penalty of complication at the increase in the order. We developed the three–mode (not reported here) with the structural approach and without difficulty, resulting, however, in long formulas (which were radical free). With the algebraic approach, the three mode results to be very complicated, mainly for the large number of particular cases to be treated separately.