One possible motivation for introducing quantum distribution functions is their utility for the comparison of classical and quantum mechanics, as mentioned above. In addition to this there is another, equally important motivation for studying these functions: they can be used to compute expectation values in a comparatively simple way, where the computational simplification is mainly due to the fact that the corresponding formulae depend on scalars only, as opposed to conventional expressions of quantum expectation values which typically involve operators.
Consider a classical observable that depends on the position and
momentum variables
and
. The expectation value of
can be
computed as
Rather than discussing
a general
quantum
observable
, with the
position and the momentum operators
and
, I begin by
considering a particular operator instead, namely
,
with constants
.
Exponentials of this type are used below in the FOURIER expansion
(A.7) to construct any other operator.
In order to obtain an expression analogous to equation (A.1)
the operator
somehow has to be substituted by a
corresponding scalar expression. For example one could set
as well, with
another
distribution function .
But due to the fact that
and
do not commute one has
![]() |
(A.1) |
In order to avoid this ambiguity one first chooses a complex-valued
kernel function ; then the scalar
is defined to be associated with the operator
exclusively,
The
quantization rule
(A.5) not only defines how to
associate exponential operators with scalars, but is much more general,
as it can be applied to each term of the FOURIER expansion of any
operator ,
An explicit expression for the distribution function
can be obtained from the implicit definition (A.6) by
FOURIER transformation. For a system in the state
at
time
, the expectation values are given by
, and one gets
Note that
the kernel function
can
also
be chosen,
more generally,
as a functional
of the quantum state
of the system
itself:
.
COHEN
gives an example for such a kernel function
that, despite being quite
complicated, leads to the very simple
COHEN distribution function
that nicely combines the position and momentum representations of
in an intuitive way
[Coh66].
However,
since choosing a
-dependent
has a number of unfavourable consequences -- for
example, equation (A.8) indicates that
in this case
the function
associated with the operator
becomes
-dependent, too -- I
do not further discuss kernels that
are functionals of
.