
160 G.C. Hegerfeldt
model outlined above, although in this case the numerical simplifications are not so
pronounced.
Replacing density matrices by pure states in simulations: Onestartsinthestate
|α, develops with U
cond
(t, 0), calculates the probability density w
1
(t, t
0
= 0; |α)
for the first photon as in (6.122), and generates the first photon time, t
1
, by a random
number generator according to this probability density. Then one determines the
corresponding reset matrix ˆρ ≡ ˆρ(t
1
) as in (6.123). Instead of resetting the atomic
state to ˆρ(t
1
) one determines its eigenvalues p
±
(t
1
) and eigenstates |p
±
(t
1
) and
resets the atom to the pure state |p
+
(t
1
) with probability p
+
(t
1
)orto|p
−
(t
1
) with
probability p
−
(t
1
). Then one applies the conditional time development U
cond
(t, t
1
)
to the pure state chosen and calculates the probability density w
1
(t, t
1
; |p
±
(t
1
))for
the second photon. With this probability density one generates the time, t
2
, for this
second photon and determines the reset matrix at time t
2
, which obviously depends
on the pure reset state chosen at time t
1
. Again, instead of resetting the atom to this
reset matrix, one resets to one of its eigenvectors with the probability given by the
corresponding eigenvalue. This procedure is depicted in Fig. 6.11. Continuing in
this way, one generates a pseudo-quantum trajectory with pure states which does
not correspond to an actual physical trajectory. Nevertheless, the jump statistics
obtained by time averaging over such a trajectory agrees with the photon statistics
of the original physical process [26].
Fig. 6.11 Simulation of a trajectory consisting of pure states. One starts in |α, develops with U
cond
,
generates the first jump time t
1
, and determines the reset state for U
cond
(t
1
, 0)|αand its eigenvalues
p
±
(t
1
) and eigenvectors |p
±
(t
1
).Att
1
one resets to |p
±
(t
1
) with probability p
±
(t
1
). Then one
develops the chosen reset state with U
cond
(t, t
1
), generates the next jump time t
2
, and determines
the reset state for U
cond
(t
2
, t
1
)|p
±
(t
1
) and its eigenvalues p
±
(t
2
) and eigenvectors |p
±
(t
2
).Att
2
one resets to |p
±
(t
2
) with probability p
±
(t
2
),andsoon
Recovering the Bloch equations: Now one can repeatedly generate a large ensem-
ble of such trajectories, always starting with the same initial state |α. At a fixed time
t, each trajectory is in a particular pure state, which depends on its history. Let the
incoherent weighted sum of these states be denoted by ρ
(sim)
|α
(t). It can then be shown
[26] that this density matrix satisfies the optical Bloch equations of the original
problem with the same initial condition, i.e., |αα|, and hence renders a solution of
the Bloch equations of the original problem. In general, for a large number of levels,
this can be numerically extremely advantageous.