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Ψe (t) Ck Ck Ψe (t)

With this notation, the contribution of |Ψe (t) to the second term in eqn (18.149) is
(“e (t) ∆t) ρe
meas (t), where

Pk |φek (t) φek (t)| ,
ρe e
(t) = (18.151)

Ψe (t) Ck Ck Ψe (t)
Pk =
, (18.152)
“e (t)

“e (t) = Ψe (t) Ck Ck Ψe (t) (18.153)

is the total transition (quantum-jump) rate of |Ψe (t) into the collection of normalized
states de¬ned by eqn (18.150). Since the coe¬cients Pk satisfy 0 Pk 1 and
e e

Pk = 1 ,

they can be treated as probabilities.
With this interpretation, ρe
meas has the form (2.127) of the mixed state describing
the sample after a measurement has been performed, but before the particular outcome
is known. This suggests that we interpret the second term on the right side of eqn
(18.149) as a wave packet reduction resulting from a measurement-like interaction
with the reservoir.
After summing over the ensemble, eqn (18.148) becomes

ρS (t + ∆t) = Udis (∆t) ρS (t) Udis (∆t) + “ (t) ∆t ρmeas (t) , (18.155)

P e ρe
ρmeas (t) = meas (t) , (18.156)

Pe “e (t)
Pe = , (18.157)
“ (t)
Quantum jumps

Pe “e (t)
“ (t) = (18.158)

is the ensemble-averaged transition rate.

A The Monte Carlo wave function algorithm
In quantum theory, a system evolves smoothly by the Schr¨dinger equation until a
measurement event forces a discontinuous change. This feature is the basis for the
procedure described here.
It is plausible to expect that only one of the two terms in eqn (18.155)”dissipative
evolution or wave packet reduction”will operate during a su¬ciently small time step.
We will ¬rst describe the Monte Carlo wave function algorithm (MCWFA) that follows
from this assumption, and then show that the density operator calculated in this way
is an approximate solution of the master equation (18.115).
In order to simplify the presentation we assume that the initial ensemble is de¬ned
states {|˜1 , . . . , |˜M } ,
probabilities {P1 , . . . , PM } ,
so that the index e = 1, 2, . . . , M .
In each time step, a choice between dissipative evolution and wave packet reduc-
tion”i.e. a quantum jump”has to be made. For this purpose, we note that the prob-
ability of a quantum jump during the interval (t, t + ∆t) is ∆Pe (t) = “e (t) ∆t, where
“e (t) is the total transition rate de¬ned by eqn (18.153). The discrete scheme will
only be accurate if the jump probability during a time step is small, i.e. ∆Pe (t) 1.
Consequently, the time step ∆t must satisfy “e (t) ∆t 1.
With this preparation, we are now ready to state the algorithm for integrating the
master equation in the interval (0, T ).
(1) Set e = 1 and de¬ne the discrete times tn = (n ’ 1) ∆t, where 1 n N and
(N ’ 1) ∆t = T .
(2) At the initial time t = 0, set |Ψ (0) = |Ψe (0) = |˜e .
(3) For n = 2, . . . , N choose a random number r in the interval (0, 1). If ∆Pe (tn’1 ) < r
go to (a), and if ∆Pe (tn’1 ) > r go to (b). Since we have imposed ∆Pe (t) 1,
this procedure guarantees that quantum jumps are relatively rare interruptions of
continuous evolution.
(a) In this case there is no quantum jump, and the state vector is advanced from
tn’1 to tn by dissipative evolution followed by normalization:

Udis (∆t) |Ψe (tn’1 )
|Ψe (tn ) =

Ψe (tn’1 ) Udis (∆t) Udis (∆t) Ψe (tn’1 )

1’ Hdis |Ψe (tn’1 )
= , (18.160)
1 ’ ∆Pe (tn’1 )

where the last line follows from the de¬nition (18.147) of Udis (∆t).
¼ The master equation

(b) In this case there is a quantum jump, and the new state vector is de¬ned
by choosing k randomly from {1, 2, . . . , K}”conditioned by the probability
distribution Pk de¬ned in eqn (18.152)”and setting

|Ψe (tn ) = |φek (tn’1 ) , (18.161)

i.e. |Ψe (tn ) jumps to one of the states permitted by the second term in eqn
(4) Repeat step (3) Ntraj times to get Ntraj discrete representations

{|Ψej (tn ) , 1 N } , j = 1, . . . , Ntraj
n (18.162)

of the state vector. These representations are distinct, due to the random choices
made in each time step. The density operator that evolves from the original pure
state |˜e is then given by
|Ψej (tn ) Ψej (tn )| .
ρe (tn ) = (18.163)
Ntraj j=1

(5) Replace e by e + 1. If e + 1 M go to step (2). If e + 1 > M go to step (6).
(6) The density operator ρ (t) that evolves from the initial density operator ρ (0)”
de¬ned by the ensemble (18.159)”is given by
Pe ρe (tn ) .
ρ (tn ) = (18.164)

The computational cost of this method scales as Ntraj N , where N is the dimen-
sionality of the sample Hilbert space HS . Consequently, the MCWFA would not be
very useful as a technique for solving the master equation, if the required number of
trials is itself of order N . Fortunately, there are applications with large N for which
one can get good statistics with Ntraj N.

B Proof that the MCWFA generates a solution
If each of the density operators ρe (t) satis¬es the master equation, then so will the
overall density operator de¬ned by eqn (18.164); therefore, it is su¬cient to give the
proof for a single ρe (t). For a su¬ciently large number of trials, the evolution of the
pure state operators,
ρej (tn ) = |Ψej (tn ) Ψej (tn )| , (18.165)
is e¬ectively given by step (2a) with probability 1 ’ ∆Pe (tn’1 ) and by step (2b) with
probability ∆Pe (tn’1 ). In other words,

ρej (tn ) = (1 ’ ∆Pe (tn’1 )) Ψdis (tn ) Ψdis (tn )
ej ej

Pk (tn’1 ) |φek (tn’1 ) φek (tn’1 )| ,
+ ∆Pe (tn’1 ) (18.166)
Quantum jumps

1’ Hdis |Ψej (tn’1 )
Ψdis (tn ) = . (18.167)
1 ’ ∆Pe (tn’1 )
The |φek (tn’1 ) s are de¬ned by substituting |Ψej (tn’1 ) for |Ψe (tn’1 ) in eqn (18.150).
Using the de¬nitions of ∆Pe , Pk , and Hdis in this equation and neglecting O ∆t2 -

terms leads to
ρej (tn ) ’ ρej (tn’1 ) i
= ’ [HS , ρej (tn’1 )] + Ldis ρej (tn’1 ) . (18.168)
Averaging this result over the trials, according to eqn (18.163), and taking the limit
∆t ’ 0 shows that ρe (t) satis¬es the master equation (18.115).
Laser-induced ¬‚uorescence—
For a concrete application of the MCWFA, we return to the trapped three-level ion
considered in Section 18.7.1. For this example, however, we replace the incoherent
source driving 3 ” 1 by a coherent laser ¬eld E L e’iωL t that is close to resonance,
i.e. |ωL ’ ω31 | ωL . In the interests of simplicity, we also drop the ¬eld driving
3 ” 2. The semiclassical approximation for the laser is applied by substituting E(+) ’
E L e’iωL t in the general results (11.36) and (11.40) of Section 11.1.4.
In the resonant wave approximation, the Schr¨dinger-picture Hamiltonian is HS =
HS0 + HS1 , where
HS0 = q Sqq , (18.169)

HS1 = „¦L S31 e’iωL t + HC , (18.170)
and „¦L = ’d31 · E L / is the Rabi frequency for the laser driving the 1 ” 3 transition.
The Sqp s are the atomic transition operators de¬ned in Section 11.1.4, and the labels
q and p range over the values 1, 2, 3.
The form of the dissipative operator Ldis for the three-level ion can be inferred from
the result (18.44) for the two-level atom, by identifying each pair of levels connected
by a decay channel with a two-level atom. For example, the lowering operator σ’ in
eqn (18.44) will be replaced by S13 for the 3 ’ 1 decay channel, and the remaining
transitions are treated in the same way.
There are two important simpli¬cations in the present case. The ¬rst is that the
phase-changing collision term in eqn (18.44) is absent for an isolated ion. The second
simpli¬cation is the assumption that the reservoirs coupled to the three transitions”
i.e. the modes of the radiation ¬eld near resonance”are at zero temperature. This
approximation is generally accurate at optical frequencies, since kB T ωopt for any
reasonable temperature.
One can use these features to show that Ldis is de¬ned by
Ldis ρS = ’ (S31 S13 ρS + ρS S31 S13 ’ 2S13 ρS S31 )
’ (S32 S23 ρS + ρS S32 S23 ’ 2S23 ρS S32 )
’ (S21 S12 ρS + ρS S21 S12 ’ 2S12 ρS S21 ) . (18.171)
¾ The master equation

This expression for Ldis can be cast into the general Lindblad form (18.117) by setting
K = 3 and de¬ning the operators

C1 = “31 S13 , C2 = “32 S23 , C3 = “2 S12 , (18.172)

corresponding respectively to the decay channels 3 ’ 1, 3 ’ 2, and 2 ’ 1.
The Rabi frequency „¦L is small compared to the laser frequency ωL , so the
Schr¨dinger-picture master equation,

ρS (t) = [HS , ρS (t)] + Ldis ρS (t) ,
i (18.173)

involves two very di¬erent time scales, 1/ωL 1/„¦L . Di¬erential equations with this
feature are said to be sti¬, and it is usually very di¬cult to obtain accurate numerical
solutions for them (Press et al., 1992, Sec. 16.6). In the case at hand, this di¬culty
can be avoided by transforming to the interaction picture.
The general results in Section 4.8 yield the transformed master equation

‚I †
ρS (t) = HS1 , ρI (t) + U0 (t) Ldis ρS (t) U0 (t) ,
i (18.174)

where U0 (t) = exp (’iHS0 t/ ) and the transform of any operator X is X I (t) =

U0 (t) XU0 (t). Applying this rule to the transition operators gives

Sqp (t) = U0 (t) Sqp U0 (t) = eiωqp t Sqp ,

and this in turn leads to

U0 (t) Ldis ρS (t) U0 (t) = Ldis ρI (t) . (18.176)

Thus we arrive at the useful conclusion that Ldis has the same form in both pictures.
The transformed interaction Hamiltonian is

HS1 = „¦L S31 e’iδt + HC ,

where δ = ωL ’ ω31 . The interaction-picture master equation (18.174) is not sti¬, but
it still has time-dependent coe¬cients. This annoyance can be eliminated by a further
ρS (t) = eitF ρI (t) e’itF , (18.178)

F= fq Sqq . (18.179)

The algebra involved here is essentially identical to the original transformation to
the interaction picture, and it is not di¬cult to show that the equation of motion
Quantum jumps

for ρ (t) will have constant coe¬cients provided that the parameters fq are chosen to
f3 ’ f1 = δ . (18.180)
The simple solution f1 = f2 = 0, and f3 = δ, leads to

ρ (t) = H S1 , ρS (t) + Ldis ρS (t) ,
i (18.181)
‚t S
where the transformed interaction Hamiltonian is
⎡ ¤
0 0 „¦— L
H S1 = ⎣ 0 0 0 ¦ . (18.182)
„¦L 0 ’δ
We are now in a position to calculate all the bits and pieces that are needed for the
direct solution of the master equation (18.181), or the application of the MCWFA. We
leave the algebra as an exercise for the reader and proceed directly to the numerical
solution of the master equation. The density operator for this problem is represented
by a 3 — 3 hermitian matrix which is determined by nine real numbers. Thus the
master equation in this case consists of nine linear, ordinary di¬erential equations
with constant coe¬cients. There are many packaged programs that can be used to
solve this problem.
Of course, this means that we do not really need the MCWFA, but it is still useful
to have a solvable problem as a check on the method. In Fig. 18.7 we compare the
direct solution to the average over 48 trials of the MCWFA. The match between the
averaged results and the direct solution can be further improved by using more trials
in the average, but it should already be clear that the MCWFA is converging on a
solution of the master equation.
Following the general practice in physics, we assume”on the basis of this special
case”that the MCWFA can be con¬dently applied in all cases. In particular, this
includes those applications for which the dimension of the relevant Hilbert space is
large compared to the number of trials needed.

Quantum trajectories—
The results displayed in Fig. 18.7 show that the full-blown master equation”whether
solved directly or by averaging over repeated trials of the MCWFA”does no better
than the rate equations of Section 18.7.1 in describing the phenomenon of interrupted
¬‚uorescence. This should not be a surprise, since the master equation describes the
evolution of the entire ensemble of state vectors for the ion.
What is needed for the description of quantum jumps (interrupted ¬‚uorescence)
is an improved version of the simple on-and-o¬ model used to derive the random
telegraph signal in Fig. 18.3. This is where single trials of the MCWFA come into
play. Each trial yields a sequence of state vectors
|Ψ (t1 ) , |Ψ (t2 ) , . . . , |Ψ (tN ) , (18.183)
which is a discrete sampling of a continuous function |Ψ (t) . This has led to the use
of the name discrete quantum trajectory for each individual trial of the MCWFA.
The master equation



Fig. 18.7 The population of |µ3 as a function of time. The smooth curve represents the
direct solution of eqn (18.181) and the jagged curve is the result of averaging over 48 trials of
the Monte Carlo wave function algorithm. Time is measured in units of the decay time 1/“31
for the 3 ’ 1 transition. In these units „¦L = 0.5, δ = 0, “32 = 0.01, and “21 = 0.001.

An example of the upper-level population P3 obtained from a single quantum trajec-
tory is shown in Fig. 18.8. Once again, a judicious choice from the results for several
trajectories nicely exhibits the random telegraph signal characterizing interrupted ¬‚u-
According to the standard rules of quantum theory, the information from a com-
pleted measurement”in particular, the collapse of the state vector”should be taken
into account immediately. In the algorithm presented in Section 18.7.3 the new infor-



Fig. 18.8 The population of |µ3 as a function of time for a single quantum trajectory. The
parameter values are the same as in Fig. 18.7.
Quantum jumps

mation is not used until the next time step at tn + ∆t, so single trials of the Monte
Carlo wave function method are approximations to the true quantum trajectory.
A more re¬ned treatment involves allowing for the projection or collapse event to
occur one or more times during the interval ∆t, and using the dissipative Hamiltonian
to propagate the state vector in the subintervals between collapses. With this kind
of analysis, it can be shown that the Monte Carlo method is accurate to order ∆t.
Increasing the accuracy to order ∆t2 requires the inclusion of jumps at both ends of
the interval and also the possibility that two jumps can occur in succession (Plenio
and Knight, 1998).
Results like that shown in Fig. 18.8 might tempt one to believe that the Monte Carlo
technique”or the more re¬ned quantum trajectory method”provides a description of
single quantum events in isolated microscopic samples. Any such conclusion would be
completely false. A large sample of trials for the Monte Carlo technique will resemble
a corresponding set of experimental runs, but the relation between the two sets is
purely statistical. Both will yield the same expectation values, correlation functions,
etc. In other words, the Monte Carlo or quantum trajectory methods are still based
on ensembles. The di¬erence between these methods and the full master equation is
that the ensembles are conditioned, i.e. reduced, by taking experimental results into

Quantum state di¬usion—
As explained above, the standard formulations of quantum theory do not apply to
individual microscopic samples, but rather to ensembles of identically prepared sam-
ples. Several of the founders of the quantum theory, including Einstein (Einstein et al.,
1935) and Schr¨dinger (Schr¨dinger, 1935b), were not at all satis¬ed with this feature,
o o
and there have been many subsequent e¬orts to reformulate the theory so that it ap-
plies to individual microscopic objects. One approach, which has attracted a great deal
of attention, is to replace the Schr¨dinger equation for an ensemble by a stochastic
equation”e.g. a di¬usion equation in the Hilbert space of quantum states”for an
individual system.
The universal empirical success of conventional quantum theory evidently requires
that the new stochastic equation should agree with the Schr¨dinger equation when ap-
plied to ensembles. Many such equations are possible, but symmetry considerations”

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