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Stochastic Master Equations

A stochastic master equation is a differential equation for a quantum state conditioned on a continuously acquired measurement record. It describes a single observer’s best state assignment during monitoring, while the ordinary master equation describes the ensemble average when the record is ignored.

The central contrast is:

unconditional state: average over all records
conditional state: state given a particular record

Stochastic master equations are the bridge between generalized measurement, Lindblad dynamics, quantum trajectories, feedback control, and experimentally recorded signals such as photon counts, homodyne currents, heterodyne currents, and qubit readout voltages.

For the conceptual entry point to records, conditioning, measurement strength, and ensemble averaging, see Continuous Monitoring.

Conditional Versus Unconditional Evolution

Section titled “Conditional Versus Unconditional Evolution”

An unconditional Markovian master equation has the form

dρdt=Lρ,\frac{d\rho}{dt} = \mathcal L\rho,

where, in Lindblad–GKSL form,

Lρ=−iℏ[H,ρ]+∑kD[Lk]ρ,\mathcal L\rho = - \frac{i}{\hbar}[H,\rho] + \sum_k \mathcal D[L_k]\rho,

with

D[L]ρ=LρL†−12{L†L,ρ}.\mathcal D[L]\rho = L\rho L^\dagger - \frac12 \{L^\dagger L,\rho\}.

This equation describes the average state when environmental outputs or measurement records are not retained.

A stochastic master equation describes the conditional state ρc(t)\rho_c(t) given the measurement record up to time tt:

dρc=deterministic drift+record-dependent stochastic update.d\rho_c = \text{deterministic drift} + \text{record-dependent stochastic update}.

The ensemble average over records must recover the unconditional state:

E[ρc(t)]=ρ(t),dρdt=Lρ.\mathbb E[\rho_c(t)] = \rho(t), \qquad \frac{d\rho}{dt} = \mathcal L\rho.

This consistency check is non-negotiable. If averaging trajectories does not recover the intended master equation, either the stochastic equation, the numerical scheme, or the convention has been misused.

A continuous measurement produces a time series, not just a final outcome. The dedicated Measurement Records page discusses calibration, time bins, detector imperfections, and the main record types. The record may be:

  • a counting record NtN_t for photon detection;
  • a noisy current YtY_t for homodyne measurement;
  • two noisy quadratures for heterodyne measurement;
  • a voltage trace for dispersive qubit readout;
  • a coarse-grained detector signal in a solid-state measurement.

The conditional state is filtered from this record. It is not an additional hidden ontic variable in the formalism; it is the state assignment conditioned on the information made available to the observer.

Ignoring the record gives the nonselective state:

ρ(t)=Erecordsρc(t).\rho(t) = \mathbb E_{\text{records}}\rho_c(t).

This is the continuous-time analogue of the distinction between selective and nonselective measurements.

Most stochastic master equations in quantum optics and measurement theory are written in Itô form.

For a Wiener increment dWtdW_t,

E[dWt]=0,dWt2=dt,dWt dt=0.\mathbb E[dW_t]=0, \qquad dW_t^2=dt, \qquad dW_t\,dt=0.

For a counting increment dNtdN_t,

dNt∈{0,1},dNt2=dNt.dN_t\in\{0,1\}, \qquad dN_t^2=dN_t.

Its conditional mean is set by the instantaneous detection rate. For a monitored collapse operator cc with unit efficiency,

E[dNt∣ρc(t)]=Tr⁡(c†cρc(t)) dt.\mathbb E[dN_t\mid\rho_c(t)] = \operatorname{Tr}(c^\dagger c\rho_c(t))\,dt.

These multiplication rules are part of the equation. Treating dWtdW_t like an ordinary small number loses the Itô correction terms that make the ensemble average work.

A common normalized diffusive stochastic master equation for monitoring an output associated with a collapse operator cc is

dρc=Lρc dt+η H[c]ρc dWt,d\rho_c = \mathcal L\rho_c\,dt + \sqrt{\eta}\, \mathcal H[c]\rho_c\,dW_t,

where 0≤η≤10\le\eta\le1 is the detection efficiency and

H[c]ρ=cρ+ρc†−Tr⁡ ⁣[(c+c†)ρ]ρ.\mathcal H[c]\rho = c\rho+\rho c^\dagger - \operatorname{Tr}\!\left[(c+c^\dagger)\rho\right]\rho.

The corresponding homodyne-style record may be written, in this convention, as

dYt=η Tr⁡ ⁣[(c+c†)ρc(t)]dt+dWt.dY_t = \sqrt{\eta}\, \operatorname{Tr}\!\left[(c+c^\dagger)\rho_c(t)\right]dt + dW_t.

The stochastic term is an innovation: the difference between the observed noisy increment and its conditional expectation. It updates the state because the record contains partial information about the system.

Since E[dWt]=0\mathbb E[dW_t]=0, averaging this SME gives the unconditional equation dρ/dt=Lρd\rho/dt=\mathcal L\rho, provided the Itô equation is interpreted correctly.

The dedicated Diffusive Trajectories page treats homodyne, heterodyne, and continuous weak-measurement records in more detail. For the local-oscillator detection model behind the homodyne record, see Homodyne Detection.

For photon counting or other discrete detection events, a normalized jump SME has Poisson increments. For one monitored collapse operator cc and no unmonitored channels, a standard form is

dρc=−iℏ[H,ρc]dt−12{c†c,ρc}dt+Tr⁡(c†cρc)ρc dt+(cρcc†Tr⁡(c†cρc)−ρc)dNt.\begin{aligned} d\rho_c =& - \frac{i}{\hbar}[H,\rho_c]dt - \frac12 \{c^\dagger c,\rho_c\}dt \\ &+ \operatorname{Tr}(c^\dagger c\rho_c)\rho_c\,dt + \left( \frac{c\rho_c c^\dagger} {\operatorname{Tr}(c^\dagger c\rho_c)} - \rho_c \right)dN_t. \end{aligned}

The increment satisfies

E[dNt∣ρc]=Tr⁡(c†cρc) dt.\mathbb E[dN_t\mid\rho_c] = \operatorname{Tr}(c^\dagger c\rho_c)\,dt.

When no count occurs, the state evolves under a no-jump drift. When a count occurs, the state jumps by the normalized update

ρc⟶cρcc†Tr⁡(c†cρc).\rho_c \longrightarrow \frac{c\rho_c c^\dagger} {\operatorname{Tr}(c^\dagger c\rho_c)}.

Averaging over dNtdN_t recovers

dρdt=−iℏ[H,ρ]+D[c]ρ.\frac{d\rho}{dt} = - \frac{i}{\hbar}[H,\rho] + \mathcal D[c]\rho.

The dedicated Quantum Jump Trajectories page treats waiting times, effective non-Hermitian Hamiltonians, and trajectory simulations in more detail.

For a continuous measurement of σz\sigma_z, take

c=γ σz.c=\sqrt{\gamma}\,\sigma_z.

The unconditional dissipator is pure dephasing:

D[c]ρ=γ(σzρσz−ρ).\mathcal D[c]\rho = \gamma(\sigma_z\rho\sigma_z-\rho).

The diffusive SME is

dρc=−iℏ[H,ρc]dt+γD[σz]ρc dt+ηγ H[σz]ρc dWt.d\rho_c = - \frac{i}{\hbar}[H,\rho_c]dt + \gamma\mathcal D[\sigma_z]\rho_c\,dt + \sqrt{\eta\gamma}\, \mathcal H[\sigma_z]\rho_c\,dW_t.

The record is

dYt=2ηγ ⟨σz⟩c dt+dWt.dY_t = 2\sqrt{\eta\gamma}\, \langle\sigma_z\rangle_c\,dt + dW_t.

The deterministic part dephases the ensemble. The stochastic part updates the conditional state toward one of the σz\sigma_z eigenstates as the noisy record accumulates information.

Conditioning can make a state purer even while the ensemble state decoheres. For efficient monitoring, the observer learns information about the system from the record. For inefficient monitoring, some information is lost into unobserved channels, so the conditional state may remain mixed.

This distinction is essential:

decoherence of the average state
can coexist with
purification of the conditioned state

There is no contradiction. The average state mixes together different possible records; the conditional state follows one record.

An SME can be viewed as the continuous-time limit of a sequence of weak quantum instruments. Over a small interval dtdt, one has outcome-dependent completely positive maps:

ρ⟶Idy(ρ)Tr⁡Idy(ρ)\rho \longrightarrow \frac{\mathcal I_{dy}(\rho)} {\operatorname{Tr}\mathcal I_{dy}(\rho)}

conditioned on the infinitesimal outcome dydy. Expanding these maps to first order in dtdt and to the appropriate stochastic order gives the SME.

This connects stochastic master equations to Quantum Instruments rather than making them a separate postulate.

SMEs appear in normalized and unnormalized forms.

Normalized equations keep

Tr⁡ρc(t)=1\operatorname{Tr}\rho_c(t)=1

on every trajectory, but the equation is nonlinear because probabilities depend on ρc\rho_c.

Unnormalized equations are often linear and easier for likelihood calculations. If ρ~c\tilde\rho_c is unnormalized, then

ρc=ρ~cTr⁡ρ~c.\rho_c = \frac{\tilde\rho_c} {\operatorname{Tr}\tilde\rho_c}.

The trace of ρ~c\tilde\rho_c carries the likelihood density of the observed record. Mixing normalized and unnormalized conventions is a common source of wrong factors and wrong record probabilities.

Itô and Stratonovich forms are not interchangeable by notation alone. In Itô calculus, increments are nonanticipating and satisfy rules such as dWt2=dtdW_t^2=dt. In Stratonovich calculus, the chain rule looks more like ordinary calculus, but drift terms are shifted.

Most filtering and trajectory formulas in this volume use Itô form unless stated otherwise. If a source uses Stratonovich form, convert the drift before comparing equations.

  • Treating the conditional state as the same object as the unconditional density matrix.
  • Averaging state vectors or density matrices without weighting records correctly.
  • Forgetting Itô rules such as dWt2=dtdW_t^2=dt.
  • Mixing normalized and unnormalized equations.
  • Dropping detection efficiency while keeping the same stochastic term.
  • Reading a Lindblad operator as an actual detector event without specifying the monitoring scheme.
  • Simulating too few trajectories and expecting the average to match the master equation.
  • Confusing an individual noisy record with the ensemble expectation value.

For the diffusive SME

dρc=Lρc dt+ηH[c]ρc dWt,d\rho_c = \mathcal L\rho_c\,dt + \sqrt{\eta}\mathcal H[c]\rho_c\,dW_t,

show why the stochastic term has no direct contribution to the ensemble mean at order dtdt.

Solution

In Itô form, the increment dWtdW_t has conditional mean zero:

E[dWt∣ρc(t)]=0.\mathbb E[dW_t\mid\rho_c(t)]=0.

Therefore

E[ηH[c]ρc dWt]=0\mathbb E[ \sqrt{\eta}\mathcal H[c]\rho_c\,dW_t ] = 0

at order dtdt. The ensemble mean obeys

ddtE[ρc]=E[Lρc]=L E[ρc],\frac{d}{dt}\mathbb E[\rho_c] = \mathbb E[\mathcal L\rho_c] = \mathcal L\,\mathbb E[\rho_c],

using linearity of L\mathcal L.

Use the jump SME in the page to show that the ensemble drift contains cρc†c\rho c^\dagger.

Solution

Let

r=Tr⁡(c†cρc).r=\operatorname{Tr}(c^\dagger c\rho_c).

The jump term has conditional mean

E[(cρcc†r−ρc)dNt|ρc]=(cρcc†r−ρc)r dt.\mathbb E\left[ \left( \frac{c\rho_c c^\dagger}{r} - \rho_c \right)dN_t \middle|\rho_c \right] = \left( \frac{c\rho_c c^\dagger}{r} - \rho_c \right)r\,dt.

This equals

cρcc†dt−rρcdt.c\rho_c c^\dagger dt-r\rho_c dt.

Adding the no-count drift term rρcdtr\rho_c dt cancels the last term, leaving cρcc†dtc\rho_c c^\dagger dt together with the anticommutator and Hamiltonian pieces. The result is the Lindblad dissipator.

For the continuous σz\sigma_z measurement record

dYt=2ηγ ⟨σz⟩c dt+dWt,dY_t = 2\sqrt{\eta\gamma}\, \langle\sigma_z\rangle_c\,dt + dW_t,

what is the conditional mean of dYtdY_t?

Solution

Since E[dWt∣ρc]=0\mathbb E[dW_t\mid\rho_c]=0,

E[dYt∣ρc]=2ηγ ⟨σz⟩c dt.\mathbb E[dY_t\mid\rho_c] = 2\sqrt{\eta\gamma}\, \langle\sigma_z\rangle_c\,dt.

The innovation is the difference between the observed dYtdY_t and this conditional mean.

Why does ignoring the measurement record turn an SME into an ordinary master equation?

Solution

The SME updates the state conditioned on each particular record. Ignoring the record means averaging over all possible records with their correct probabilities:

ρ(t)=E[ρc(t)].\rho(t)=\mathbb E[\rho_c(t)].

The stochastic innovation has zero mean in the diffusive case, and jump probabilities average the normalized jumps into the Lindblad jump term. Thus the record-conditioned dynamics reduces to the nonselective Lindblad master equation.

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