Add run_from_experiment to stochastic solvers.#2318
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Ericgig merged 22 commits intoApr 25, 2024
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This is a cool new feature! I do not think that I have much to add.
Just as a side note, I used this opportunity to finally try to understand what exactly happens with the heterodyne option, and it took me quite a while to figure out. In the users guide, there is the definition of the homodyne current; I think it could be useful to add a definition of the heterodyne currents as well? (But I don't know if that's in the scope of this PR.)
Co-authored-by: Paul <[email protected]>
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April 22, 2024 09:05
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Description
Add the capacity of stochastic solver to run from know noise or measurements.
In the lab, the measurement can be obtained, but that measurements is a mix of and expectation values and noise. The stochastic solver can be used to split these components to study the system.
The new method for this is
SMESolver.run_from_experiment. It works similarly torunof deterministic solver: it compute a single trajectory. It can take eitherdWormeasurementas the noise input. (dWis the default).dWinput is available with every integration method, but measurement input is limited toeuler,milstein,pred_corr,platen.The measurement is
expect(m_ops, state_t) + dW / dt. Forrun_from_experimentit has no choice but to takestate_tat the start of the interval, but for the result forsmesovle, it was taken at the end of the interval. I updated thestore_measurementoptions to allow the user the choose where it is taken betweenstart,endandmiddle. Whenstartis used, therun's result's measurement can be feed back torun_from_experimentto recompute the same trajectory. (The theory is the integral of the expectation value over the interval.)Another change in this PR is to keep the dW in the wiener and result class. We used to sum them into
W, but thedWare what we use so it just end up being extra calls tonp.cumsumandnp.diff.