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Expectation value of observables non diagonal in the computational basis #1489
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Resolves #852, happy to see that this functionality is getting added in :) |
That is very cool and will be very useful for my work with @Sam-XiaoyueLi @BrunoLiegiBastonLiegi you can request review from @Sam-XiaoyueLi and she can help add further docs or examples if needed |
@stavros11 may I trouble you with the review of this PR? I changed some things in the @Sam-XiaoyueLi feel free to suggest any meaningfull example for the docs, for the moment I just made up a random observable. |
@Sam-XiaoyueLi maybe TLFIM or XXZ?
…On Sun, 27 Oct 2024, 17:05 BrunoLiegiBastonLiegi, ***@***.***> wrote:
@stavros11 <https://github.com/stavros11> may I trouble you with the
review of this PR? I changed some things in the SymbolicHamiltonian and
related objects, which I presume you first implemented.
@Sam-XiaoyueLi <https://github.com/Sam-XiaoyueLi> feel free to suggest
any meaningfull example for the docs, for the moment I just made up a
random observable.
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frequencies = [ | ||
result.frequencies() | ||
for result in self.backend.execute_circuits(rotated_circuits, nshots=nshots) |
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I understand this is useful only for hardware execution, right? Since in simulation you could just simulate the circuit and use expectation
directly, which should also be more efficient (and exact).
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Yes I had exactly in mind hardware execution for this. But even for simulation, you wouldn't want the exact expectation from the final state (is this what you are suggesting? not sure) as this is supposed to be sample dependent.
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you wouldn't want the exact expectation from the final state (is this what you are suggesting? not sure) as this is supposed to be sample dependent.
Yes, above I meant to get the exact expectation from the final state, however you can still get a sample dependent estimate by first simulating the circuit with shots, getting the samples and using expectation_from_samples
. I would expect this to be faster than using expectation_from_circuit
for simulation, since in the first case the "state preparation" circuit is only simulated once, while expectation_from_circuit
will repeat the simulation of the same circuit for every basis.
On hardware, or if you are simulating with noise, it is probably a different story.
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Ah yeah, I see, I've been thinking about doing the state preparation first and then just measuring for simulation. To do that though, we should probably move (at least some part of) the expectation_from_circuit
inside the backend, which I am not against by the way, but for the moment I decided to keep it simple. In any case, I was assuming that since execute_circuits
launches several parallel jobs, if I am not mistaken, the overhead of running several copies of the same simulation would have been reduced, assuming you have many cores available.
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I also think we don't need to do anything about this, since it is already available by executing the circuit manually and then calling expectation_from_samples
. This is a bit more manual for the user, but it is still fairly simple and it is not the best idea to provide multiple ways (almost equally simple) to do the same thing as it would increase maintenance from our side.
Co-authored-by: Stavros Efthymiou <[email protected]>
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Thanks for the updates @BrunoLiegiBastonLiegi.
Currently, our
SymbolicHamiltonian.expectation_from_samples
only works for observables that are composition ofZ
operators. To compute the expectation value of some non-Z
operator, for instanceXZ + YX
, one has to manually rotate the measurement basis accordingly, collect the frequencies and then calculate the expectation value of aZZ
operator starting from those frequencies. Therefore, in this example, one has to measureM(0,1, basis=["X", "Z"])
first, collect the frequencies and calculate the expectation ofZZ
, then re-execute a second time measuringM(0,1, basis=["Y", "X"])
, get the frequencies and again calculate the expectation ofZZ
. The sum of the two expectations is then the global expected value.This PR automatizes this process, by allowing circuits to be passed as inputs to
SymbolicHamiltonian.expectation_from_samples
.Checklist: