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I am running variational algorithms using QuasmSimulator, which means I am performing a classical optimization where the cost function is computed running a quantum circuit. Moreover, for the optimizer to converge I need high accuracy, which implies a large number of shots.

These two things combined make a run last for days on my laptop, even for a few qubits. Thus, I am trying to run shots in parallel to save some time. Qiskit has a backend option for this:

from qiskit.providers.aer import QasmSimulator
backend = QasmSimulator(method="automatic", max_parallel_threads=6, max_parallel_shots=6)

... the rest of the code...

job = execute(circ, backend, shots=nshots)

However, when setting this option I see no difference, neither in runtime nor in CPU usage from Window's task manager. I think the problem could be that normally a python script just uses one core, so when Qiskit runs it "thinks" that indeed you have one core only. Any suggestion?

I am using Qiskit 0.23.0

(Note that I asking specifically about parallel shots and not parallel circuits, although that might also help with another issue I have)

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  • $\begingroup$ Not answering your question, this is a comment about simulation for calculating an expectation value. Depending on your circuit (e.g. does it contain intermediate measurements) and what you're looking for (e.g. do you simulate a noisy device), there are cases where you can run a single shot and use the expectation value snapshot, which calculates <psi|O|psi>, where |psi> is the state and O is the obervable. $\endgroup$ – Yael Ben-Haim Nov 16 at 9:29
  • $\begingroup$ Also, you may find the variational solvers in Qiskit Aqua useful $\endgroup$ – Yael Ben-Haim Nov 16 at 9:33
  • $\begingroup$ You can get short runtime indeed if you just use the state vector simulator, which is exact and performs matrix-vector multiplications etc. Perhaps that's what you mean by "one shot". However, if you are simulating a QC functioning, obviously it's impossible to get a one-shot result. In that case, you would just get a reading that is 1 or 0 which does not hold any info. You need to sample many times until you have a meaningful ration of 1s/0s. Or did I misunderstood you? $\endgroup$ – Enrico Nov 16 at 10:13
  • $\begingroup$ Yes, you understood correctly $\endgroup$ – Yael Ben-Haim Nov 16 at 12:53
  • $\begingroup$ I am also interested in statistical error introduced by a finite sampling of the expectation value, so, unfortunately, I can't bypass the problem as you suggest. $\endgroup$ – Enrico Nov 16 at 13:28
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There is a question which is quite alike to your problem in Quantum Computing-stack exchange. I have answered it before, you may see that.

I think the number of shots nearly does not change the running time(although I have been voted -1), you can see the problem and answers yourself.

Just for short, I think after qiskit evaluates the state vector, it gives you measurement results based on that state vector, instead of evaluate state vector $n$ times and gives you one measurement result each time(I guess, since if you import the time package and observe the time consumed about different shots you won't see a pattern that the consumed time grows with the number of shots).

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  • $\begingroup$ What you say it's partially true but I don't think it holds for my case because I am running with more than an order of magnitude more shots (order 10^6). For instance, in your example, it's 5 second out of 45 due to the number of shot increase. In my case that would become a 50 seconds increase which effectively doubles the runtime. $\endgroup$ – Enrico Nov 16 at 10:20
  • $\begingroup$ I know what you mean, even you import np.random.random to generate some numbers, the process takes time, right? In my earlier answer, I tested a 10^5 case for about 20 qubits, but if I enlarge the number of shots to 10^6 it can hardly change much because in my case the main part(even the overwhelming part) is still to get the state vector of the 20-qubit state. $\endgroup$ – Yitian Wang Nov 16 at 11:21
  • $\begingroup$ But when you refer to using multiple cores to run multiple codes, I am not familiar with such a thing (like how many cores python use by default), so this answer might just be a bit of tiny advice. $\endgroup$ – Yitian Wang Nov 16 at 11:23
  • $\begingroup$ To your first comment: I am quite sure that's not the case. If you are curious, I invite you to run the same experiment with 1e6 shots and check. When it comes to python, I am no computer scientist myself but you can just check that it uses one processor by opening the task manager while running any intensive task and you'll see what I mean. $\endgroup$ – Enrico Nov 16 at 11:33

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