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| # Flux shift amplitude conversion (device-specific) | ||
| # NOTE: this assumes freq_vs_flux_01_quad_term exists and sign makes the sqrt valid | ||
| flux_shift = np.sqrt(-params.physical_detuning / freq_vs_flux_01_quad_term) |
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should we have the ability to set it to zero if the freq_vs_flux term doesn't exist? what is the the style of the usecase - very specfic to the original implementation or plug and play?
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very specific to the original implementation... then people can modify it. Do you think it's important to add it?
| # Bayesian update | ||
| assign(rk, Cast.to_fixed(state) - 0.5) | ||
| assign(t_sample, Cast.mul_fixed_by_int(1e-3, t * 4)) # tau in us | ||
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I would add a comment "Looping over the frequencies and updating the probabilit for each one based on the last measurement result"
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But in this loop we don't use the measurement result... what am I missing?
| This use case demonstrates Bayesian estimation of a Ramsey fringe frequency (effective detuning) using single-shot measurements on a flux-tunable transmon controlled by the OPX. | ||
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| The protocol performs a Ramsey-style experiment and updates a discrete posterior distribution over a frequency grid after each measurement outcome. At the end of each repetition, the posterior mean provides an estimate of the effective detuning. | ||
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We should add a reference to Fabrizzio's paper, saying that this is based on that. And also, acknowledge the work by Fabrizzion
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Sure! Can you send me the paper you are referring to?
TomDvirQM
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TomDvirQM
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Looks great, tiny comment about documentation
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