GST can estimate gates up to an overall gauge. PyGSTi tries to find a good gauge in which to report process matrices and gauge-variant metrics like fidelity -- but sometimes this goes wrong. The most reliable error metrics and gate properties are gauge-invariant ones, and these are listed on this tab.
RB error metricsThis table shows estimates for the error rate that would be obtained using the Randomized Benchmarking (RB) protocol. RB is performed in several different ways. The Clifford RB number corresponds to the most standard form of RB, where random Clifford gate sequences are performed. This number is dependent on how the Clifford operations are compiled from the primitive gates, and so if you didn't specify a Clifford compilation and pygsti couldn't deduce one, this quantity will be absent. The primitive RB number corresponds to performing RB on random sequences of the primitive gates, rather than the Cliffords. This number does not require any compilation table and is always be computed by pyGSTi. Two caveats regarding these RB numbers: 1) the primitive RB number is not meaningful for arbitrary gate sets; if the gate set generates the Clifford group then it is definitely meaningful. 2) these predicted RB numbers rely on a perturbative technique, and if the estimated gates are far from their ideal counterparts the predicted numbers may be very inaccurate.
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SPAM probabilities This table shows estimated SPAM probabilities for each measurement outcome. These are computed as \mathrm{Tr}[\rho E_i], where \rho is an estimated initial state (often labelled \rho_0), and \{E_i\} is the estimated n-outcome POVM. The symbol E_C denotes the nth POVM effect, which is not allowed to vary freely but is defined by subtracting the sum of the other effects (which are freely varied) from the identity.
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Gram matrix spectrum.The GST Gram matrix is not a standard error metric, but it is gauge-invariant and critical to the GST process. It provides some insight into generalized SPAM. It is the (estimated) matrix of inner products between all the input states prepared by the various preparation fiducials, and all the measured effects prepared by the various measurement fiducials. LGST involves inverting the Gram matrix, so it needs to be full rank. In the plot, each pair of bars shows the nth eigenvalues of the estimated Gram matrix and the Gram matrix predicted by the ideal targets (respectively). Larger eigenvalues indicate better sensitivity, and the number of non-zero values indicates the dimension of the state (density matrix) space being probed (e.g., for a single qubit, the Gram matrix should have 4 O(1) eigenvalues).
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Spectral error metrics between estimated gates and ideal targetsThis table presents a variety of gauge-invariant quantities that quantify the distance or discrepancy between (1) an estimated gate, and (2) the ideal corresponding target operation. Each of these error metrics depends only on a specific gate's spectrum (eigenvalues), which are gauge-invariant and non-relational (i.e., they pertain to a single gate). Hovering over a column header will pop up a mathematical description of the corresponding metric.
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Single metric comparison.TODO: caption
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Eigenvalues of estimated gates. This table lists the spectrum of each estimated gate. It also breaks out the real and imaginary parts of each eigenvalue, and it compares the estimated eigenvalues to those of the ideal target gates in several useful ways. To do these comparisons, each estimated eigenvalue needs to be matched up with a target eigenvalue, and pyGSTi does this independently for each metric by computing a minimum-weight matching based on that metric. Hovering over a column header will pop up a mathematical description of the corresponding metric.
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