  Accurate methods of assessing the performance of quantum gates are extremely
important. Quantum process tomography and randomized benchmarking are the
current favored methods. Quantum process tomography gives detailed information,
but significant approximations must be made to reduce this information to a
form quantum error correction simulations can use. Randomized benchmarking
typically outputs just a single number, the fidelity, giving no information on
the structure of errors during the gate. Neither method is optimized to assess
gate performance within an error detection circuit, where gates will be
actually used in a large-scale quantum computer. Specifically, the important
issues of error composition and error propagation lie outside the scope of both
methods. We present a fast, simple, and scalable method of obtaining exactly
the information required to perform effective quantum error correction from the
output of continuously running error detection circuits, enabling accurate
prediction of large-scale behavior.
