Put and . In the computational basis, input has outcome probabilities , while has . The marginal outcomes are equally likely; the Bayesian posterior probabilities areGuess after zero and after one. Each guess is correct with probability and wrong with probability . Neither outcome is certain evidence for one input.
For a real state , the Born rule in the Hadamard basis gives . Both angles and have . Thus both preparations give and . By Bayes' theorem, either outcome leaves the two input hypotheses equally likely. The biased outcome frequencies carry no information about which state was prepared; the best success probability with this measurement is .
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