= Solution
A <Pauli-based computation> starts with the supplied nonstabilizer resource state $|\alpha\rangle$ and performs an adaptive sequence of mutually commuting <measurements of Pauli observables>. Each outcome is recorded classically and may determine the next Pauli observable and the final classical output. When a proposed observable anticommutes with a previously fixed Pauli constraint, its outcome is uniformly random by part b(i); one samples that outcome and uses the <Clifford operation> $V(\lambda_P,\lambda_Q)$ from part b(ii) to update the <Clifford frame>. This replaces the old constraint by the newly measured one while preserving the distribution and the <post-measurement state> represented by the computation.
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