Efficient Learning of Quantum States Prepared With Few Non-Clifford Gates
1The University of Texas at Austin
2Simons Institute for the Theory of Computing, UC Berkeley
3Rice University
4Portland State University
| Published: | 2025-11-06, volume 9, page 1907 |
| Editor: | Tom Gur |
| Eprint: | arXiv:2305.13409v5 |
| Doi: | https://doi.org/10.22331/q-2025-11-06-1907 |
| Citation: | Quantum 9, 1907 (2025). |
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Abstract
We give a pair of algorithms that efficiently learn a quantum state prepared by Clifford gates and $O(\log n)$ non-Clifford gates. Specifically, for an $n$-qubit state $|\psi\rangle$ prepared with at most $t$ non-Clifford gates, our algorithms use $\mathsf{poly}(n,2^t,1/\varepsilon)$ time and copies of $|\psi\rangle$ to learn $|\psi\rangle$ to trace distance at most $\varepsilon$.
The first algorithm for this task is more efficient, but requires entangled measurements across two copies of $|\psi\rangle$. The second algorithm uses only single-copy measurements at the cost of polynomial factors in runtime and sample complexity. Our algorithms more generally learn any state with sufficiently large stabilizer dimension, where a quantum state has stabilizer dimension $k$ if it is stabilized by an abelian group of $2^k$ Pauli operators. We also develop an efficient property testing algorithm for stabilizer dimension, which may be of independent interest.
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