Improved Stabilizer Estimation via Bell Difference Sampling
arXiv:2304.13915 · doi:10.1145/3618260.3649738
Abstract
We study the complexity of learning quantum states in various models with respect to the stabilizer formalism and obtain the following results: - We prove that -gates are necessary for any Clifford+ circuit to prepare computationally pseudorandom quantum states, an exponential improvement over the previously known bound. This bound is asymptotically tight if linear-time quantum-secure pseudorandom functions exist. - Given an -qubit pure quantum state that has fidelity at least with some stabilizer state, we give an algorithm that outputs a succinct description of a stabilizer state that witnesses fidelity at least . The algorithm uses samples and time. In the regime of constant, this algorithm estimates stabilizer fidelity substantially faster than the naïve -time brute-force algorithm over all stabilizer states. - In the special case of , we show that a modification of the above algorithm runs in polynomial time. - We exhibit a tolerant property testing algorithm for stabilizer states. The underlying algorithmic primitive in all of our results is Bell difference sampling. To prove our results, we establish and/or strengthen connections between Bell difference sampling, symplectic Fourier analysis, and graph theory.
41 pages, 2 figures. v3: changed presentation of tolerant testing algorithm and other minor edits
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Cited by in corpus (12)
- Learning shallow quantum circuits
- Improved Stabilizer Estimation via Bell Difference Sampling
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- Stabilizer Testing and Magic Entropy via Quantum Fourier Analysis
- Efficient distributed inner product estimation via Pauli sampling
- Efficient Learning of Quantum States Prepared With Few Non-Clifford Gates
- Efficient witnessing and testing of magic in mixed quantum states
- Operational interpretation of the Stabilizer Entropy
- Single-copy stabilizer testing
- Agnostic Process Tomography
- Agnostic Tomography of Stabilizer Product States
- The abelian state hidden subgroup problem: Learning stabilizer groups and beyond