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Learning quantum states prepared by shallow circuits in polynomial time

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arxiv 2410.23618 v1 pith:TURF4MNQ submitted 2024-10-31 quant-ph cs.CCcs.LG

classification quant-phcs.CCcs.LG
keywords quantumranglevertalgorithmcircuitdepthstateunknown
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We give a polynomial time algorithm that, given copies of an unknown quantum state $\vert\psi\rangle=U\vert 0^n\rangle$ that is prepared by an unknown constant depth circuit $U$ on a finite-dimensional lattice, learns a constant depth quantum circuit that prepares $\vert\psi\rangle$. The algorithm extends to the case when the depth of $U$ is $\mathrm{polylog}(n)$, with a quasi-polynomial run-time. The key new idea is a simple and general procedure that efficiently reconstructs the global state $\vert\psi\rangle$ from its local reduced density matrices. As an application, we give an efficient algorithm to test whether an unknown quantum state on a lattice has low or high quantum circuit complexity.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Certifying localizable quantum properties with constant sample complexity

    quant-ph 2025-09 unverdicted novelty 7.0 of 10

    A new framework certifies global quantum properties including multipartite entanglement, circuit complexity, and quantum magic on small subsystems with constant sample complexity via local Pauli measurements.

  2. Energy-independent tomography of Gaussian states

    quant-ph 2025-08 unverdicted novelty 7.0 of 10

    A tomography protocol estimates Gaussian states in trace distance with sample complexity independent of energy (up to doubly logarithmic factors), a doubly exponential improvement over prior methods.

  3. Artificial intelligence for representing and characterizing quantum systems

    quant-ph 2025-09 unverdicted novelty 1.0 of 10

    A review organizes AI-based quantum system characterization into ML, deep learning, and language model paradigms, covering property prediction and implicit state reconstruction.

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