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Agnostic Tomography of Stabilizer Product States
We define a quantum learning task called agnostic tomography, where given copies of an arbitrary state $\rho$ and a class of quantum states $\mathcal{C}$, the goal is to output a succinct description of a state that approximates $\rho$ at least as well as any state in $\mathcal{C}$ (up to some small error $\varepsilon$).
Efficient Learning of Quantum States Prepared With Few Non-Clifford Gates II: Single-Copy Measurements
Recent work has shown that $n$-qubit quantum states output by circuits with at most $t$ single-qubit non-Clifford gates can be learned to trace distance $\epsilon$ using $\mathsf{poly}(n, 2^t, 1/\epsilon)$ time and samples.
Efficient Learning of Quantum States Prepared With Few Non-Clifford Gates
We give a pair of algorithms that efficiently learn a quantum state prepared by Clifford gates and $O(\log n)$ non-Clifford gates.
Low-Stabilizer-Complexity Quantum States Are Not Pseudorandom
We show that quantum states with "low stabilizer complexity" can be efficiently distinguished from Haar-random.
Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages
Reasoning on large and complex real-world models is a computationally difficult task, yet one that is required for effective use of many AI applications.
