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Found 7 papers, 1 papers with code
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.
Clifford Circuits can be Properly PAC Learned if and only if $\textsf{RP}=\textsf{NP}$
In particular, one candidate class of circuits for which an efficient learner might have been possible was that of Clifford circuits, since the corresponding set of states generated by such circuits, called stabilizer states, are known to be efficiently PAC learnable (Rocchetto 2018).
On the Hardness of PAC-learning Stabilizer States with Noise
Our results position the problem of learning stabilizer states as a natural quantum analogue of the classical problem of learning parities: easy in the noiseless setting, but seemingly intractable even with simple forms of noise.
Investigating Quantum Approximate Optimization Algorithms under Bang-bang Protocols
The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices.
Quantum Physics
