QUANT-PHCCITLGSTSep 25, 2023

Efficient Pauli channel estimation with logarithmic quantum memory

arXiv:2309.14326v521 citationsh-index: 23
Originality Highly original
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This provides an exponential statistical advantage for quantum learning tasks, enabling efficient noise characterization in quantum devices with minimal quantum memory.

The paper tackled the problem of estimating eigenvalues of an n-qubit Pauli noise channel with error ε, showing that a protocol using only O(log n/ε²) ancilla qubits and Õ(n²/ε²) measurements achieves this, overcoming prior no-go theorems that required exponentially many measurements with limited quantum memory.

Here we revisit one of the prototypical tasks for characterizing the structure of noise in quantum devices: estimating every eigenvalue of an $n$-qubit Pauli noise channel to error $ε$. Prior work [14] proved no-go theorems for this task in the practical regime where one has a limited amount of quantum memory, e.g. any protocol with $\le 0.99n$ ancilla qubits of quantum memory must make exponentially many measurements, provided it is non-concatenating. Such protocols can only interact with the channel by repeatedly preparing a state, passing it through the channel, and measuring immediately afterward. This left open a natural question: does the lower bound hold even for general protocols, i.e. ones which chain together many queries to the channel, interleaved with arbitrary data-processing channels, before measuring? Surprisingly, in this work we show the opposite: there is a protocol that can estimate the eigenvalues of a Pauli channel to error $ε$ using only $O(\log n/ε^2)$ ancilla and $\tilde{O}(n^2/ε^2)$ measurements. In contrast, we show that any protocol with zero ancilla, even a concatenating one, must make $Ω(2^n/ε^2)$ measurements, which is tight. Our results imply, to our knowledge, the first quantum learning task where logarithmically many qubits of quantum memory suffice for an exponential statistical advantage. Our protocol can be naturally extended to a protocol that learns the eigenvalues of Pauli terms within any subset $A$ of a Pauli channel with $O(\log\log(|A|)/ε^2)$ ancilla and $\tilde{O}(n^2/ε^2)$ measurements.

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