A projected least-squares quantum measurement tomography protocol is shown to achieve dimension-optimal sample complexity (up to log factors) for worst-case and average-case distances, with a provable gap in the number-of-outcomes scaling.
Sample-Optimal Quantum Process Tomography with Non-Adaptive Incoherent Measurements
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abstract
How many copies of a quantum process are necessary and sufficient to construct an approximate classical description of it? We extend the result of Surawy-Stepney, Kahn, Kueng, and Guta (2022) to show that $\tilde{\mathcal{O}}(d_{\text{in}}^3d_{\text{out}}^3/\varepsilon^2)$ copies are sufficient to learn any quantum channel $C^{d_{\text{in}}\times d_{\text{in}}} \rightarrow C^{d_{\text{out}}\times d_{\text{out}}}$ to within $\varepsilon$ in diamond norm. Moreover, we show that $\Omega(d_{\text{in}}^3 d_{\text{out}}^3/\varepsilon^2)$ copies are necessary for any strategy using incoherent non-adaptive measurements. This lower bound applies even for ancilla-assisted strategies.
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Fast quantum measurement tomography with optimal error bounds
A projected least-squares quantum measurement tomography protocol is shown to achieve dimension-optimal sample complexity (up to log factors) for worst-case and average-case distances, with a provable gap in the number-of-outcomes scaling.