Research map / Learning theory and algorithms / Sample complexity
Learning Distributions over Quantum Measurement Outcomes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use distribution threshold search and online learning instead of estimating each outcome event through separate two-outcome shadow tomography | K-outcome shadow tomography · K-outcome shadow tomography | copies of ρ: \tilde{O}(K^4) -> \tilde{O}(K) | Helped |
| Extend the proposed algorithm to multiple outcomes while retaining the two-outcome sample-complexity dependence on M, d, and ε | Shadow tomography · K-outcome shadow tomography | copies of ρ: \tilde{O}\left(\frac{\log(1/\delta)}{\epsilon^4}\cdot K\cdot\log^2M\cdot\log d\right) | Mixed |
| Use the proposed procedure rather than unrestricted estimation for the K dependence | K-outcome shadow tomography · K-outcome shadow tomography | copies of ρ: \tilde{O}(K) upper bound; \Omega(K) dependence lower bound | No clear effect |
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