Yihui Quek
@quekpottheories.bsky.social
Turning tea into tea-orems every day. MIT quantum postdoc.
"Orange" you glad that quantum advantage only requires two-local Hamiltonians? ;) Warm thanks to Simons Institute for the Theory of Computing and US Department of Energy, Office
of Science, National Quantum Information Science Research Centers, Quantum Systems Accelerator for supporting my work!
of Science, National Quantum Information Science Research Centers, Quantum Systems Accelerator for supporting my work!
October 9, 2025 at 12:54 PM
"Orange" you glad that quantum advantage only requires two-local Hamiltonians? ;) Warm thanks to Simons Institute for the Theory of Computing and US Department of Energy, Office
of Science, National Quantum Information Science Research Centers, Quantum Systems Accelerator for supporting my work!
of Science, National Quantum Information Science Research Centers, Quantum Systems Accelerator for supporting my work!
As a bonus, I also introduce an algorithmic version of the Berlekamp-Welch algorithm, resolving an open question from the random circuit sampling literature.
October 9, 2025 at 12:49 PM
As a bonus, I also introduce an algorithmic version of the Berlekamp-Welch algorithm, resolving an open question from the random circuit sampling literature.
This works for analog devices with only coarse-grained control.
My argument relies on a worst-to-average-case reduction for output probabilities, for which I introduce a new trick: Slicing and dicing the sphere. This reduces multivariate to univariate polynomial interpolation.
My argument relies on a worst-to-average-case reduction for output probabilities, for which I introduce a new trick: Slicing and dicing the sphere. This reduces multivariate to univariate polynomial interpolation.
October 9, 2025 at 12:49 PM
This works for analog devices with only coarse-grained control.
My argument relies on a worst-to-average-case reduction for output probabilities, for which I introduce a new trick: Slicing and dicing the sphere. This reduces multivariate to univariate polynomial interpolation.
My argument relies on a worst-to-average-case reduction for output probabilities, for which I introduce a new trick: Slicing and dicing the sphere. This reduces multivariate to univariate polynomial interpolation.