Research map / Learning theory and algorithms / Submodular and game algorithms
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the improved dynamic Gibbs sampler in the zero-sum game solver | Quantum zero-sum game solver · Zero-sum games | runtime: √(m + n) · ε^-3 -> √(m + n) · ε^-2.5 + ε^-3 | Helped |
| Maintain a slowly changing Gibbs-distribution hint across phases | Quantum dynamic Gibbs sampler · Sampling maintenance (T = ε^-2, η = ε) | max(t_samp, t_up): √(m + n) · ε^-1 -> √(m + n) · ε^-1/2 | Helped |
| Use an empirical-distribution overestimate as the sampling hint instead of a uniform hint | Quantum dynamic Gibbs sampler · Sampling maintenance | amortized sampling and update runtime: ε^-1 -> ε^-1/2 | Helped |
Closest papers by meaning
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