Algorithmic Polynomials
Alexander A. Sherstov

TL;DR
This paper introduces a classical, constructive approach to bounding the approximate degree of Boolean functions, providing explicit polynomials for key problems and revealing a natural problem with a polynomial gap between approximate degree and quantum query complexity.
Contribution
It develops a first-principles, classical method for polynomial approximation, yielding explicit bounds for fundamental problems and challenging previous conjectures about surjectivity.
Findings
Constructive upper bounds for approximate degree of key problems.
Explicit, closed-form approximating polynomials unrelated to quantum arguments.
Refutation of the conjecture that surjectivity has approximate degree $ ext{Omega}(n)$.
Abstract
The approximate degree of a Boolean function is the minimum degree of a real polynomial that approximates pointwise within . Upper bounds on approximate degree have a variety of applications in learning theory, differential privacy, and algorithm design in general. Nearly all known upper bounds on approximate degree arise in an existential manner from bounds on quantum query complexity. We develop a first-principles, classical approach to the polynomial approximation of Boolean functions. We use it to give the first constructive upper bounds on the approximate degree of several fundamental problems: - for the -element distinctness problem; - for the -subset sum problem; - for any -DNF or -CNF formula; - for the…
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Videos
Algorithmic Polynomials· youtube
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Complexity and Algorithms in Graphs · Machine Learning and Algorithms
