Quantum Advantage via Solving Multivariate Quadratics
Pierre Briaud, Riddhi Ghosal, Aayush Jain, Paul Lou, Amit Sahai

TL;DR
This paper demonstrates a quantum algorithm that efficiently solves certain multivariate quadratic equations over finite fields, providing a potential quantum advantage and challenging the classical hardness assumptions of such problems.
Contribution
The work introduces a quantum polynomial-time algorithm for specific multivariate quadratic systems, replacing the random oracle in prior schemes with degree-2 polynomials, and challenges classical hardness assumptions.
Findings
Quantum algorithm solves specific multivariate quadratic systems efficiently.
Counterexample to the belief that classical hardness implies quantum hardness.
Replaces random oracle with degree-2 polynomial families in quantum advantage schemes.
Abstract
In this work, we propose a new way to (non-interactively, verifiably) demonstrate Quantum Advantage by solving the average-case search problem of finding a solution to a system of (underdetermined) multivariate quadratic equations over the finite field drawn from a specified distribution. In particular, we design a distribution of degree-2 polynomials for over for which we show that there is a quantum polynomial-time algorithm that simultaneously solves for a random vector . On the other hand, while a solution exists with high probability, we conjecture that it is classically hard to find one based on classical cryptanalysis that we provide, including a comprehensive review of all known relevant classical algorithms for solving multivariate…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture
