Polynomial Equivalence of Complexity Geometries
Adam R. Brown

TL;DR
This paper demonstrates that a wide class of quantum complexity geometries are polynomially equivalent, meaning they have similar computational power and can approximate each other efficiently, broadening the scope of Nielsen's geometric approach to quantum complexity.
Contribution
It establishes the polynomial equivalence of a broad class of right-invariant metrics on the unitary group, extending Nielsen's earlier results to include metrics with modest curvature.
Findings
All metrics in the equivalence class have exponential diameter.
Metrics in the operator-norm class define the BQP complexity class.
The enlarged class includes metrics with modest curvature, aiding geometric analysis.
Abstract
This paper proves the polynomial equivalence of a broad class of definitions of quantum computational complexity. We study right-invariant metrics on the unitary group -- often called `complexity geometries' following the definition of quantum complexity proposed by Nielsen -- and delineate the equivalence class of metrics that have the same computational power as quantum circuits. Within this universality class, any unitary that can be reached in one metric can be approximated in any other metric in the class with a slowdown that is at-worst polynomial in the length and number of qubits and inverse-polynomial in the permitted error. We describe the equivalence classes for two different kinds of error we might tolerate: Killing-distance error, and operator-norm error. All metrics in both equivalence classes are shown to have exponential diameter; all metrics in the operator-norm…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Complexity and Algorithms in Graphs · Computability, Logic, AI Algorithms
