DQC1-completeness of normalized trace estimation for functions of log-local Hamiltonians

arXiv:2604.01519 · quant-ph, cs.CC · Submitted 2026-04-02 · Read on arXiv

quant-ph, cs.CC

Submitted: 2026-04-02

Updated: 2026-09-23

Comments: With the assistance of GPT-5.6 Sol, we have further relaxed this "technical condition'' to the assumption that $f(x)$ is Lipschitz continuous. To appear in FOCS 2026

Project page: https://changpeng-shao.github.io/DQC1_trace_estimation_

License: http://creativecommons.org/licenses/by/4.0/

The gist: We study the computational complexity of estimating the normalized trace 2-n Tr[f(A)] for a log-local Hamiltonian A acting on n qubits.

Terminology

Abstract

We study the computational complexity of estimating the normalized trace 2-n Tr[f(A)] for a log-local Hamiltonian A acting on n qubits. This problem arises naturally in the DQC1 model, yet its complexity is only understood for a limited class of functions f(x). We show that if f(x) is a continuous function with approximate degree Ω(poly(n)), then estimating 2-n Tr[f(A)] up to constant additive error is DQC1-complete, under a technical condition on the polynomial approximation error of f(x). This condition holds for a broad class of functions, including exponentials, trigonometric functions, logarithms, and inverse-type functions. We further prove that when A is sparse, the classical query complexity of this problem is exponential in the approximate degree. Together, these results identify the approximate degree as the key parameter governing the complexity of normalized trace estimation: it characterizes both the quantum complexity (via efficient DQC1 algorithms) and the classical hardness, yielding an exponential quantum-classical separation. Our proof develops a unified framework that cleanly combines circuit-to-Hamiltonian constructions, periodic Jacobi operators, and tools from polynomial approximation theory, including the Chebyshev equioscillation theorem.

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