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arXiv:2510.07495

3-Local Hamiltonian Problem and Constant Relative Error Quantum Partition Function Approximation: $O(2^{\frac{n}{2}})$ Algorithm Is Nearly Optimal under QSETH

Nai-Hui Chia, Yu-Ching Shen

Published Oct 8, 2025• 0 citations
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Abstract

We investigate the computational complexity of the Local Hamiltonian (LH) problem and the approximation of the Quantum Partition Function (QPF), two central problems in quantum many-body physics and quantum complexity theory. Both problems are known to be QMA-hard, and under the widely believed assumption that $\mathsf{BQP} \neq \mathsf{QMA}$, no efficient quantum algorithm exits. The best known quantum algorithm for LH runs in $O\bigl(2^{\frac{n}{2}(1 - o(1))}\bigr)$ time, while for QPF, the state-of-the-art algorithm achieves relative error $δ$ in $O^\ast\bigl(\frac{1}δ\sqrt{\frac{2^n}{Z}}\bigr)$ time, where $Z$ denotes the value of the partition function. A nature open question is whether more efficient algorithms exist for both problems. In this work, we establish tight conditional lower bounds showing that these algorithms are nearly optimal. Under the plausible Quantum Strong Exponential Time Hypothesis (QSETH), we prove that no quantum algorithm can solve either LH or approximate QPF significantly faster than $O(2^{n/2})$, even for 3-local Hamiltonians. In particular, we show: 1) 3-local LH cannot be solved in time $O(2^{\frac{n}{2}(1-\varepsilon)})$ for any $\varepsilon > 0$ under QSETH; 2) 3-local QPF cannot be approximated up to any constant relative error in $O(2^{\frac{n}{2}(1-\varepsilon)})$ time for any $\varepsilon > 0$ under QSETH; and 3) we present a quantum algorithm that approximates QPF up to relative error $1/2 + 1/\mathrm{poly}(n)$ in $O^\ast(2^{n/2})$ time, matching our conditional lower bound. Notably, our results provide the first fine-grained lower bounds for both LH and QPF with fixed locality. This stands in sharp contrast to QSETH and the trivial fine-grained lower bounds for LH, where the locality of the SAT instance and the Hamiltonian depends on the parameter $\varepsilon$ in the $O(2^{\frac{n}{2}(1-\varepsilon)})$ running time.

DOI
10.48550/arxiv.2510.07495

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