Rounding Almost Commuting Hamiltonians
2026-05-25 • Computational Complexity
Computational Complexity
AI summaryⓘ
The authors study quantum systems where parts of the system almost commute, meaning they nearly behave like classical systems but with some quantum effects. They develop an efficient method to approximate these 'almost commuting' quantum systems with fully commuting ones, which are easier to analyze. This approximation lets them better understand the energy properties of such systems and shows that certain quantum problems fall into a complexity class called NP under these conditions. They also show how their method can be used for tasks like simulating the system’s behavior and sampling from its thermal states.
Hamiltonian2-local Hamiltoniancommuting operatorsalmost commutingquantum many-body physicsground state energyNP complexity classGibbs samplingHamiltonian simulationoperator norm
Authors
Islam Faisal, Anand Natarajan, Alexander Poremba
Abstract
Commuting Hamiltonians lie at the boundary between classical constraint satisfaction and quantum many-body physics, exhibiting rich quantum structure while remaining more tractable than general noncommuting models. In contrast, physical Hamiltonians are rarely exactly commuting, which naturally motivates the study of almost commuting Hamiltonians. Despite their relevance, the implications of approximate commutation are only poorly understood. In this work, we show how to efficiently approximate any almost commuting $2$-local qubit Hamiltonian by a commuting one: we give a locality-preserving algorithmic rounding technique that maps any $2$-local Hamiltonian $H=\sum_{i=1}^m h_i$ with $\|[h_i,h_j]\| \leq ε$ to a nearby Hamiltonian $\hat{H}$ whose terms pair-wise commute, and which is within overall distance $\|H-\hat{H}\| = O(m\,ε^{1/6})$. As a consequence, we show that $δ$-approximations to the ground energy for $ε$-almost commuting $2$-local qubit Hamiltonians lie in $\mathsf{NP}$ when $δ\gg mε^{1/6}$, extending the classical containment well beyond the commuting setting. Finally, we present two applications of our rounding framework: Gibbs sampling and fast Hamiltonian simulation for almost commuting systems.