Many Hamiltonians Are Sparsifiable

TL;DR

This paper demonstrates that many Hamiltonians can be efficiently sparsified, reducing the number of terms significantly while preserving their spectral properties, challenging previous beliefs about their inherent complexity.

Contribution

The authors prove that a wide class of Hamiltonians are sparsifiable to a much smaller set of terms, including those with local Pauli strings and certain rank conditions, showing sparsifiability is a robust phenomenon.

Findings

Hamiltonians with local Pauli strings are sparsifiable to fewer terms.

Hamiltonians with local random operators of rank R are sparsifiable, R ≥ 2^{r-1}+1.

Hamiltonians with arbitrary local operators of rank ≥ 2^r - 1 are also sparsifiable.

Abstract

We study the problem of Hamiltonian sparsification: given a parameter $\varepsilon \in (0,1)$ and an $n$-qubit Hamiltonian $H$ which is the sum of $r$-local positive semi-definite (PSD) terms $H_1, \dots H_m$, our goal is to compute a sparse set $L \subseteq [m]$, along with weights $w: L \rightarrow \mathbb{R}_{\geq 0}$ such that for every state $|\psi\rangle\in \mathbb{C}^{2^n}$, $$ \sum_{i \in L} w(i) \langle \psi | H_i | \psi \rangle \in (1 \pm \epsilon) \sum_{i = 1}^m \langle \psi | H_i | \psi \rangle $$. When the set $L$ is significantly smaller than $m$, this reduces the number of terms in the underlying system, while still ensuring that the behavior of the system is essentially unchanged. We show that many Hamiltonians indeed are sparsifiable to a number of terms much smaller than $n^r$, including: (a) Hamiltonians where each term is an $r$-local Pauli string, (b) Hamiltonians where each term is an $r$-local random operator of rank $R$, for $R \geq 2^{r-1}+1$, and (c) Hamiltonians where each term is an arbitrary $r$-local operator of rank $\geq 2^r -1$ (a.k.a. Quantum SAT). Taken together, our results show that the sparsifiability of Hamiltonians is a robust phenomenon, contrary to prevailing belief (see for instance, Aharonov-Zhou ITCS 2019, QIP 2019). Our results find applications, for instance, to better (semi-)streaming algorithms for quantum Max-Cut, answering a question left open by Kallaugher and Parekh (FOCS 2022). In fact, our results even codify that quantum systems are often easier to sparsify than their classical counterparts.