Alexander Zlokapa

We provide a framework for average-case quantum complexity by showing that glassiness obstructs a natural family of quantum algorithms. Glassiness --- a phenomenon in physics characterized by a disordered, slow-mixing phase --- is known to imply hardness for stable classical algorithms; for example, constant-time Langevin dynamics or message-passing fail for random $k$-SAT and max-cut problems in a glassy parameter regime. We present comparable results in the quantum setting with the following contributions. \begin{itemize}[rightmargin=7em] \item \emph{Quantum optimal transport view of glassiness.} We show that the standard notion of quantum glassiness in physics implies that the Gibbs state is decomposed into clusters extensively separated in quantum Wasserstein distance. We prove this implies lower bounds on the quantum Wasserstein complexity of channels from non-glassy to glassy states. \item \emph{Structural argument for hardness.} We define \emph{stable quantum algorithms} in terms of Lipschitz temperature dependence. We prove that constant-time local Lindbladian evolution and shallow variational algorithms are stable and hence fail to capture the clustered geometry of the Gibbs state, yielding a geometrically interpretable algorithmic obstruction. Contrary to prior Lindbladian runtime lower bounds that only apply to evolution from worst-case initial states, our results hold even when starting from the maximally mixed state. \end{itemize} At a technical level, our techniques (based on channel complexity) differ significantly from classical probabilistic approaches due to the sign problem in the absence of a known eigenbasis. This allows our average-case hardness results to apply to non-commuting, non-stoquastic quantum Hamiltonians. As an example, we show the average-case hardness of random 3-local Hamiltonians: the ensemble of all 3-local Pauli strings with independent Gaussian coefficients. To obtain this result, we compute the full replica symmetry breaking solution of the general $p$-local Pauli Hamiltonian ensemble via the replica trick, a non-rigorous but widely used method in statistical physics. The system's phase diagram is richer than its classical (Ising $p$-spin) and fermionic (SYK) analogues, which either always or never have a glassy phase; instead, the Pauli ensemble has a glassy phase only below some constant value of $p$, confirming the phase diagram predicted by prior finite-size numerical analyses.

Community detection is a foundational problem in data science concerned with detecting clustering structure in datasets. A natural extension is hypergraph community detection, which aims to capture higher-order correlations beyond pairwise interactions. In this work, we develop a quantum algorithm for hypergraph community detection and show that it achieves a quartic (power-of-four) speedup over the best known classical algorithm, along with superpolynomial savings in space. The speedup is based on a quantized version of the Kikuchi method and arises from the efficient preparation of a guiding state that has enhanced overlap with the solution space. To support our main result, we prove matching low-coordinate degree function lower bounds, a generalization of sum-of-squares and low-degree likelihood ratio lower bounds, which establishes that the classical baseline algorithm over which we achieve a quartic quantum speedup is (nearly) optimal. Our work constitutes a general framework for identifying when our guiding state–based approach, rooted in the Kikuchi hierarchy, can yield super-quadratic quantum speedups. We suggest that a quantity known as marginal order, which captures the presence of a tradeoff between signal-to-noise ratio and computational cost, effectively determines the existence of quantum Kikuchi-guided algorithms.