activity
20172026
most citedDetermining a local Hamiltonian from a single eigenstate

142 citations · 342 across the 20 of their papers we have counts for

collaborators

21 papers

quant-ph2026

Emergent classicality and wavefunction branching in an isolated quantum many-body system

Saúl Pilatowsky-Cameo, Jordan Cotler, Daniel Ranard +1

Decoherence in quantum systems is conventionally modeled as the effect of interactions with an external environment. However, such a prescription excludes isolated many-body system…

cs.LG2026

Likelihood scoring for continuations of mathematical text: a self-supervised benchmark with tests for shortcut vulnerabilities

Daniel Ranard

We introduce an automatically generated benchmark for predicting hidden text in technical papers. A paper supplies visible context and a hidden continuation ; the evaluated…

quant-ph2026

Approximate QCAs in one dimension using approximate algebras

Daniel Ranard, Michael Walter, Freek Witteveen

Quantum cellular automata (QCAs) are automorphisms of tensor product algebras that preserve locality, with local quantum circuits as a simple example. We study approximate QCAs, wh…

quant-ph2025

The threshold for quantum-classical correspondence is

Felipe Hernández, Daniel Ranard, C. Jess Riedel

In chaotic quantum systems, an initially localized quantum state can deviate strongly from the corresponding classical phase-space distribution after the Ehrenfest time $t_{\mathrm…

quant-ph2025

Lower bounding the MaxCut of high girth 3-regular graphs using the QAOA

Edward Farhi, Sam Gutmann, Daniel Ranard +1

We study MaxCut on 3-regular graphs of minimum girth for various 's. We obtain new lower bounds on the maximum cut achievable in such graphs by analyzing the Quantum Approxi…

quant-ph2024★ 1 cited

Learning State Preparation Circuits for Quantum Phases of Matter

Hyun-Soo Kim, Isaac H. Kim, Daniel Ranard

Many-body ground state preparation is an important subroutine used in the simulation of physical systems. In this paper, we introduce a flexible and efficient framework for obtaini…