Work

Improved Rodeo Algorithm Performance for Spectral Functions and State Preparation

Year: 2026

Type: preprint

Abstract: The Rodeo Algorithm is a quantum computing method for computing the energy spectrum of a Hamiltonian and preparing its energy eigenstates. We discuss how to improve the performance of the rodeo algorithm for each of these two applications. In particular, we demonstrate that using a geometric series of time samples offers a near-optimal optimization space for a given total runtime by studying the Rodeo Algorithm performance on a model Hamiltonian representative of gapped many-body quantum systems. Analytics explain the performance of this time sampling and the conditions for it to maintain the established exponential performance of the Rodeo Algorithm. We finally demonstrate this sampling protocol on various physical Hamiltonians, showing its practical applicability. Our results suggest that geometric series of times provide a practical, near-optimal, and robust time-sampling strategy for quantum state preparation with the Rodeo Algorithm across varied Hamiltonians without requiring model-specific fine-tuning.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2602.05978

Authors: Patkowski, Matthew, Ayyildiz, Onat, Hunt, Katherine, Jansen, Nathan, Lee, Dean

Institutions: University of Colorado Boulder, Michigan State University

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, U.S. Department of Energy, Office of Science, Division of Chemistry

Awards: THEORETICAL NUCLEAR STRUCTURE, Nonadiabatic Transition Probabilities: Applications in Spectroscopy, Quantum Thermodynamics, and Quantum Computing, G4675925564, THEORETICAL NUCLEAR STRUCTURE, Efficient Quantum State Preparation Algorithms, G6353015704, G8110749304

OpenAlex ID: W7127900880

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