2025/11/26 by Baßler, Pascal, Heinrich, Markus, Kliesch, Martin
Computer Science · Physics and Astronomy · #Atomic and Nuclear physics #Computer Science #Information and General Works::003: Systems Theory #Information and General Works::004: Computer Sciences #Molecular Physics #Natural Sciences and Mathematics::530: Physics #Natural Sciences and Mathematics::539: Matter #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics #Quantum algorithms information theory qubits #Quantum many-body systems #Radiation
paper · doi:10.15480/882.16254
openalex publication_date 2025/11/26 · openalex created_date 2025/11/28 · openalex updated_date 2026/07/01
Implementing the time evolution under a desired target Hamiltonian is critical for various applications in quantum science. Due to the exponential increase of parameters in the system size and due to experimental imperfections, this task can be challenging in quantum many-body settings. We introduce an efficient and robust scheme to engineer arbitrary local many-body Hamiltonians. To this end, our scheme applies single-qubit π or π/2 pulses to an always-on system Hamiltonian, which we assume to be native to a given platform. These sequences are constructed by efficiently solving a linear program (LP) which minimizes the total evolution time. In this way, we can engineer target Hamiltonians that are only limited by the locality of the interactions in the system Hamiltonian. Based on average Hamiltonian theory and by using robust composite pulses, we make our schemes robust against errors, including finite-pulse-time errors and various control errors. To demonstrate the performance of our scheme, we provide numerical simulations. In particular, we solve the Hamiltonian-engineering problem on a laptop for arbitrary two-local Hamiltonians on a two-dimensional square lattice with 196 qubits in only 60 s. Moreover, we simulate the engineering of general Heisenberg Hamiltonians from Ising Hamiltonians with imperfect single-qubit pulses for smaller system sizes, and achieve a fidelity larger than 99.9%, which is orders of magnitude better than nonrobust implementations.