Ansatz-free Hamiltonian learning with Heisenberg-limited scaling
Journal:
arXiv
Published Date:
Feb 17, 2025
Abstract
Learning the unknown interactions that govern a quantum system is crucial for
quantum information processing, device benchmarking, and quantum sensing. The
problem, known as Hamiltonian learning, is well understood under the assumption
that interactions are local, but this assumption may not hold for arbitrary
Hamiltonians. Previous methods all require high-order inverse polynomial
dependency with precision, unable to surpass the standard quantum limit and
reach the gold standard Heisenberg-limited scaling. Whether Heisenberg-limited
Hamiltonian learning is possible without prior assumptions about the
interaction structures, a challenge we term \emph{ansatz-free Hamiltonian
learning}, remains an open question. In this work, we present a quantum
algorithm to learn arbitrary sparse Hamiltonians without any structure
constraints using only black-box queries of the system's real-time evolution
and minimal digital controls to attain Heisenberg-limited scaling in estimation
error. Our method is also resilient to state-preparation-and-measurement
errors, enhancing its practical feasibility. Moreover, we establish a
fundamental trade-off between total evolution time and quantum control on
learning arbitrary interactions, revealing the intrinsic interplay between
controllability and total evolution time complexity for any learning algorithm.
These results pave the way for further exploration into Heisenberg-limited
Hamiltonian learning in complex quantum systems under minimal assumptions,
potentially enabling new benchmarking and verification protocols.
