Testing and learning structured quantum Hamiltonians

We consider the problems of testing and learning an unknown n-qubit Hamiltonian H from queries to its evolution operator e-iHt under the normalized Frobenius norm. We prove: 1. Local Hamiltonians: We give a tolerant testing protocol to decide if H is ε1-close to k-local or ε2-far from k-local, with O(1/(ε2-ε1)4) queries, solving open questions posed in a recent work by Bluhm et al. For learning a k-local H up to error ε, we give a protocol with query complexity exp(O(k2+klog(1/ε))) independent of n, by leveraging the non-commutative Bohnenblust-Hille inequality. 2. Sparse Hamiltonians: We give a protocol to test if H is ε1-close to being s-sparse (in the Pauli basis) or ε2-far from being s-sparse, with O(s6/(ε22-ε12)6) queries. For learning up to error ε, we show that O(s4/ε8) queries suffice. 3. Learning without memory: The learning results stated above have no dependence on n, but require n-qubit quantum memory. We give subroutines that allow us to learn without memory; increasing the query complexity by a (log n)-factor in the local case and an n-factor in the sparse case. 4. Testing without memory: We give a new subroutine called Pauli hashing, which allows one to tolerantly test s-sparse Hamiltonians with O(s14/(ε22-ε12)18) queries. A key ingredient is showing that s-sparse Pauli channels can be tolerantly tested under the diamond norm with O(s2/(ε2-ε1)6) queries. Along the way, we prove new structural theorems for local and sparse Hamiltonians. We complement our learning results with polynomially weaker lower bounds. Furthermore, our algorithms use short time evolutions and do not assume prior knowledge of the terms in the support of the Pauli spectrum.