Single-Particle Spectral Estimation

Abstract: We study Hamiltonian learning in a novel setting, where we are promised a physical structure that is obfuscated by a global unitary. In particular, we consider the task of learning the weights $E_i$ of a Hamiltonian $$H=U^{{\dagger}(\sum_{i} \in I} E_i n_i + H_O)U$$ comprised of a set of non-interacting modes $I$ and an arbitrary ``spectator'' $H_O$ acting on a disjoint set of modes $O$. The non-interacting structure is hidden by an unknown global unitary $U$, and our goal is to recover the $E_i$ without reconstructing $U$. Such Hamiltonians are well-motivated in the setting of materials modeling, where they find application for classical methods such as mean-field theory, Green's functions, and density functional theory. We show that this problem is DQC1-hard, despite the apparent simplification given by the non-interacting structure. We then introduce SPICES (single-particle inference via contour estimators), which combines a Hadamard test with a novel classical postprocessing procedure to efficiently recover the spectral distribution in Wasserstein distance under certain natural assumptions. The method exploits the correspondence between single-particle energies of the individual quasiparticles and the Fourier frequencies governing zeros of the partition function at complex inverse temperature. As quasiparticle spectra feature ubiquitously in downstream materials calculations, and with SPICES being simple and practical to execute, we expect that SPICES is a promising route to quantum applications in materials simulation.