  Quantum computing has shown great potential in various quantum chemical
applications such as drug discovery, material design, and catalyst
optimization. Although significant progress has been made in quantum simulation
of simple molecules, ab initio simulation of solid-state materials on quantum
computers is still in its early stage, mostly owing to the fact that the system
size quickly becomes prohibitively large when approaching the thermodynamic
limit. In this work, we introduce an orbital-based multi-fragment approach on
top of the periodic density matrix embedding theory, resulting in a
significantly smaller problem size for the current near-term quantum computer.
We demonstrate the accuracy and efficiency of our method compared with the
conventional methodologies and experiments on solid-state systems with complex
electronic structures. These include spin polarized states of a hydrogen chain
(1D-H), the equation of states of a boron nitride layer (h-BN) as well as the
magnetic ordering in nickel oxide (NiO), a prototypical strongly correlated
solid. Our results suggest that quantum embedding combined with a chemically
intuitive fragmentation can greatly advance quantum simulation of realistic
materials, thereby paving the way for solving important yet classically hard
industrial problems on near-term quantum devices.
