  For decades, people are developing efficient numerical methods for solving
the challenging quantum many-body problem, whose Hilbert space grows
exponentially with the size of the problem. However, this journey is far from
over, as previous methods all have serious limitations. The recently developed
deep learning methods provide a very promising new route to solve the
long-standing quantum many-body problems. We report that a deep learning based
simulation protocol can achieve the solution with state-of-the-art precision in
the Hilbert space as large as $2^{1296}$ for spin system and $3^{144}$ for
fermion system , using a HPC-AI hybrid framework on the new Sunway
supercomputer. With highly scalability up to 40 million heterogeneous cores,
our applications have measured 94% weak scaling efficiency and 72% strong
scaling efficiency. The accomplishment of this work opens the door to simulate
spin models and Fermion models on unprecedented lattice size with extreme high
precision.
