Transformer Wave Function for two dimensional frustrated magnets: emergence of a Spin-Liquid Phase in the Shastry-Sutherland Model

Understanding quantum magnetism in two-dimensional systems represents a lively branch in modern condensed-matter physics. In the presence of competing super-exchange

couplings, magnetic order is frustrated and can be suppressed down to zero temperature. Still, capturing the correct nature of the exact ground state is a highly

complicated task, since energy gaps in the spectrum may be very small and states with different physical properties may have competing energies. Here, we introduce a variational {\it Ansatz} for two-dimensional frustrated magnets by leveraging the power of representation learning. The key idea is to use a particular

deep neural network with real-valued parameters, a so-called Transformer, to map physical spin configurations into a high-dimensional feature space. Within this

abstract space, the determination of the ground-state properties is simplified and requires only a shallow output layer with complex-valued parameters. We demonstrate

the effectiveness of this variational {\it Ansatz} by studying the ground-state phase diagram of the Shastry-Sutherland model, which captures the low-temperature behavior of

SrCu$_2$(BO$_3$)$_2$ with its intriguing properties. With highly accurate numerical simulations, we provide strong evidence for the stabilization of a spin-liquid

between the plaquette and antiferromagnetic phases. Our findings underscore the potential of Neural-Network Quantum States as a valuable tool for probing uncharted

phases of matter, and open up new possibilities to establish the properties of many-body systems.