Hybrid Ground-State Quantum Algorithms based on Neural Schr\"odinger Forging

Understand

Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems.

• The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis states, or bitstrings, when performing the Schmidt decomposition of the whole system.
• To overcome this challenge, we propose a new method for entanglement forging employing generative neural networks to identify the most pertinent bitstrings, eliminating the need for the exponential sum.
• Through empirical demonstrations on systems of increasing complexity, we show that the proposed algorithm achieves comparable or superior performance compared to the existing standard implementation of entanglement forging.