Neural Quantum Digital Twins for Optimizing Quantum Annealing
This work addresses the problem of optimizing quantum annealing performance for researchers and practitioners in quantum computing, though it appears incremental as it builds on existing digital twin and neural network approaches.
The paper tackles the performance limitations of quantum annealers due to scalability and errors by proposing a Neural Quantum Digital Twin framework that reconstructs energy landscapes and models dynamics, demonstrating accurate capture of quantum phenomena and enabling identification of optimal annealing schedules to minimize errors.
Quantum annealers have shown potential in addressing certain combinatorial optimization problems, though their performance is often limited by scalability and errors rates. In this work, we propose a Neural Quantum Digital Twin (NQDT) framework that reconstructs the energy landscape of quantum many-body systems relevant to quantum annealing. The digital twin models both ground and excited state dynamics, enabling detailed simulation of the adiabatic evolution process. We benchmark NQDT on systems with known analytical solutions and demonstrate that it accurately captures key quantum phenomena, including quantum criticality and phase transitions. Leveraging this framework, one can identify optimal annealing schedules that minimize excitation-related errors. These findings highlight the utility of neural network-based digital twins as a diagnostic and optimization tool for improving the performance of quantum annealers.