Neural Quantum Digital Twins for Optimizing Quantum Annealing

2025-05-21

AI Summary

Quantum annealers suffer from excitation errors and scalability limitations. This work introduces the Neural Quantum Digital Twin (NQDT) framework, the first to integrate physics-constrained neural networks with differentiable quantum many-body simulation. NQDT accurately reconstructs the energy landscape and models both ground-state and excited-state adiabatic evolution to diagnose defects in annealing trajectories. The method enables characterization of quantum criticality and gradient-guided optimization of dynamic annealing schedules. Validated on analytically solvable systems, NQDT achieves energy spectrum reconstruction error below 0.5% and reduces excitation probabilities by one to two orders of magnitude. As the first end-to-end, deployable, error-aware scheduling optimizer for real quantum annealing hardware, NQDT bridges the gap between theoretical quantum control and practical device performance.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Optimizing quantum annealing performance using Neural Quantum Digital Twins
• Modeling ground and excited state dynamics for adiabatic evolution simulation
• Identifying optimal annealing schedules to minimize excitation-related errors

Innovation

• Neural Quantum Digital Twin models energy landscape
• Simulates ground and excited state dynamics
• Optimizes annealing schedules to reduce errors