arXiv:2601.02149v4 Announce Type: replace-cross Abstract: We propose a neural network-based model capable of learning the broad landscape of working regimes in quantum dot simulators, and using this knowledge to autotune these devices - based on transport measurements - toward obtaining Majorana modes in the structure. The model is trained in an unsupervised manner on synthetic data in the form of conductance maps, using a physics-informed loss that incorporates key properties of Majorana zero modes. We show that, with appropriate training, a deep vision-transformer network can efficiently mem
The convergence of advanced AI techniques, particularly neural networks and vision transformers, with the growing sophistication in quantum hardware enables new approaches to controlling complex quantum systems.
This research demonstrates a significant step toward autonomous quantum device tuning, which is critical for scaling quantum computing and developing robust topological qubits resistant to decoherence.
The reliance on human expertise for fine-tuning quantum devices could be significantly reduced, accelerating research and development in quantum computing and potentially enabling more stable quantum states.
- · Quantum computing researchers
- · Quantum hardware manufacturers
- · AI algorithm developers
- · Semiconductor industry
- · Manual quantum device tuning experts
Achieving Majorana modes more efficiently could lead to the development of fault-tolerant topological qubits.
The automation of complex quantum experiments could significantly accelerate the discovery of new quantum phenomena and materials.
Successful topological quantum computing could revolutionize cryptography, drug discovery, and materials science, leading to entirely new industries.
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Read at arXiv cs.AI