Deep learning for quantum computing applications

Computer science and engineering

The collaboration between the two fields quantum computing and deep learning is explored for various applications, including optimizing quantum algorithms, mitigating errors in quantum hardware, and developing hybrid quantum-classical systems. The deep learning approach proves valuable in handling the complexities of quantum data, contributing to tasks such as quantum state tomography and error correction. Quantum states, inherently complex, find resonance with the pattern recognition capabilities of deep neural networks. We investigate the role of deep learning in deciphering intricate patterns within quantum data, contributing to quantum state tomography and error correction. Moreover, the emergence of quantum neural networks serves as a bridge between classical and quantum computing paradigms, paving the way for hybrid solutions that harness the strengths of both. Additionally, the emergence of quantum neural networks facilitates a seamless integration between classical and quantum computing, unlocking the potential for hybrid solutions. The paper reviews key contributions in algorithm optimization, quantum machine learning, and the development of quantum neural networks, offering insights into the evolving landscape at the intersection of deep learning and quantum computing.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.