Rapidly Achieving Chemical Accuracy with Quantum Computing Enforced Language Model

• URL: http://arxiv.org/abs/2405.09164v1
• Date: Wed, 15 May 2024 07:50:57 GMT
• Title: Rapidly Achieving Chemical Accuracy with Quantum Computing Enforced Language Model
• Authors: Honghui Shang, Xiongzhi Zeng, Ming Gong, Yangju Wu, Shaojun Guo, Haoran Qian, Chen Zha, Zhijie Fan, Kai Yan, Xiaobo Zhu, Zhenyu Li, Yi Luo, Jian-Wei Pan, Jinlong Yang,
• Abstract summary: QiankunNet-VQE is a transformer based language models enforced with quantum computing to learn and generate quantum states. It has been implemented using up to 12 qubits and attaining an accuracy level competitive with state-of-the-art classical methods.
• Score: 22.163742052849432
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Finding accurate ground state energy of a many-body system has been a major challenge in quantum chemistry. The integration of classic and quantum computers has shed new light on resolving this outstanding problem. Here we propose QiankunNet-VQE, a transformer based language models enforced with quantum computing to learn and generate quantum states. It has been implemented using up to 12 qubits and attaining an accuracy level competitive with state-of-the-art classical methods. By leveraging both quantum and classical resources, this scheme overcomes the limitations of variational quantum eigensolver(VQE) without the need for cumbersome error mitigation. Moreover, QiankunNet-VQE provides a different route to achieve a practical quantum advantage for solving many-electron Schr\"odinger equation without requiring extremely precise preparation and measurement of the ground-state wavefunction on quantum computer.

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