标题： 基于混合量子-神经波函数的分子能量量子机器学习 显示英文标题

标题： Quantum Machine Learning of Molecular Energies with Hybrid Quantum-Neural Wavefunction

摘要： 量子计算化学通过利用量子位来表示分子波函数，比经典方法在模拟分子系统方面具有更大的潜力。 然而，当前的实现由于硬件噪声和算法限制，在准确性方面面临重大局限。 为克服这些挑战，我们引入了一种混合框架，该框架使用高效的量子电路和深度神经网络相结合的方法来学习分子波函数。 这种方法提高了计算效率和准确性，超过了传统的量子计算化学方法。 对分子系统的数值基准测试表明，我们的混合量子-神经波函数方法实现了接近化学精度的结果，与先进的量子和经典技术相当。 在超导量子计算机上进行的实验验证，使用了环丁二烯的异构化反应，进一步证明了其实际应用价值，表现出高精度的能量估计和对噪声的显著抗性。 显示英文摘要

摘要： Quantum computational chemistry holds great promise for simulating molecular systems more efficiently than classical methods by leveraging quantum bits to represent molecular wavefunctions. However, current implementations face significant limitations in accuracy due to hardware noise and algorithmic constraints. To overcome these challenges, we introduce a hybrid framework that learns molecular wavefunction using a combination of an efficient quantum circuit and a deep neural network. This approach enhances computational efficiency and accuracy, surpassing traditional quantum computational chemistry methods. Numerical benchmarking on molecular systems shows that our hybrid quantum-neural wavefunction approach achieves near-chemical accuracy, comparable to advanced quantum and classical techniques. Experimental validation on a superconducting quantum computer, using the isomerization reaction of cyclobutadiene, further demonstrates its practical applicability, with high accuracy in energy estimation and significant resilience to noise.