Quantum computation of molecular structure using data from challenging-to-classically-simulate nuclear magnetic resonance experiments.
Quantum computation of molecular structure using data from challenging-to-classically-simulate nuclear magnetic resonance experiments.
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DOI：
10.1103/prxquantum.3.030345
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
T. O’Brien;L. Ioffe;Yuan Su;D. Fushman;H. Neven;R. Babbush;V. Smelyanskiy
中科院分区：
文献类型：
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作者：
T. O’Brien;L. Ioffe;Yuan Su;D. Fushman;H. Neven;R. Babbush;V. Smelyanskiy
我们提出了一个量子算法来推断分子核自旋哈密顿量的自旋-自旋自旋相互作用的时间分辨测量，这可以通过核磁共振（NMR）获得。我们专注于学习哈密顿量的各向异性偶极项，它产生的动力学在某些情况下是具有挑战性的经典模拟。我们证明了在量子计算机上直接估计相应学习问题的雅可比矩阵和海森矩阵的能力，使我们能够学习哈密顿参数。我们开发的算法进行这种计算的噪声近期和未来的容错量子计算机。我们认为，前者是有前途的早期超越经典的量子应用，因为它只需要一个本地自旋哈密顿量的演变。我们调查的蛋白质（泛素）限制在膜上作为我们的方法的基准的例子。我们孤立的小自旋集群，证明我们的学习算法的收敛性在这样的例子，然后研究这些集群的可学习性，因为我们通过抑制偶极相互作用跨越遍历到非遍历相变。我们看到，在这些簇的多体本征态上测量的多重分形维数的下降与学习成本函数的Hessian结构的转变（从简并到可学习）之间存在明显的对应关系。我们希望这样的量子计算能够解释和发展新的核磁共振技术来分析分子结构。
We propose a quantum algorithm for inferring the molecular nuclear spin Hamiltonian from time-resolved measurements of spin-spin correlators, which can be obtained via nuclear magnetic resonance (NMR). We focus on learning the anisotropic dipolar term of the Hamiltonian, which generates dynamics that are challenging to classically simulate in some contexts. We demonstrate the ability to directly estimate the Jacobian and Hessian of the corresponding learning problem on a quantum computer, allowing us to learn the Hamiltonian parameters. We develop algorithms for performing this computation on both noisy near-term and future fault-tolerant quantum computers. We argue that the former is promising as an early beyond-classical quantum application since it only requires evolution of a local spin Hamiltonian. We investigate the example of a protein (ubiquitin) confined on a membrane as a benchmark of our method. We isolate small spin clusters, demonstrate the convergence of our learning algorithm on one such example, and then investigate the learnability of these clusters as we cross the ergodic to non-ergodic phase transition by suppressing the dipolar interaction. We see a clear correspondence between a drop in the multifractal dimension measured across many-body eigenstates of these clusters, and a transition in the structure of the Hessian of the learning cost function (from degenerate to learnable). Our hope is that such quantum computations might enable the interpretation and development of new NMR techniques for analyzing molecular structure.