Quantum and classical simulations of molecular clusters

In classical computational chemistry, the coupled-cluster ansatz is one of the most commonly used ab initio methods, which is critically limited by its nonunitary nature. The unitary modification as an ideal solution to the problem is, however, extremely inefficient in classical conventional computation. Here, we provide experimental evidence that indeed the unitary version of the coupled-cluster ansatz can be reliably performed in a physical quantum system, a trapped-ion system. We perform a simulation on the electronic structure of a molecular ion ( ${\mathrm{HeH}}^{+}$ ), where the ground-state energy surface curve is probed, the energies of the excited states are studied, and bond dissociation is simulated nonperturbatively. Our simulation takes advantages from quantum computation to overcome the intrinsic limitations in classical computation, and our experimental results indicate that the method is promising for preparing molecular ground states for quantum simulations.In classical computational chemistry, the coupled-cluster ansatz is one of the most commonly used ab initio methods, which is critically limited by its nonunitary nature. The unitary modification as an ideal solution to the problem is, however, extremely inefficient in classical conventional computation. Here, we provide experimental evidence that indeed the unitary version of the coupled-cluster ansatz can be reliably performed in a physical quantum system, a trapped-ion system. We perform a simulation on the electronic structure of a molecular ion (${\mathrm{HeH}}^{+}$), where the ground-state energy surface curve is probed, the energies of the excited states are studied, and bond dissociation is simulated nonperturbatively. Our simulation takes advantages from quantum computation to overcome the intrinsic limitations in classical computation, and our experimental results indicate that the method is promising for preparing molecular ground states for quantum simulations.

arXiv:1506.00443Physical Review AFeb 15, 2017295 citations

Accelerating quantum molecular simulations

Tran Doan Huan

Nature Computational ScienceMay 19, 20220 citations

Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz

Jonathan Romero, Ryan Babbush, Jarrod R. McClean +3 more

Abstract The variational quantum eigensolver (VQE) algorithm combines the ability of quantum computers to efficiently compute expectation values with a classical optimization routine in order to approximate ground state energies of quantum systems. In this paper, we study the application of VQE to the simulation of molecular energies using the unitary coupled cluster (UCC) ansatz. We introduce new strategies to reduce the circuit depth for the implementation of UCC and improve the optimization of the wavefunction based on efficient classical approximations of the cluster amplitudes. Additionally, we propose an analytical method to compute the energy gradient that reduces the sampling cost for gradient estimation by several orders of magnitude compared to numerical gradients. We illustrate our methodology with numerical simulations for a system of four hydrogen atoms that exhibit strong correlation and show that the circuit depth of VQE using a UCC ansatz can be reduced without introducing significant loss of accuracy in the final wavefunctions and energies.Abstract The variational quantum eigensolver (VQE) algorithm combines the ability of quantum computers to efficiently compute expectation values with a classical optimization routine in order to approximate ground state energies of quantum systems. In this paper, we study the application of VQE to the simulation of molecular energies using the unitary coupled cluster (UCC) ansatz. We introduce new strategies to reduce the circuit depth for the implementation of UCC and improve the optimization of the wavefunction based on efficient classical approximations of the cluster amplitudes. Additionally, we propose an analytical method to compute the energy gradient that reduces the sampling cost for gradient estimation by several orders of magnitude compared to numerical gradients. We illustrate our methodology with numerical simulations for a system of four hydrogen atoms that exhibit strong correlation and show that the circuit depth of VQE using a UCC ansatz can be reduced without introducing significant loss of accuracy in the final wavefunctions and energies.

arXiv:1701.02691Quantum Science and TechnologyJul 17, 2018726 citations

Quantum Monte Carlo Simulations for Molecular Systems

Tanaka Shigenori

IEICE Technical Report; IEICE Tech. Rep.Oct 14, 20110 citations

Quantum Computation of Molecular Structure Using Data from Challenging-To-Classically-Simulate Nuclear Magnetic Resonance Experiments

Thomas E. O’Brien, L. B. Ioffe, Yuan Su +4 more

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.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.

arXiv:2109.02163PRX QuantumSep 27, 202217 citations

{\mathrm{HeH}}^{+}

), where the ground-state energy surface curve is probed, the energies of the excited states are studied, and bond dissociation is simulated nonperturbatively. Our simulation takes advantages from quantum computation to overcome the intrinsic limitations in classical computation, and our experimental results indicate that the method is promising for preparing molecular ground states for quantum simulations.