Search for: All records

The accurate computational study of wavepacketnuclear dynamics is considered to be a classically intractableproblem, particularly with increasing dimensions. Here, we presenttwo algorithms that, in conjunction with other methods developedby us, may result in one set of contributions for performingquantum nuclear dynamics in arbitrary dimensions. For one of thetwo algorithms discussed here, we present a direct map betweenthe Born−Oppenheimer Hamiltonian describing the nuclearwavepacket time evolution and the control parameters of a spin−lattice Hamiltonian that describes the dynamics of qubit states in anion-trap quantum computer. This map is exact for three qubits, andwhen implemented, the dynamics of the spin states emulates thoseof the nuclear wavepacket in a continuous representation. However, this map becomes approximate as the number of qubits grows.In a second algorithm, we present a general quantum circuit decomposition formalism for such problems using a method called theQuantum Shannon Decomposition. This algorithm is more robust and is exact for any number of qubits at the cost of increasedcircuit complexity. The resultant circuit is implemented on IBM’s quantum simulator (QASM) for 3−7 qubits, without using a noisemodel so as to test the intrinsic accuracy of the method. In both cases, the wavepacket dynamics is found to be in good agreementwith the classical propagation result and the corresponding vibrational frequencies obtained from the wavepacket density timeevolution are in agreement to within a few tenths of a wavenumber. 

The exponential scaling of the quantum degrees of freedom with the size of the system is one of the biggest challenges in computational chemistry and particularly in quantum dynamics. We present a tensor network approach for the time-evolution of the nuclear degrees of freedom of multiconfigurational chemical systems at a reduced storage and computational complexity. We also present quantum algorithms for the resultant dynamics. To preserve the compression advantage achieved via tensor network decompositions, we present an adaptive algorithm for the regularization of nonphysical bond dimensions, preventing the potentially exponential growth of these with time. While applicable to any quantum dynamical problem, our method is particularly valuable for dynamical simulations of nuclear chemical systems. Our algorithm is demonstrated using ab initio potentials obtained for a symmetric hydrogen-bonded system, namely, the protonated 2,2′-bipyridine, and compared to exact diagonalization numerical results. 

We provide an approach to sample rare events during classical ab initio molecular dynamics and quantum wavepacket dynamics. For classical AIMD, a set of fictitious degrees of freedom are introduced that may harmonically interact with the electronic and nuclear degrees of freedom to steer the dynamics in a conservative fashion toward energetically forbidden regions. A similar approach when introduced for quantum wavepacket dynamics has the effect of biasing the trajectory of the wavepacket centroid toward the regions of the potential surface that are difficult to sample. The approach is demonstrated for a phenol-amine system, which is a prototypical problem for condensed phase-proton transfer, and for model potentials undergoing wavepacket dynamics. In all cases, the approach yields trajectories that conserve energy while sampling rare events. 

ABSTRACT The relationship between magnetic field strength B and gas density n in the interstellar medium is of fundamental importance. We present and compare Bayesian analyses of the B–n relation for two comprehensive observational data sets: a Zeeman data set and 700 observations using the Davis–Chandrasekhar–Fermi (DCF) method. Using a hierarchical Bayesian analysis we present a general, multiscale broken power-law relation, $$B=B_0(n/n_0)^{\alpha }$$, with $$\alpha =\alpha _1$$ for $$n< n_0$$ and $$\alpha _2$$ for $$n>n_0$$, and with $$B_0$$ the field strength at $$n_0$$. For the Zeeman data, we find: $$\alpha _1={0.15^{+0.06}_{-0.09}}$$ for diffuse gas and $$\alpha _2 = {0.53^{+0.09}_{-0.07}}$$ for dense gas with $$n_0 = 0.40^{+1.30}_{-0.30}\times 10^4$$ cm$$^{-3}$$. For the DCF data, we find: $$\alpha _1={0.26^{+0.01}_{-0.01}}$$ and $$\alpha _2={0.77_{-0.15}^{+0.14}}$$, with $$n_0=14.00^{+10.00}_{-7.00}\times 10^4$$ cm$$^{-3}$$, where the uncertainties give 68 per cent credible intervals. We perform a similar analysis on nineteen numerical magnetohydrodynamic simulations covering a wide range of physical conditions from protostellar discs to dwarf and Milky Way-like galaxies, computed with the arepo, flash, pencil, and ramses codes. The resulting exponents depend on several physical factors such as dynamo effects and their time-scales, turbulence, and initial seed field strength. We find that the dwarf and Milky Way-like galaxy simulations produce results closest to the observations. 

Prediction of surface topography in milling usually requires complex kinematics and dynamics modeling of the milling process, plus solving physical models of surface generation is a daunting task. This paper presents a multimodal data-driven machine learning (ML) method to predict milled surface topography. The proposed method predicts the height map of the surface topography by fusing process parameters and in-process acoustic information as model inputs. This method has been validated by comparing the predicted surface topography with the measured data. 

Incremental graphs that change over time capture the changing relationships of different entities. Given that many real-world networks are extremely large, it is often necessary to partition the network over many distributed systems and solve a complex graph problem over the partitioned network. This paper presents a distributed algorithm for identifying strongly connected components (SCC) on incremental graphs. We propose a two-phase asynchronous algorithm that involves storing the intermediate results between each iteration of dynamic updates in a novel meta-graph storage format for efficient recomputation of the SCC for successive iterations. To the best of our knowledge, this is the first attempt at identifying SCC for incremental graphs across distributed compute nodes. Our experimental analysis on real and synthesized graphs shows up to 2.8x performance improvement over the state-of-the-art by reducing the overall memory utilized and improving the communication bandwidth. 

We present a graph-theory-based reformulation of all ONIOM-based molecular fragmentation methods. We discuss applications to (a) accurate post-Hartree–Fock AIMD that can be conducted at DFT cost for medium-sized systems, (b) hybrid DFT condensed-phase studies at the cost of pure density functionals, (c) reduced cost on-the-fly large basis gas-phase AIMD and condensed-phase studies, (d) post-Hartree–Fock-level potential surfaces at DFT cost to obtain quantum nuclear effects, and (e) novel transfer machine learning protocols derived from these measures. Additionally, in previous work, the unifying strategy discussed here has been used to construct new quantum computing algorithms. Thus, we conclude that this reformulation is robust and accurate.