arXiv subjects

K. Asnaashari

Publications and source records attributed to K. Asnaashari.

4 recordsLinked to original sources

arXiv2024

General classification of qubit encodings in ultracold diatomic molecules

Owing to their rich internal structure and significant long-range interactions, ultracold molecules have been widely explored as carriers of quantum information. Several different schemes for encoding qubits into molecular states, both bare and field-dressed, have been proposed. At the same time, the rich internal structure of molecules leaves many unexplored possibilities for qubit encodings. We show that all molecular qubit encodings can be classified into four classes by the type of the effective interaction between the qubits. In the case of polar molecules, the four classes are determined by the relative magnitudes of matrix elements of the dipole moment operator in the single molecule basis. We exemplify our classification scheme by considering a new type of encoding of the effective spin-1/2 system into non-adjacent rotational states (e.g., $N=0$ and $N=2$) of polar and non-polar molecules with the same nuclear spin projection. Our classification scheme is designed to inform the optimal choice of molecular qubit encoding for quantum information storage and processing applications, as well as for dynamical generation of many-body entangled states and for quantum annealing.

quant-ph↗

arXiv2023

Advantage of discrete variable representation in variational quantum eigensolvers for vibrational energy calculations

While quantum computing algorithms have been widely applied for electronic structure calculations, applications to molecular dynamics remain scarce. Complex and varied landscapes of molecular potential energy surfaces give rise to vibrational states with a wide range of properties, making it difficult to construct a general representation of ro-vibrational states by a quantum computer with a limited number of qubits and gates. Another challenge is the exponential growth of the computation complexity - for example, the number of terms required to expand a general Hamiltonian in Pauli strings increases exponentially with the number of qubits. Here, we show that discrete variable representation (DVR) can be leveraged to represent molecular Hamiltonians by the polynomial (in the number of qubits) number of quantum circuits. We then demonstrate that DVR Hamiltonians lead to very efficient quantum ansatze for vibrational states. For this purpose, we develop a compositional quantum ansatz search that adapts gate sequences in variational quantum eigensolvers (VQE) to a specific molecular state. We apply VQE to compute the vibrational energy levels of Cr$_2$ in seven electronic states as well as of van der Waals complexes Ar-HCl and Mg-NH. Our numerical results show that accuracy of 1~cm$^{-1}$ can be achieved by very shallow quantum circuits with 2 to 9 entangling gates.

quant-ph↗

arXiv2023

Gradient domain machine learning with composite kernels: improving the accuracy of PES and force fields for large molecules

The generalization accuracy of machine learning models of potential energy surfaces (PES) and force fields (FF) for large polyatomic molecules can be generally improved either by increasing the number of training points or by improving the models. In order to build accurate models based on expensive high-level ab initio calculations, much of recent work has focused on the latter. In particular, it has been shown that gradient domain machine learning (GDML) models produce accurate results for high-dimensional molecular systems with a small number of ab initio calculations. The present work extends GDML to models with composite kernels built to maximize inference from a small number of molecular geometries. We illustrate that GDML models can be improved by increasing the complexity of underlying kernels through a greedy search algorithm using Bayesian information criterion as the model selection metric. We show that this requires including anisotropy into kernel functions and produces models with significantly smaller generalization errors. The results are presented for ethanol, uracil, malonaldehyde and aspirin. For aspirin, the model with composite kernels trained by forces at 1000 randomly sampled molecular geometries produces a global 57-dimensional PES with the mean absolute accuracy 0.177 kcal/mol (61.9 cm$^{-1}$) and FFs with the mean absolute error 0.457 kcal/mol Å$^{-1}$.

physics.chem-ph↗

arXiv2022

Quantum annealing with pairs of $^2Σ$ molecules as qubits

The rotational and fine structure of open-shell molecules in a $Σ$ electronic state gives rise to crossings between Zeeman states of different parity. These crossings become avoided in the presence of an electric field. We propose an algorithm that encodes Ising models into qubits defined by pairs of $^2Σ$ molecules sharing an excitation near these avoided crossings. This can be used to realize a transverse field Ising model tunable by an external electric or magnetic field, suitable for quantum annealing applications. We perform dynamical calculations for several examples with one- and two-dimensional connectivities. Our results demonstrate that the probability of obtaining valid annealing solutions is high and can be optimized by varying the annealing times.

quant-ph↗