Assessing the Precision of Quantum Simulation of Many-Body Effects in Atomic Systems Using the Variational Quantum Eigensolver Algorithm

Abstract

The emerging field of quantum simulation of many-body systems is widely recognized as a very important application of quantum computing. A crucial step towards its realization in the context of many-electron systems requires a rigorous quantum mechanical treatment of the different interactions. In this pilot study, we investigate the physical effects beyond the mean-field approximation, known as electron correlation, in the ground state energies of atomic systems using the classical-quantum hybrid variational quantum eigensolver algorithm. To this end, we consider three isoelectronic species, namely Be, Li−, and B+. This unique choice spans three classes—a neutral atom, an anion, and a cation. We have employed the unitary coupled-cluster ansätz to perform a rigorous analysis of two very important factors that could affect the precision of the simulations of electron correlation effects within a basis, namely mapping and backend simulator. We carry out our all-electron calculations with four such basis sets. The results obtained are compared with those calculated by using the full configuration interaction, traditional coupled-cluster and the unitary coupled-cluster methods, on a classical computer, to assess the precision of our results. A salient feature of the study involves a detailed analysis to find the number of shots (the number of times a variational quantum eigensolver algorithm is repeated to build statistics) required for calculations with IBM Qiskit’s QASM simulator backend, which mimics an ideal quantum computer. When more qubits become available, our study will serve as among the first steps taken towards computing other properties of interest to various applications such as new physics beyond the Standard Model of elementary particles and atomic clocks using the variational quantum eigensolver algorithm.

At The Intersection of Medical Robotic Surgery and Drug Discovery with Quantum Computing

“…As we found in the article by PLosOne, new forms of medical teams would be alleviated surgical fatigue that might otherwise impact the treatment process [34]. As a result, the medical teams might have more time to focus on patient surgery prep and oversight, calibrations, monitoring, and ideally, spending much more time on ensuring good recovery outcomes (faster and better healing) for patients, along with post-surgery medical analytics, that can all make a significant leap in treatments and increase the number of patients treated simultaneously and consistently.…”