Quantum Computing for Quantum Chemistry - Review UCC Methods and Adapt-VQE

The use of quantum computers is a promising strategy to overcome challenges occurring
in the area of quantum chemistry.
About this Event

Summary:

The use of quantum computers is a promising strategy to overcome challenges occurring in the area of quantum chemistry. Hardware quantum computers are supposed to potentially facilitate the encoding of the full configuration interaction (FCI) wavefunction of the many-electron molecular system thanks to entangled quantum bits or qubits. In classical computers, implementing the FCI scales exponentially with the number of electrons and the number of spin-orbitals, which makes the manipulation and storage of the wavefunction inefficient. However, using a quantum computer, we can instead store the (FCI) wavefunction by using only orbitals which correspond to number of qubits. This potential quantum advantage has recently excited both hardware and software communities generating rapid progresses in the field. Variational Quantum Eigen solver (VQE) is a potential algorithm-well suited for its practical implementation on present Noisy Intermediate Scaled Quantum (NISQ) devices.

The proposal presentation focuses on the development of Variational Quantum Eigensolvers (VQE) through an open soure OpenVQE, covering the latest advancements, circuit measurement techniques, and the computational demands of quantum computers. The process of VQE is hybrid, consists of quantum circuits state and classical parameter optimization, and it aims to find the ground state of a given molecular Hamiltonian. Particularly, we will concentrate on advanced quantum chemistry methods such as Unitary Coupled Cluster that involves Single and Double excitations (UCCSD) has recently attracted interest due to its use in VQE molecular simulations on NISQ device. Furthermore, we want to introduce, an algorithm termed Adaptive Derivative-Assembled Pseudo-Trotter (ADAPT)-VQE to build system-adapted ansatze with substantially fewer variational parameters. This section of the talk will introduce and compare two similar dynamically created ansatze(fermionic-ADAPT-VQE and qubit-ADAPT-VQE), and compare computational efficiency of ADAPT-VQE against UCCSD-VQE.

Speakers:

Dr.Mohammad Haidar is currently a Senior Researcher in Quantum Technologies at Qubit Pharmaceuticals, where he also serves as a supervisor and mentor for Master’s students. From 2021 to June 2023, he held a postdoctoral research position in quantum computing for quantum chemistry, certified by Sorbonne University, successfully working with TotalEnergies in collaboration with the Atos quantum computing team.In 2021, Mohammad received his Ph.D. in theoretical quantum physics from the Laboratoire Kastler Brossel, affiliated with Sorbonne University, ENS Physics Department, and Collège de France, Paris. He earned a scholarship from Sorbonne University (EDPIF) to support his doctoral studies. Prior to this, during 2016-2017, he completed his M.S. in high-energy particle physics from Lebanese University and the LAPTh research laboratory in Annecy-le-Vieux, France.

Huy Binh TRAN is currently in the Master 2 Quantum Devices offered by the Université de Paris in partnership with École Polytechnique which deeply focusing in a high-level theoretical and experimental training on different kind of quantum phenomena with a particular attention to quantum devices and nanotechnologies. He has been working as an intern at company Qubit Pharmaceuticals for 6 months under the guidance of Dr. Mohammad Haidar. His main interest is in Quantum Computing for Quantum Chemistry, he is also the lead contributor in the Open-VQE package under the scope of quantum algorithms, scientific research & create documentation.

Moderator:

Nathan is an experienced fullstack developer at Margo, a Paris-based consulting firm, where he currently contributes to a drug design platform. He holds a background in applied mathematics from Université Libre de Bruxelles and École Polytechnique in Paris. For his master’s thesis in 2021, he developed machine learning models for detecting cardiac arrhythmia. In 2022, he served as a data science advisor to the French Ministry of Health, focusing on COVID-19 vaccination strategy. Nathan also administers the open-source project OpenVQE, developing applications and algorithms for quantum computing.