Toward perturbation theory methods on a quantum computer.

Sci Adv

Department of Chemistry, Department of Physics and Astronomy, and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA.

Published: May 2023

Perturbation theory, used in a wide range of fields, is a powerful tool for approximate solutions to complex problems, starting from the exact solution of a related, simpler problem. Advances in quantum computing, especially over the past several years, provide opportunities for alternatives to classical methods. Here, we present a general quantum circuit estimating both the energy and eigenstates corrections that is far superior to the classical version when estimating second-order energy corrections. We demonstrate our approach as applied to the two-site extended Hubbard model. In addition to numerical simulations based on qiskit, results on IBM's quantum hardware are also presented. Our work offers a general approach to studying complex systems with quantum devices, with no training or optimization process needed to obtain the perturbative terms, which can be generalized to other Hamiltonian systems both in chemistry and physics.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181180PMC
http://dx.doi.org/10.1126/sciadv.adg4576DOI Listing

Publication Analysis

Top Keywords

perturbation theory
8
quantum
5
theory methods
4
methods quantum
4
quantum computer
4
computer perturbation
4
theory wide
4
wide range
4
range fields
4
fields powerful
4

Similar Publications

Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling.

ACS Appl Mater Interfaces

January 2025

Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Greater Bilbao, Basque Country, Spain.

Magnetic nanoparticles (NPs) are gaining significant interest in the field of biomedical functional nanomaterials because of their distinctive chemical and physical characteristics, particularly in drug delivery and magnetic hyperthermia applications. In this paper, we experimentally synthesized and characterized new FeO-based NPs, functionalizing its surface with a 5-TAMRA cadaverine modified copolymer consisting of PMAO and PEG. Despite these advancements, many combinations of NP cores and coatings remain unexplored.

View Article and Find Full Text PDF

Driving brain state transitions via Adaptive Local Energy Control Model.

Neuroimage

January 2025

College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China. Electronic address:

The brain, as a complex system, achieves state transitions through interactions among its regions and also performs various functions. An in-depth exploration of brain state transitions is crucial for revealing functional changes in both health and pathological states and realizing precise brain function intervention. Network control theory offers a novel framework for investigating the dynamic characteristics of brain state transitions.

View Article and Find Full Text PDF

Despite various efforts in the field, no consistent predictors of treatment outcome in anxiety disorders have been identified. Based on the Dynamic System Theory, this study proposes a novel, dynamic predictor of treatment outcome in those with public speaking anxiety. It was assessed whether speed of return to one's interpretation bias equilibrium after an experimentally-induced perturbation (i.

View Article and Find Full Text PDF

Contrastive Graph Representation Learning with Adversarial Cross-View Reconstruction and Information Bottleneck.

Neural Netw

January 2025

School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, 710049, China. Electronic address:

Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer from the popularity bias issue in a graph caused by a small number of popular categories. Additionally, real graph datasets always contain incorrect node labels, which hinders GNNs from learning effective node representations.

View Article and Find Full Text PDF

Determining the values of various properties for new bio-inks for 3D printing is a very important task in the design of new materials. For this purpose, a large number of experimental works have been consulted, and a database with more than 1200 bioprinting tests has been created. These tests cover different combinations of conditions in terms of print pressure, temperature, and needle values, for example.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!