Quantum computers which may be developed soon (Near-term), are promising and may be of great use in solving some problems more effectively than classical computers. Their application is in physics, chemistry, and other sciences in determining the ground states of the quantum systems.
Even though quantum computers effectively run quantum simulations, the effect of noise and limited hardware will limit these techniques. So, there is a need to reduce the impact of noise through quantum error mitigation techniques, which the researchers have addressed.
The researchers have developed a new technique called neural error mitigation which uses neural networks to improve estimates of ground states.
The researchers’ approach consists of two essential parts or phases. First, they trained a so-called NQS ansatz to represent a rough ground state created by a noisy quantum device using a neural quantum state tomography (NQST). NQST is a machine-learning technique that uses a small sample of empirically gathered measurements to rebuild complex quantum states. After then, they enhanced the current representation of the unidentified ground state using a variational Monte Carlo (VMC) approach. The generative machine-learning model transformer architecture, which has frequently been used to produce natural language writings and interpret images, was the NQS algorithm that the researchers’ experiments employed.
The researchers tested the performance of this method on a real problem. They examined the method’s capacity to recognize the ground-state wavefunction and energy of many-body interacting fermionic molecular Hamiltonians.
They used neural error mitigation to locate the ground states of the H2 and LiH molecular Hamiltonians and the lattice Schwinger model, created via the variational quantum eigensolver, to show the method’s wide range of applications. Their findings demonstrate that neural error mitigation enhances numerical and experimental variational quantum eigensolver computations to produce low energy errors, high fidelities, and precise estimations of more complex observables like order parameters and entanglement entropy without requiring extra quantum resources.
Future quantum simulations employing near-term devices may benefit from using neural error simulation to lessen noise-related mistakes. This may have significant ramifications for numerous scientific areas, including chemistry, physics, and materials science, as it may result in more accurate predictions or insightful findings.
This Article is written as a research summary article by Marktechpost Staff based on the research paper 'Neural Error Mitigation of Near-Term Quantum Simulations'. All Credit For This Research Goes To Researchers on This Project. Check out the paper and reference article. Please Don't Forget To Join Our ML Subreddit
Prathvik is ML/AI Research content intern at MarktechPost, he is a 3rd year undergraduate at IIT Kharagpur. He has a keen interest in Machine learning and data science.He is enthusiastic in learning about the applications of in different fields of study .