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Updated: Aug 4, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Bootstrap Embedding on a Quantum Computer.

Yuan Liu¹, Oinam R Meitei², Zachary E Chin¹

¹Department of Physics, Co-Design Center for Quantum Advantage, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

Journal of Chemical Theory and Computation

|March 31, 2023

Summary

This summary is machine-generated.

We adapted molecular bootstrap embedding for quantum computers, enabling electronic structure calculations for large molecules. This quantum approach offers a potential quadratic speedup over classical methods.

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Area of Science:

Quantum computing
Computational chemistry
Electronic structure theory

Background:

Solving the electronic structure problem for large molecules is computationally intensive.
Classical algorithms face scalability limitations for complex molecular systems.
Molecular bootstrap embedding offers a fragment-based approach to electronic structure calculations.

Purpose of the Study:

To adapt molecular bootstrap embedding for quantum computation.
To enable the solution of large molecular electronic structure problems using quantum computers.
To investigate the potential for quantum speedup in fragment-based electronic structure calculations.

Main Methods:

Extension of molecular bootstrap embedding for quantum implementation.
Formulation of the electronic structure problem as an optimization problem for a composite Lagrangian.
Utilization of quantum subroutines such as the quantum SWAP test and quantum amplitude amplification.
Matching full density matrices at fragment boundaries.

Main Results:

Demonstration of a potential quadratic speedup over classical algorithms.
Capability to match full density matrices at fragment boundaries with minimal overhead.
A generalizable strategy for utilizing small quantum computers through quantum fragment matching.

Conclusions:

Quantum bootstrap embedding provides a viable strategy for quantum computation of molecular electronic structures.
The method offers a pathway to harness current and future quantum computing capabilities for chemistry.
This approach is particularly promising for tackling large and complex molecular systems.