Chemistry

Quantum Computing for Chemistry

MATLAB offers a support package for advancing quantum computing in chemistry, enabling researchers to model, simulate, and analyze quantum algorithms for molecular and materials applications. MATLAB also provides built-in tools, and it integrates with popular quantum hardware platforms, supporting the development and deployment of quantum workflows for computational chemistry, drug discovery, and molecular design.

With MATLAB, you can: 

  • Simulate quantum circuits and quantum states locally to address molecular and chemical problems 
  • Run quantum algorithms on real quantum hardware via AWS® and IBM® integrations 
  • Apply advanced quantum techniques, such as Variational Quantum Eigensolver (VQE), for ground-state calculations and protein folding 
  • Explore quantum Monte Carlo simulations and quantum optimization methods for chemical systems 
  • Develop and test quantum neural networks for chemistry-related machine learning tasks 
  • Automate, visualize, and share reproducible quantum chemistry workflows for research and education 
Word cloud featuring terms related to algorithms, simulation techniques, and quantum computing in molecular modeling tools.

MATLAB Support Package for Quantum Computing

Explore quantum algorithms, simulate circuits, and connect to real quantum hardware using the MATLAB support package for quantum computing.

Software Support Packages

Quantum Computing

Connecting to quantum computers with MATLAB through cloud services and running quantum algorithms.
Key steps in mapping molecular energy to a quantum circuit to obtain the optimized folded state using VQE.

Protein Folding Using Variational Quantum Eigensolver (VQE)

Learn how to harness quantum computing to model protein folding with Variational Quantum Eigensolver (VQE) for MATLAB, simulating the ground-state energy of peptide structures and running optimized circuits on real quantum processors.

Quantum Monte Carlo Simulation

Perform quantum Monte Carlo simulations in MATLAB. Explore powerful tools for modeling quantum systems and chemical applications.

Estimated phase computing the mean value of the function, and comparing the quantum value against the analytic, classic Monte Carlo, and discrete values.
Two-dimensional data points are classified based on the region of their x- and y-coordinates using a mapping function.

Quantum Neural Networks

Learn how to train a hybrid quantum–classical neural network in MATLAB to solve the nonlinear XOR classification problem, combining parameterized quantum circuits with classical layers for an engaging hands on exploration.