Open-source code from the Mazziotti Group is available on Github. Furthermore, several 2-RDM methods developed by the Mazziotti Group including the variational 2-RDM (v2-RDM), the anti-Hermitian contracted Schrödinger equation (ACSE), the parametric 2-RDM (p2-RDM) methods have been implemented in the Maple Quantum Chemistry Package from RDMChem.

Variational 2-RDM

The variational 2-RDM method computes the ground-state energy as a functional of the 2-RDM constrained by N-representability conditions. This implementation, written mainly by A. W. Schlimgen, is based on code by D. A. Mazziotti. Optimization is performed by the boundary-point algorithm for semidefinite programming, developed for 2-RDMs by D. A. Mazziotti.

Parametric 2-RDM

The parametric 2-RDM method computes the ground-state energy of an atom or molecule as a functional of a parameterized two-electron reduced density matrix (2-RDM). The default parameterization was derived by D. A. Mazziotti

Hybrid Quantum Computing Algorithms for Quantum Chemistry

This python module is a compilation of tools for performing quantum chemistry simulations on near term quantum computers, with a focus on approaches based in reduced density matrix (RDM) theories, namely the contracted quantum eigensolvers. The methods are aimed at circuit based implementations, and can be readily translated to work on a variety of real device architectures.

Polariton Contracted Quantum Eigensolvers

This repository contains code for a series of Contracted Quantum Eigensolvers of Polaritonic Chemistry problems on quantum computers. Additionally, one can find reference implementations of Quantum ElectroDynamics (QED) Hartree-Fock and FCI.

Reinforcement Learning Contracted Quantum Eigensolver

Code for the Reinforcement Learning Contracted Quantum Eigensolver (RL-CQE) is presented in the accompanying Jupyter notebook. The code implements a reinforcement learning approach to solving the contracted Schrodinger equation (CSE) for molecular many-body states on quantum devices. Research is described in the manuscript “Quantum Many-body Simulations from a Reinforcement-Learned Exponential Ansatz” by Yuchen Wang and David A. Mazziotti.