Variational Quantum Algorithms for Quantum ChemistryΒΆ

PennyLane quantum chemistry and simulation

Variational Quantum Algorithms for Quantum Chemistry

A modular PennyLane research toolkit for small-molecule VQE studies, excited-state methods, phase estimation, imaginary-time evolution, real-time dynamics, and reproducible benchmark workflows.

Research workflows

One shared pipeline for quantum simulation methods

The repository keeps variational algorithms, QPE, and QITE workflows on a common problem resolution layer so small-system chemistry studies can be compared with consistent Hamiltonians, run signatures, outputs, and exact references.

Ground-State VQE

Run molecular VQE studies with calibrated defaults, multiple ansatzes, optimizer choices, geometry scans, and low-qubit benchmark helpers.

VQE ADAPT-VQE UCCSD PennyLane

Excited-State Methods

Explore post-VQE and direct variational excited-state workflows for compact molecule studies and reference-state diagnostics.

QSE EOM-QSE LR-VQE SSVQE VQD

Quantum Phase Estimation

Compare spectral and phase-estimation workflows with controlled time-evolution settings, ancilla studies, shots, and noise options.

QPE Phase Estimation Time Evolution Noise

QITE and QRTE

Use projected variational imaginary-time relaxation and real-time dynamics with shared Hamiltonian inputs and comparable output records.

VarQITE VarQRTE Dynamics State Preparation

Shared Chemistry Layer

Resolve registry molecules, generated geometry tags, explicit geometries, and expert-mode qubit Hamiltonians through common helpers.

Hamiltonians Molecules Geometry Caching

Benchmark Evidence

Maintain notebooks for cross-method comparisons, default calibration, noise studies, non-molecule Hamiltonians, and reproducibility checks.

Benchmarks Notebooks Reproducibility Exact References

Published package

Installable Python tooling

The PyPI package exposes four importable stacks: vqe, qpe, qite, and common.

vqe-pennylane

Variational quantum chemistry tooling with VQE, QPE, and QITE modules.

pip install vqe-pennylane PyPI

CLI entrypoints

Run solver workflows directly from the terminal.

python -m vqe -m H2 Usage

Python APIs

Import high-level runners for notebooks, scripts, and tests.

from vqe import run_vqe Quickstart

Notebook library

Examples and benchmark studies

The notebook tree separates getting-started examples from benchmark notebooks. Start with the H2 comparison, QITE, and QRTE examples before moving into calibration or cross-method studies.

Documentation and source

Read the full project materials

This page is generated by the repository's Sphinx Pages workflow. The deeper project documentation remains available as generated HTML, Markdown source, and notebooks.