Quantum Many-Body Problem Kit (qmp-kit)

The quantum many-body problem kit (qmp-kit) is a powerful tool designed to solve quantum-many-body problems especially for strongly correlated systems. This project includes our work on Hamiltonian-Guided Autoregressive Selected-Configuration Interaction Achieves Chemical Accuracy in Strongly Correlated Systems.

About The Project

This repository hosts a Python package named qmp-kit, dedicated to solving the quantum-many-body problem. It implements a suite of algorithms and interfaces with various model descriptors, such as the OpenFermion format and FCIDUMP. Additionally, qmp can efficiently utilize accelerators such as GPU(s) to enhance its performance.

Getting Started

Installation

The qmp-kit requires Python >= 3.12 and CUDA-compatible environment for GPU acceleration.

You can install the package via pip:

pip install qmp-kit

Or run it directly using uvx (recommended):

uvx qmp-kit

Usage

The application is now configured via a config.yaml file located in the working directory. It uses Hydra for configuration management.

To run the application:

# Using the installed script
qmp

# Or using uvx
uvx qmp-kit

Configuration Structure

A typical config.yaml includes several sections: common, model, network, optimizer, and action.

# Example config.yaml
model:
  name: fcidump
  params:
    model_path: /path/to/molecule.fcidump
    ref_energy: -7.44606892

network:
  name: mlp/u1u1
  params:
    hidden: [512, 512]

optimizer:
  name: Adam
  params:
    lr: 0.001

common:
  random_seed: 2333
  device: cuda
  dtype: bfloat16

action:
  name: haar
  params:
    sampling_count_from_neural_network: 1024
    krylov_iteration: 32

Configuration Options

Common Settings (common)

Model Settings (model)

• fcidump: Interface for FCIDUMP files.
  • model_path: Path to the FCIDUMP file.
  • ref_energy: Reference energy (optional).
• hubbard: 2D Hubbard model.
  • m, n: Lattice dimensions.
  • t, u: Model coefficients.
  • electron_number: Number of electrons.
• ising: 2D Ising-like model on a lattice.
  • m, n: Lattice dimensions.
  • x, y, z: Coefficients for the transverse and longitudinal fields.
  • xh, yh, zh: Coefficients for horizontal bond interactions (XX, YY, ZZ).
  • xv, yv, zv: Coefficients for vertical bond interactions.
  • xd, yd, zd: Coefficients for diagonal bond interactions.
  • xa, ya, za: Coefficients for anti-diagonal bond interactions.

Network Settings (network)

The available network architectures and their naming conventions depend on the chosen model, as they implement specific symmetry and conservation laws. Explicit naming is recommended to clarify the constraints being used (e.g., total electron number u1 or spin-resolved u1u1).

For models like fcidump and hubbard, recommended options include:

• mlp/u1u1: Multi-Layer Perceptron with $U(1) \times U(1)$ symmetry (conserves spin-up and spin-down electrons separately).
• mlp/u1: MLP with $U(1)$ symmetry (conserves total electron number).
• transformers/u1u1: Transformer architecture with $U(1) \times U(1)$ symmetry.
• transformers/u1: Transformer architecture with $U(1)$ symmetry.

Parameters for these networks:

• mlp variants:
  • hidden: Tuple of hidden layer widths (e.g., [512, 512]).
• transformers variants:
  • embedding_dim, heads_num, depth, etc.

Action Settings (action)

The action section determines the algorithm to run. The primary algorithms are haar and vmc. Each action has its own set of parameters defined in its corresponding configuration class.

Example for haar action:

action:
  name: haar
  params:
    sampling_count_from_neural_network: 1024
    krylov_iteration: 32
    # ... other haar specific parameters

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for detailed guidelines.

License

This project is distributed under the GPLv3 License. See LICENSE.md for more information.