Change Log

Change Log#

Jan-23-2026: v0.1.0a2#

  • Release of the third alpha version of jQMC.

Key Features#

  • Analytical derivatives:

    • Implemented analytical gradients and Laplacians for atomic and molecular orbitals in both spherical and Cartesian GTO bases.

    • JAX autograd is now used primarily for validating the analytical gradients.

    • Logarithmic derivatives of the wavefunction and derivatives of atomic force calculations still use JAX autograd.

  • Testing precision:

    • Tightened and systematized decimal controls in tests, improving overall reliability.

  • Fast updates:

    • Expanded fast-update implementations to more functions, yielding significant speedups in both MCMC and GFMC modules.

Jan-14-2026: v0.1.0a1#

  • Release of the second alpha version of jQMC.

Key Features#

  • Neural Network Jastrow:

    • Introduced NNJastrow, a PauliNet-inspired neural network architecture for many-body Jastrow factors, enabling more accurate wavefunction ansatz.

  • Optimization Control:

    • Implemented proper gradient masking mechanisms (e.g., with_param_grad_mask). This allows for selectively freezing or optimizing specific parameter blocks (One-body, Two-body, Three-body, NN, and Geminal coefficients) during the VMC optimizations.

Enhancements & Fixes#

  • I/O: Changed the storage format for hamiltonian_data from pickled binary files to HDF5 (.h5) for better portability and compatibility.

  • Documentation: Updated README.md, docstrings, and API references to reflect recent changes and fix Sphinx warnings.

  • CI/CD: Updated pre-commit configurations and GitHub workflow triggers.

  • Code Quality: Refactored code based on suggestions and improved type hinting.

Aug-20-2025: v0.1.0a0#

  • Release of the first alpha version of jQMC.

We are pleased to announce the first alpha release of jQMC, a Python-based Quantum Monte Carlo package built on JAX.

Key Features#

  • JAX-based Core: Fully utilizes JAX’s Just-In-Time (JIT) compilation and automatic vectorization (vmap) for high-performance simulations on GPUs and TPUs.

  • Algorithms:

    • Variational Monte Carlo (VMC): Supports wavefunction optimization via Stochastic Reconfiguration (SR) and Natural Gradient methods.

    • Lattice Regularized Diffusion Monte Carlo (LRDMC): A stable and efficient projection method for ground state calculations.

  • Wavefunctions:

    • Ansatz: Supports Jastrow-Slater Determinant (JSD) and Jastrow-Antisymmetrized Geminal Power (JAGP).

    • Jastrow Factors: Includes One-body, Two-body, Three/Four-body terms.

    • Determinant Types: Single Determinant (SD), Antisymmetrized Geminal Power (AGP), and Number-constrained AGP (AGPn).

  • I/O & Interoperability:

    • TREX-IO Support: Interfaces with the TREX-IO library (HDF5 backend) for standardized input of molecular structure and basis sets (Cartesian & Spherical GTOs).

  • Parallelization:

    • MPI Support: Implements mpi4py for efficient parallelization across multiple nodes.

  • Documentation:

    • Comprehensive technical notes on Wavefunctions, VMC, LRDMC, and JAX implementation details.

    • Examples demonstrating usage for various systems (H2, N2, Water, etc.).

Known Limitations (Alpha)#

  • Periodic Boundary Conditions (PBC) are currently in development.

  • Atomic force calculations with spherical harmonics are computationally intensive on current JAX versions.

  • Complex wavefunctions are not yet supported.

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