trimci 0.1.3

TrimCI: high-performance accurate quantum many-body and quantum chemistry calculations

TrimCI

🔥 v0.1.3 (Apr 2026): Improved Davidson solver robustness and stability. Just pip install trimci.

Trimmed Configuration Interaction (TrimCI) is a high-performance framework for quantum many-body and quantum chemistry calculation.
It constructs accurate ground states directly from random Slater determinants — without any guiding ansatz, Hartree–Fock reference, or prior human knowledge — through an iterative expansion–trimming cycle on the determinant graph.

TrimCI demonstrates that accurate many-body ground states can emerge from randomness, achieving state-of-the-art accuracy and efficiency across molecular and lattice systems. It can outperform human-designed ansatzes or human-provided knowledge in hard problems, such as strongly correlated systems.

Paper H. Zhang, M. Otten, “From Random Determinants to the Ground State,” arXiv:2511.14734 (2025). https://arxiv.org/abs/2511.14734

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🚀 Install

pip install trimci

Alternatively, you may build the package on your environment python -m pip install ..

⚡ Quick Example

1. A fast run in AUTO mode.

cd tutorial
tci --auto --goal speed -n 1000

2. An accurate run in AUTO mode.

cd tutorial
tci --auto --goal accuracy -n 1000

3. A custom run in FULL mode. See trimci_tutorial.ipynb for more details.

4. More details are in the paper and py/trimci/TrimCI_runner/trimci_driver.py.

✨ Key Features

• Emergent accuracy from randomness: discovers the ground state without predefined ansatz or human bias.
• Expansion–trimming mechanism: iteratively expands the determinant space via Hamiltonian couplings and trims away unimportant configurations.
• C++ backend, Python interface: efficient C++ backend with OpenMP parallelization for core functions, while Python interface provides user-friendly access.
• Massive efficiency gain: achieves equivalent accuracy to selected-CI using (10^2)–(10^5\times) fewer determinants.
• Transferable module: TrimCI wavefunctions can initialize or guide AFQMC, VMC, DMRG, tensor networks, and quantum algorithms (VQE, QPE).
• Explicit wavefunction: produces a compact, analyzable coefficients and determinants dict enabling direct evaluation of observables and other measures.

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🧩 Algorithm Overview

TrimCI operates on a graph whose nodes are Slater determinants and edges correspond to Hamiltonian couplings (H_{ij}).
The algorithm alternates between two complementary stages:

1. Expansion:
   Add neighboring determinants connected by large couplings (|H_{ij}c_j|>\theta).
   This explores physically significant regions of the Hilbert space.

2. Trimming:

   • Local trimming: random groups are diagonalized independently to remove negligible states.
   • Global trimming: survivors are merged and re-diagonalized to select top-amplitude determinants.

This two-level process refines the variational subspace nearly monotonically and rapidly converges toward the ground state.

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🧠 Scientific Highlights

• Molecular systems:
  Matches SHCI accuracy on Cr₂, [4Fe–4S], and the nitrogenase P-cluster while using (10^2)–(10^5\times) fewer determinants.

• Lattice systems:
  For the 8×8 Hubbard model, TrimCI reproduces >99 % of the AFQMC ground-state energy using only (10^{-28}) of the Hilbert space.
  On 4×4 lattices, TrimCI achieves higher accuracy than AFQMC benchmarks.

• Emergent structure:
  Starting from random determinants, TrimCI self-organizes a compact “core set” of dominant configurations.
  The amplitude distribution follows a power law (p(r) \propto r^{-(1+\alpha)}) , revealing a scale-free organization and quantifiable algorithmic entropy.

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🔗 Integration with Other Frameworks

TrimCI provides a compact and explicit coefficients and determinants dict that can:

• serve as a trial or guiding wavefunction for AFQMC and VMC,
• initialize DMRG and tensor-network optimizations,
• provide high-overlap initial states for VQE or QPE quantum algorithms,
• enable cross-validation and hybrid workflows across classical and quantum domains.

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📜 License

MIT License — see LICENSE for details.