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arxiv: 2605.04483 · v1 · submitted 2026-05-06 · ⚛️ physics.comp-ph · cs.NA· math.NA· physics.chem-ph

Recognition: unknown

CDFCI: High-Performance Parallel Software for Many-Body Large-Scale Eigenvalue Problems

Jianfeng Lu, Yingzhou Li, Yuejia Zhang, Zhe Wang

Pith reviewed 2026-05-08 15:40 UTC · model grok-4.3

classification ⚛️ physics.comp-ph cs.NAmath.NAphysics.chem-ph
keywords selected configuration interactionmany-body eigenvalue problemsfermionic Hamiltoniansparallel computingquantum chemistrycondensed matter physicscoordinate descent
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0 comments X

The pith

CDFCI computes low-lying eigenpairs of large fermionic Hamiltonians with state-of-the-art accuracy and competitive parallel speed.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CDFCI as shared-memory parallel software for finding low-lying eigenpairs of non-relativistic fermionic Hamiltonians that appear in quantum chemistry and condensed matter physics. It pairs a coordinate-descent selected configuration interaction algorithm with custom parallel strategies to run on multi-core hardware. Benchmarks on standard test cases show accuracy that matches or exceeds CIPSI, SHCI, and DMRG while delivering competitive run times. A reader would care because these eigenvalue problems grow exponentially with system size and often limit what can be simulated without massive resources. If the claim holds, larger models become routinely accessible on ordinary workstations through an open-source tool with a Python interface.

Core claim

CDFCI implements a coordinate-descent-based selected configuration interaction method together with dedicated parallelization, attaining state-of-the-art accuracy and competitive performance on representative quantum chemistry and condensed matter test cases relative to established SCI and DMRG codes.

What carries the argument

Coordinate-descent selected configuration interaction algorithm combined with shared-memory parallelization strategies that distribute the configuration search and matrix operations across cores.

If this is right

  • Users can solve larger active-space problems in ab initio electronic structure without switching to specialized hardware.
  • The same code handles both molecular and lattice Hamiltonians within one framework.
  • Integration with PySCF allows the method to slot directly into existing many-body workflows.
  • Open-source release and documentation lower the barrier for community testing on new model systems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The coordinate-descent approach may extend to time-dependent or driven problems if the iteration is adapted to include external fields.
  • Performance on current multi-core chips suggests the method could scale further with GPU offloading or distributed-memory versions.
  • Because the algorithm avoids full diagonalization, it might pair with embedding techniques to treat even bigger systems at reduced cost.

Load-bearing premise

The parallel coordinate-descent procedure maintains full accuracy while avoiding hidden overheads that would erase its speed advantage on the broad class of non-relativistic fermionic Hamiltonians.

What would settle it

A benchmark on a representative Hamiltonian where CDFCI either deviates from the reference energies obtained by CIPSI, SHCI, or DMRG by more than the stated tolerance or runs slower than those codes on identical multi-core hardware.

Figures

Figures reproduced from arXiv: 2605.04483 by Jianfeng Lu, Yingzhou Li, Yuejia Zhang, Zhe Wang.

Figure 1
Figure 1. Figure 1: Relationship between the correlation energy and the number of Slater determinants in view at source ↗
Figure 2
Figure 2. Figure 2: Benzene benchmark test: The left plot shows the effect of threshold view at source ↗
Figure 3
Figure 3. Figure 3: Ground-state energy convergence curves of the two-dimensional Hubbard model with view at source ↗
Figure 4
Figure 4. Figure 4: Strong scaling of CDFCI on the N2/cc-pVQZ system: the top panel shows runtime (log scale) and speedup (including ideal linear scaling), and the bottom panel shows efficiency, defined as speedup divided by the number of processors. limited in larger-scale parallel computations. 6 Conclusion and Future Work We have presented CDFCI, an efficient and modular software package for solving the non-relativistic, t… view at source ↗
read the original abstract

CDFCI is a shared-memory parallel numerical program for computing low-lying eigenpairs of large-scale, non-relativistic fermionic Hamiltonians. The software is designed to handle a broad class of many-body quantum models, including both ab initio electronic structure Hamiltonians and lattice-based Hamiltonians arising in condensed matter physics. CDFCI combines an efficient coordinate-descent-based selected configuration interaction algorithm with dedicated parallelization strategies, achieving high performance on modern multi-core architectures. Benchmark results on representative quantum chemistry and condensed matter test cases demonstrate that CDFCI attains state-of-the-art accuracy with competitive performance compared to established selected configuration interaction (such as CIPSI or SHCI) and DMRG implementations. The software is open-source, extensively documented, and provides a Python interface for seamless integration with PySCF and other many-body simulation workflows.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript presents CDFCI, a shared-memory parallel software package implementing a coordinate-descent-based selected configuration interaction (SCI) algorithm for computing low-lying eigenpairs of large-scale non-relativistic fermionic Hamiltonians. It targets both ab initio quantum chemistry and lattice models from condensed matter physics, with dedicated parallelization strategies for modern multi-core hardware. Benchmark results on representative test cases are reported to demonstrate state-of-the-art accuracy and competitive performance relative to established methods such as CIPSI, SHCI, and DMRG. The software is open-source, documented, and provides a Python interface for integration with PySCF and similar workflows.

Significance. If the benchmark results and implementation details hold, CDFCI would be a significant contribution to computational many-body physics by offering an efficient, scalable tool for challenging eigenvalue problems that arise in electronic structure and lattice Hamiltonians. The combination of the coordinate-descent SCI approach with explicit parallel strategies addresses practical performance needs on contemporary hardware, and the open-source release with PySCF integration promotes reproducibility and broader adoption. The absence of hidden overheads or accuracy trade-offs in the reported tests supports the design goals without apparent internal inconsistencies.

minor comments (2)
  1. The abstract would benefit from inclusion of at least one quantitative benchmark metric (e.g., a specific energy error or wall-time comparison) to better substantiate the 'state-of-the-art accuracy' and 'competitive performance' claims for readers who encounter only the abstract.
  2. In the implementation or parallelization section, a brief pseudocode snippet or diagram illustrating the load-balancing and communication pattern would improve clarity for users adapting the code to new Hamiltonians.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive and constructive review of our manuscript. We are pleased that the referee recognizes CDFCI as a significant contribution to computational many-body physics and recommends acceptance. The report contains no major comments requiring specific responses or revisions.

Circularity Check

0 steps flagged

No circularity detected in derivation or claims

full rationale

The paper describes a software implementation of a coordinate-descent selected CI algorithm with parallelization for fermionic eigenvalue problems. Its central claims consist of benchmarked accuracy and performance on external test cases compared to CIPSI, SHCI, and DMRG. These are empirical validations against independent implementations rather than internal predictions, fitted parameters renamed as outputs, or self-referential definitions. No load-bearing step reduces by construction to the paper's own inputs or prior self-citations; the algorithm description and parallel strategies are presented as design choices whose efficacy is measured externally. The derivation chain is therefore self-contained and non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard domain assumptions from selected configuration interaction literature and the premise that the chosen parallelization will scale without accuracy loss; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Coordinate-descent selected configuration interaction remains accurate and efficient for the broad class of non-relativistic fermionic Hamiltonians when combined with the described parallelization.
    This premise underpins the design and the benchmark claims.

pith-pipeline@v0.9.0 · 5451 in / 1235 out tokens · 55347 ms · 2026-05-08T15:40:23.834145+00:00 · methodology

discussion (0)

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Reference graph

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