fHEOM 1.0.0

Low-rank factorization for efficient HEOM simulations of quantum coherence in biological systems

fHEOM: Factorized Hierarchical Equations of Motion

Efficient simulation of quantum coherence in biological systems using low-rank factorization of spatially correlated environmental baths.

🎯 Core Innovation

fHEOM (Factorized HEOM) reduces the computational complexity of simulating quantum dynamics in systems coupled to spatially correlated baths:

• Problem: Simulating N sites with HEOM requires (Nk+1)^N auxiliary density operators (ADOs)

  • FMO complex: N=7, Nk=3 → 4^7 = 16,384 ADOs
  • Computational cost scales exponentially with number of sites
  • Full hierarchy becomes intractable for large systems

• Solution: Low-rank factorization of correlation matrix reduces effective modes from N → r

  • Decompose: C ≈ L L^T where L is N×r
  • Create r effective bath operators: Q_eff[k] = Σ_i L[i,k] * Q_site[i]
  • Run HEOM with r modes instead of N: (Nk+1)^r ADOs

• Result: For FMO with r=3:

  • 4^3 = 64 ADOs (vs 16,384 for full hierarchy)
  • Factorization captures 60.5% of correlation structure variance
  • Reconstruction error: 0.4693
  • Reduction in hierarchy size: 256×
  • All validation tests passing (see Validation section)

📦 What's Included

fHEOM/
├── src/fheom/
│   ├── __init__.py              # Package exports
│   ├── fheom.py                 # Core algorithm (426 lines)
│   ├── heom_utils.py            # HEOM simulation utilities
│   └── fmo.py                   # FMO model for validation
├── examples/
│   └── fheom_validation.py      # Minimal working demonstration
├── requirements.txt             # Dependencies
├── setup.py / pyproject.toml   # Package configuration
├── LICENSE                      # MIT License
└── README.md                    # This file

🚀 Quick Start

Installation

# Clone and install
git clone https://github.com/rihp/fHEOM.git
cd fheom
pip install -e .

Minimal Example

import numpy as np
import qutip as qt
from fheom import get_factorized_bath, run_heom_simulation
from fheom.fmo import build_fmo_hamiltonian, bath_operator, get_site_coordinates

# Setup FMO complex
H = build_fmo_hamiltonian()
coords = get_site_coordinates()
q_sites = [bath_operator() for _ in range(7)]

# Get factorized bath (rank-3 instead of 7 modes)
result = get_factorized_bath(q_sites, coords, rank=3)
print(f"Reduced to {result.rank} effective modes")
print(f"Variance explained: {result.explained_variance:.1%}")
print(f"Reconstruction error: {result.reconstruction_error:.4f}")

# Run HEOM simulation
rho0 = qt.ket2dm(qt.basis(7, 0))
tlist = np.linspace(0, 500e-15, 501)  # 500 fs
e_ops = [qt.basis(7, 0) * qt.basis(7, 0).dag()]

heom_result = run_heom_simulation(
    H, result.q_eff,
    lam_cm=35.0,      # Reorganization energy
    gamma_cm=106.0,   # Bath cutoff
    T_K=77.0,         # Temperature
    Nk=2,             # Hierarchy depth
    tlist=tlist,
    rho0=rho0,
    e_ops=e_ops
)

# Access results
populations = heom_result.expect[0]

Run Validation Suite

python examples/fheom_validation.py

This script tests:

1. ✓ Spatial correlation matrix construction
2. ✓ Low-rank factorization accuracy
3. ✓ Effective bath operator properties
4. ✓ HEOM dynamics simulation
5. ✓ Concept visualization

📐 Algorithm Overview

Step 1: Spatial Correlation Matrix

From N site coordinates, construct N×N correlation matrix:

C[i,j] = exp(-|r_i - r_j| / λ_corr)

Step 2: Low-Rank Factorization

Eigendecomposition: C = V Λ V^T Truncate to rank r: keep only top r eigenvalues Result: L = V[:, :r] @ diag(√Λ[:r])

Step 3: Effective Bath Operators

Q_eff[k] = Σ_i L[i,k] * Q_site[i]  for k = 1..r

Step 4: HEOM with Reduced Hierarchy

Run standard HEOM with r effective bath operators instead of N site operators.

🔬 Mathematical Framework

Correlation matrix:

C ≈ L L^T  where L is N×r, r ≪ N

Hierarchy reduction:

Full:        (Nk+1)^N auxiliary density operators
Factorized:  (Nk+1)^r auxiliary density operators

For FMO (N=7, r=3, Nk=3):
Full:        4^7 = 16,384 ADOs
Factorized:  4^3 = 64 ADOs
Reduction:   256×

Accuracy control:

Variance threshold: Configurable parameter to control rank selection (default: 0.99)
  Note: This is the target for auto-rank selection, not necessarily achieved
Reconstruction error: ||C - LL^T||_F / ||C||_F (minimized by rank selection)
  For FMO rank-3: 0.4693 (60.5% variance explained)

📚 Core Functions

get_factorized_bath(q_site_ops, coordinates, rank=None, ...)

One-shot function to construct factorized bath.

Args:

• q_site_ops: List of N site bath operators
• coordinates: N×3 array of site positions (Angstroms)
• rank: Number of effective modes (None = auto)
• correlation_length: Spatial decay length (Angstroms)
• variance_threshold: Fraction of variance to retain (0.99)
• kernel: 'exponential', 'gaussian', or 'power_law'

Returns: FactorizationResult with:

• q_eff: List of r effective bath operators
• rank: Actual rank used
• explained_variance: Fraction of variance retained
• reconstruction_error: L2 error in correlation matrix

spatial_correlation_matrix(coordinates, correlation_length, kernel)

Construct correlation matrix from site positions.

factorize_correlation_matrix(corr_matrix, rank=None, ...)

Low-rank factorization using eigendecomposition or Cholesky.

run_heom_simulation(H, Q, lam_cm, gamma_cm, T_K, Nk, tlist, rho0, e_ops)

Standard HEOM solver (QuTiP 5.2+ wrapper).

✅ Validation

Tested on FMO complex (Fenna-Matthews-Olson) photosynthetic light-harvesting:

Factorization Accuracy (FMO 7-site system)

Performance metrics vary by hardware. Run the validation suite for system-specific benchmarks:

Rank	Modes	Explained Variance	Reconstruction Error
2	2	46.8%	0.5696
3	3	60.5%	0.4693
4	4	72.4%	0.3772
7	7	100%	0.0000

Note: Runtime, speedup, and memory metrics are hardware-dependent. Execute python examples/fheom_validation.py to measure performance on your system.

Concept Visualization

Figure: fHEOM factorization of the FMO complex correlation structure. (A) Spatial correlation matrix C showing site-site couplings. (B) Eigenvalue spectrum with rank-3 cutoff highlighted. (C) Factorization matrix L (7×3) showing effective mode composition. (D) Rank-3 reconstruction with relative reconstruction error of 0.4693 (Frobenius norm) while capturing 60.5% of total variance.

Validation Test Results

All validation tests passing:

• ✓ TEST 1: Spatial correlation matrix (7×7, symmetric, PSD)
• ✓ TEST 2: Low-rank factorization (ranks 2-7 analyzed)
• ✓ TEST 3: Effective bath operators (3 modes, Hermitian, properly weighted)
• ✓ TEST 4: HEOM dynamics simulation (Nk=2, 500 fs)
• ✓ TEST 5: Concept visualization (generated successfully)

📖 References

HEOM Methodology:

• Tanimura, Y. (2006). "Stochastic Liouville, Langevin, Fokker–Planck, and master equation approaches to quantum dissipative systems." J. Phys. Soc. Jpn., 75(8), 082001.
• Ishizaki, A., & Fleming, G. R. (2009). "Unified treatment of quantum coherent and incoherent hopping dynamics." J. Chem. Phys., 130(23), 234111.

FMO Complex:

• Engel, G. S., et al. (2007). "Evidence for wavelike energy transfer through quantum coherence." Nature, 446(7137), 782-786.
• Adolphs, J., & Renger, T. (2006). "How proteins trigger excitation energy transfer in the FMO complex." Biophys. J., 91(8), 2778-2797.

💻 System Requirements

Minimum:

• Python 3.8+
• 4 GB RAM
• CPU-based (no GPU required)

Dependencies:

• QuTiP 5.2+ (HEOM solver)
• NumPy, SciPy (numerical operations)
• Matplotlib (visualization)

📄 License

MIT License - See LICENSE

This framework is open-source to enable:

• Community validation and replication
• Extension to other quantum biology systems
• Performance optimization and improvements
• Transparent, reproducible science

👤 Author

Roberto Ignacio Henriquez-Perozo
GitHub: @rihp

Implementation of fHEOM method and application to quantum biology systems

🤝 Contributing

We welcome contributions:

• Bug reports: Issues with reproducibility or accuracy
• Performance optimization: Algorithmic improvements
• New systems: Other well-characterized quantum biology models
• Validation: Experimental comparisons

Acknowledgments

• QuTiP Development Team: Quantum Toolbox in Python
• Experimental Groups: Engel, Fleming, and others for FMO data
• Theory Community: Tanimura, Ishizaki, Lambert for HEOM foundations

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fHEOM v1.0 — Low-rank factorization method for correlated baths in quantum simulations

🔬 Reproducible · Falsifiable · Open Science