A production-grade Python and C# library implementing holographic quantum computing primitives derived from the AdS₃/CFT₂ correspondence, including the Virasoro algebra, bulk-to-boundary propagators, holographic decoherence suppression, Majorana qubit encoding, Shor's algorithm, and Grover's algorithm. The library is designed to accompany the paper:

Holographic Engineering for Topological Quantum Computing and Suppression-Protection of Qubit Decoherence Ahmed Ali, University of Bonn / Max Planck Institute for Physics

The AdS₃/CFT₂ correspondence maps a three-dimensional gravitational theory in anti-de Sitter space to a two-dimensional conformal field theory on its boundary. The helical edge modes of a quantum spin Hall insulator realize this boundary CFT with central charge $c = 1$ per edge channel. Environmental noise couples to boundary operators of conformal dimension $\Delta_n$, sources the bulk dilaton $\phi(z, x, t)$, and is screened by the bulk-to-boundary propagator:

$$K(z, x; x') = \left(\frac{z}{z^2 + (x - x')^2}\right)^{!\Delta}$$

yielding a coherence-time enhancement:

$$\frac{T_2^{\mathrm{holo}}}{T_2^{\mathrm{std}}} = \left(\frac{L}{\xi}\right)^{c/6}$$

git clone https://github.com/ahmed19999520-alt/holographic-qc.git
cd holographic-qc
pip install -e ".[all]"

pip install tensorflow>=2.12
pip install torch>=2.0
pip install qiskit>=0.44

from holographic_qc.core.virasoro import VirasoroAlgebra, VirasoroConfig
from holographic_qc.core.ads_cft import AdsCft3
from holographic_qc.protection.decoherence import HolographicDecoherence
from holographic_qc.materials.bi2se3 import Bi2Se3

mat = Bi2Se3()
ads = AdsCft3(central_charge=mat.central_charge, ads_radius=mat.xi)
dec = HolographicDecoherence(ads_system=ads, material=mat)

ratio = dec.coherence_time_ratio(system_size=1e-6)
print(f"T2_holo / T2_std = {ratio:.4f}")

Expected output: T2_holo / T2_std = 3.1124

python scripts/train_decoherence_tf.py \
    --epochs 200 \
    --batch_size 64 \
    --learning_rate 1e-3 \
    --data data/bi2se3_arpes.csv \
    --output models/decoherence_tf/

python scripts/train_decoherence_torch.py \
    --epochs 200 \
    --batch_size 64 \
    --learning_rate 1e-3 \
    --data data/bi2se3_arpes.csv \
    --output models/decoherence_torch/

@article{ali2026holographic,
  title   = {Holographic Engineering for Topological Quantum Computing
             and Suppression-Protection of Qubit Decoherence},
  author  = {Ali, Ahmed},
  journal = {Journal of High Energy Physics},
  year    = {2026},
  note    = {Preprint}
}