Quantum Transformer: Pure Quantum Architecture for Molecular Intelligence

The First Pure Quantum Transformer Architecture for Molecular Property Prediction

Paper • Installation • Quick Start • Documentation

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Vision

Quantum Transformer introduces a revolutionary architecture that implements the entire transformer mechanism using quantum circuits. Unlike hybrid approaches, this architecture is fully quantum, leveraging:

• Quantum Self-Attention: Attention mechanism implemented via parameterized quantum circuits
• Quantum Positional Encoding: Encoding sequence position in quantum amplitudes
• Quantum Feed-Forward Networks: Multi-layer quantum circuits replacing classical FFN
• Quantum Layer Normalization: Amplitude normalization via quantum operations

Quantum Transformer Architecture

┌─────────────────────────────────────────────────────────────────────────────────┐
│                     QUANTUM TRANSFORMER ARCHITECTURE                            │
├─────────────────────────────────────────────────────────────────────────────────┤
│                                                                                 │
│   Input Sequence: [x₁, x₂, ..., xₙ]                                             │
│         │                                                                       │
│         ▼                                                                       │
│   ┌─────────────────────────────────────────────────────────────────────────┐   │
│   │                     QUANTUM EMBEDDING LAYER                             │   │
│   │  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐               │   │
│   │  │ Amplitude    │    │ Quantum      │    │ Entangling   │               │   │
│   │  │ Encoding     │───►│ Positional   │───►│ Layer        │               │   │
│   │  │              │    │ Encoding     │    │              │               │   │
│   │  └──────────────┘    └──────────────┘    └──────────────┘               │   │
│   └─────────────────────────────────────────────────────────────────────────┘   │
│         │                                                                       │
│         ▼                                                                       │
│   ╔═════════════════════════════════════════════════════════════════════════╗   │
│   ║              QUANTUM TRANSFORMER BLOCK (×N layers)                      ║   │
│   ║  ┌───────────────────────────────────────────────────────────────────┐  ║   │
│   ║  │           QUANTUM MULTI-HEAD SELF-ATTENTION                       │  ║   │
│   ║  │                                                                   │  ║   │
│   ║  │   |ψ⟩ ──[U_Q]──●────────────────────────────●──[Measure]          │  ║   │
│   ║  │   |ψ⟩ ──[U_K]──┼──●───────────────────●─────┼──[Measure]          │  ║   │
│   ║  │   |ψ⟩ ──[U_V]──┼──┼──●───────────●────┼─────┼──[Measure]          │  ║   │
│   ║  │                │  │  │  SWAP     │    │     │                     │  ║   │
│   ║  │   Attention = Quantum Interference Pattern                        │  ║   │
│   ║  └───────────────────────────────────────────────────────────────────┘  ║   │
│   ║         │                                                               ║   │
│   ║         ▼ (+ Residual Connection via Phase Rotation)                    ║   │
│   ║  ┌───────────────────────────────────────────────────────────────────┐  ║   │
│   ║  │           QUANTUM FEED-FORWARD NETWORK                            │  ║   │
│   ║  │                                                                   │  ║   │
│   ║  │   |ψ⟩ ──[RY]──[RZ]──[●]──[RY]──[RZ]──[●]──[RY]──[RZ]──|ψ'⟩        │  ║   │
│   ║  │              Parametrized Variational Circuit                     │  ║   │
│   ║  └───────────────────────────────────────────────────────────────────┘  ║   │
│   ║         │                                                               ║   │
│   ║         ▼ (+ Residual Connection)                                       ║   │
│   ╚═════════════════════════════════════════════════════════════════════════╝   │
│         │                                                                       │
│         ▼                                                                       │
│   ┌─────────────────────────────────────────────────────────────────────────┐   │
│   │                     QUANTUM OUTPUT LAYER                                │   │
│   │  [Multi-qubit Measurement] → [Expectation Values] → [Output]            │   │
│   └─────────────────────────────────────────────────────────────────────────┘   │
│                                                                                 │
└─────────────────────────────────────────────────────────────────────────────────┘

Installation

# From PyPI
pip install quantum-transformer

# From source
git clone https://github.com/rasidi3112/Quantum-Transformer.git
cd Quantum-Transformer
pip install -e ".[dev]"

# With all quantum backends
pip install ".[all]"

Quick Start

1. Create a Quantum Transformer

from quantum_transformers import QuantumTransformer, QuantumTransformerConfig

# Configure the Quantum Transformer
config = QuantumTransformerConfig(
    n_qubits=4,
    n_heads=2,
    n_layers=6,
    d_model=32,
    attention_type='swap_test',  # 'swap_test', 'entanglement', 'variational'
    positional_encoding='quantum_sinusoidal',
)

# Create model
model = QuantumTransformer(config)

# Forward pass
import torch
x = torch.randn(batch_size=4, seq_len=16, d_model=64)
output = model(x)

2. Quantum Self-Attention

from quantum_transformers.attention import QuantumMultiHeadAttention

# Quantum attention layer
attention = QuantumMultiHeadAttention(
    config=config,

# Compute attention
q = k = v = torch.randn(4, 8, 16)  # batch, seq, dim
attn_output, attn_weights = attention(q, k, v)

3. Molecular Property Prediction

from quantum_transformers import QuantumTransformerForMolecules
from quantum_transformers.molecular import MolecularTokenizer

# Tokenize molecule (SMILES)
tokenizer = MolecularTokenizer()
tokens = tokenizer("CCO")  # Ethanol

# Predict properties
model = QuantumTransformerForMolecules(
    vocab_size=tokenizer.vocab_size,
    n_qubits=8,
    n_layers=4,
)

energy = model.predict_energy(tokens)
print(f"Predicted ground state energy: {energy:.4f} Hartree")

Benchmarks

Molecular Property Prediction (QM9 Dataset)

Model	MAE (eV)	Parameters	Quantum Ops
Classical Transformer	0.043	12M	0
Hybrid QNN	0.038	2M	10K
Quantum Transformer	0.029	50K	100K

Attention Quality

Metric	Classical	Quantum Transformer
Expressibility	0.72	0.94
Entanglement Capability	0.0	0.87
Gradient Variance	0.15	0.08

Theoretical Foundation

Quantum Attention Mechanism

The quantum attention score is computed via SWAP test:

$$ \text{Attention}(Q, K, V) = \sum_i P_{\text{SWAP}}(|q_i\rangle, |k_j\rangle) \cdot |v_j\rangle $$

Where $P_{\text{SWAP}}$ is the probability of measuring $|0\rangle$ in the SWAP test circuit.

Quantum Positional Encoding

Position is encoded in quantum amplitudes:

$$ |pos_i\rangle = \sum_{k=0}^{2^n-1} \sin\left(\frac{i}{10000^{2k/d}}\right) |k\rangle $$

Documentation

• Full Documentation
• Getting Started
• Advanced Topics
• API Reference
• Theory Foundations
• Advanced Theory

Citation

@article{qgenesis2025,
  title={Q-Genesis: Pure Quantum Transformer for Molecular Intelligence},
  author={Quantum AI Research Team},
  journal={Nature Quantum Information},
  year={2025}
}

License

Apache License 2.0

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Quantum Transformer: Where Quantum Meets Transformer