Grover DNA sequence alignment and VQE protein folding

1. DNA Sequence Alignment with Grover’s Search

Problem: Find a short DNA sequence within a large genome database — a search problem with ~3 billion base pairs. Quantum advantage: Grover’s algorithm searches unsorted data in O(√N) queries vs O(N) classical — quadratic speedup. Full example: github.com/softquantus/qcos/examples/genomics/dna_search_grover.py

import os, requests, base64

API_KEY = os.environ["QCOS_API_KEY"]
BASE    = "https://api.softquantus.com"
HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

# Encode DNA as binary (A=00, T=01, G=10, C=11)
target_sequence = "ATCGATCG"  # 8 bases = 16 bits

resp = requests.post(f"{BASE}/api/v1/algorithms/grover/run", headers=HEADERS, json={
    "n_qubits": 8,
    "target": target_sequence,
    "encoding": "dna",     # QCOS handles DNA → binary encoding
    "shots": 2048,
    "backend": "aer_simulator",
})

result = resp.json()
print("Found:", result["found_state"])
print("Probability:", result["probability"])
print("Matches at positions:", result.get("match_positions", []))

2. Protein Folding Energy Minimisation with VQE + Hybrid

Problem: Find the minimum energy configuration of a protein — the protein folding problem. Classical methods become exponentially expensive for chains > 100 amino acids. Quantum advantage: VQE (Variational Quantum Eigensolver) finds the ground state energy of a Hamiltonian that encodes the protein folding problem.

# Protein folding as a lattice model
amino_acids = "HPHPPHHPHPPH"   # H = hydrophobic, P = polar

resp = requests.post(f"{BASE}/api/v1/algorithms/hybrid/run", headers=HEADERS, json={
    "workflow": "protein_folding",
    "sequence": amino_acids,
    "model": "lattice_hp",
    "classical_backend": "azure_cpu_worker",
    "quantum_backend": "aer_simulator",
    "max_iterations": 100,
    "ansatz": "RealAmplitudes",
})

result = resp.json()
print("Minimum energy:", result["ground_state_energy"])
print("Optimal folding:", result["folding_configuration"])
print("Credits used:", result["credits_used"])

3. Drug-Target Interaction Prediction

Problem: Predict binding affinity between a drug molecule and a protein target — critical for drug discovery.

resp = requests.post(f"{BASE}/api/v1/algorithms/hybrid/run", headers=HEADERS, json={
    "workflow": "molecular_simulation",
    "molecule": {
        "smiles": "CC(=O)Oc1ccccc1C(=O)O",   # Aspirin
        "name": "aspirin"
    },
    "target_protein": "COX-2",
    "n_qubits": 12,
    "quantum_backend": "aer_simulator",
    "classical_backend": "azure_cpu_worker",
})

result = resp.json()
print("Binding energy (kcal/mol):", result["binding_energy"])
print("Interaction sites:", result["interaction_sites"])

More genomics examples on GitHub

dna_search_grover.py — Genome database search
protein_folding_vqe.py — Lattice HP model with VQE
molecular_docking.py — Drug-target binding prediction
sequence_alignment_qram.py — Quantum RAM-based alignment

→ View all genomics examples

​1. DNA Sequence Alignment with Grover’s Search

​2. Protein Folding Energy Minimisation with VQE + Hybrid

​3. Drug-Target Interaction Prediction

​More genomics examples on GitHub

1. DNA Sequence Alignment with Grover’s Search

2. Protein Folding Energy Minimisation with VQE + Hybrid

3. Drug-Target Interaction Prediction

More genomics examples on GitHub