q-biolat-protein-fitness-quantum

name	q-biolat-protein-fitness-quantum
description	Q-BIOLAT: Binary latent protein fitness landscapes for quantum annealing optimization. Maps protein sequences to binary latent spaces via pretrained protein language models, then uses quantum annealing (D-Wave) for fitness landscape exploration and protein engineering.
metadata	{"arxiv_id":"2603.17247","published":"2026-03-27","authors":"Truong-Son Hy","tags":["quantum-annealing","protein-fitness","binary-latent","protein-engineering","D-Wave"]}

Q-BIOLAT: Protein Fitness Quantum Annealing

Core Framework

Q-BIOLAT combines protein language model embeddings with quantum annealing for protein fitness landscape optimization. Key insight: projecting protein sequences into binary latent spaces enables direct mapping to QUBO (Quadratic Unconstrained Binary Optimization) problems solvable on quantum annealers.

Key Components

1. Binary Latent Projection: Pretrained protein language model (ESM/ProtTrans) encodes sequences, then binary quantization maps continuous embeddings to discrete binary vectors.
2. QUBO Formulation: Fitness landscape encoded as QUBO Hamiltonian H(x) = x^T Q x where Q captures epistatic interactions between residue positions.
3. Quantum Annealing: D-Wave hardware explores fitness landscape by minimizing H(x), finding high-fitness protein variants more efficiently than classical enumeration.

Mathematical Framework

• Protein sequence s → embedding E(s) ∈ R^d → binary quantization → x ∈ {0,1}^n
• QUBO matrix Q learned from fitness data: Q_ij captures pairwise epistasis between positions i,j
• Quantum annealer samples from p(x) ∝ exp(-β H(x)) where H(x) = Σ Q_ij x_i x_j

Activation Keywords

• 蛋白质适应度, protein fitness landscape, quantum annealing protein
• Q-BIOLAT, binary latent protein, D-Wave protein engineering
• 量子退火蛋白质, QUBO protein design, protein language model quantum
• protein optimization quantum, fitness landscape exploration

Implementation Patterns

Pattern 1: Protein Variant Discovery Pipeline

Protein Sequence → Language Model → Binary Embedding → QUBO Construction → Quantum Annealing → High-Fitness Variants

Pattern 2: Epistasis Analysis via QUBO Weights

The QUBO matrix Q directly encodes pairwise epistatic interactions — diagonal elements represent single-position fitness effects, off-diagonal elements capture residue-residue coupling.

Pitfalls

• Binary quantization loss: Continuous embedding → binary projection loses information; use multi-bit quantization if quantum hardware supports it
• QUBO embedding overhead: D-Wave Chimera/Pegasus topologies require minor embedding, which can use 3-10 physical qubits per logical variable
• Temperature effects: Quantum annealing at finite temperature samples from Boltzmann distribution, not ground state — use reverse annealing for refinement

References

• arXiv: 2603.17247 - "Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization"