COMET: Combinatorial Optimization for Multiplex Editing Targets Via Constraint-Preserving QAOA
arXiv:2607.02622v1 Announce Type: cross Abstract: Multiplex CRISPR-Cas9 gene editing requires selecting one guide RNA per target gene subject to cross-gene interactions: a constrained combinatorial problem that can be formulated as a Quadratic Unconstrained Binary Optimization (QUBO) and solved via the Quantum Approximate Optimization Algorithm (QAOA). The one-hot per-gene constraint is conventionally enforced by adding quadratic penalty terms to the cost Hamiltonian, but penalty coefficient selection is heuristic and penalties amplify hardware noise. An alternative is to enforce the constrain
This research addresses a critical optimization challenge in quantum computing for genetic engineering, indicating a maturation of quantum solutions for complex biological problems.
Improving the efficiency and reliability of quantum algorithms for gene editing could significantly accelerate advancements in synthetic biology and therapeutics.
The ability to more precisely and efficiently perform multiplex gene editing via quantum algorithms could open new avenues for drug discovery and bioengineering.
- · Synthetic biology companies
- · Quantum computing hardware developers
- · Pharmaceutical R&D
- · Academic researchers in quantum biology
- · Traditional genetic engineering methods (potentially, long-term)
- · Companies reliant solely on classical optimization for bioengineering
More accurate and scalable quantum solutions for complex biological problems like multiplex gene editing become feasible.
Accelerated development of novel CRISPR-based therapies and synthetic biological systems due to improved design methodologies.
The integration of quantum computing becomes a standard tool in advanced biotech R&D, requiring new skills and infrastructure.
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Read at arXiv cs.AI