Quantum Genetic Algorithm: Evolving Circuits to Optimize
Quantum circuits that evolve
Researchers propose a gate-based Quantum Genetic Algorithm (QGA) for solving tough global optimization problems.
Instead of DNA, each individual is a tiny quantum circuit. When measured, the circuit produces bits that decode into real numbers. The algorithm applies mutation and crossover directly to the gates, in fixed or variable depth, and scores candidates by sampling the circuit many times.
- Using superposition via the Hadamard gate consistently speeds up and stabilizes convergence across benchmark functions like Rastrigin.
- Letting pairs of individuals share entanglement gives an extra early boost, suggesting quantum correlations help the population explore smarter.
The takeaway: combining evolutionary search with core quantum features can make optimization more efficient and robust. Gate-based QGAs look like a promising path toward practical quantum-enhanced optimization as hardware improves.
Authors: Leandro C. Souza, Laurent E. Dardenne, Renato Portugal. Read more: http://arxiv.org/abs/2511.05254v1
Paper: http://arxiv.org/abs/2511.05254v1
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