Quantum-Enhanced Optimization Techniques

The field of optimization is witnessing a significant shift towards quantum-enhanced techniques, which leverage the principles of superposition and entanglement to improve the performance of classical algorithms. Researchers are exploring the application of quantum computing to address complex combinatorial optimization problems, with a focus on developing novel crossover strategies and device-algorithm co-design frameworks. These innovations have the potential to significantly reduce hardware overhead and improve solving efficiency. Noteworthy papers in this area include:

  • EAQGA, which proposes a novel quantum genetic algorithm that introduces an innovative crossover strategy, resulting in improved fitness values and reduced circuit depth.
  • Device-Algorithm Co-Design of Ferroelectric Compute-in-Memory In-Situ Annealer, which presents a ferroelectric compute-in-memory in-situ annealer that overcomes the challenges of traditional Ising annealers, reducing energy consumption and time cost.

Sources

EAQGA: A Quantum-Enhanced Genetic Algorithm with Novel Entanglement-Aware Crossovers

Application of the Brain Drain Optimization Algorithm to the N-Queens Problem

Device-Algorithm Co-Design of Ferroelectric Compute-in-Memory In-Situ Annealer for Combinatorial Optimization Problems

Using quantum annealing to generate test cases for cyber-physical systems

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