Quantum-inspired Evolutionary Algorithm (QEA): Recent Trends and Implementation Insights

The Quantum-inspired Evolutionary Algorithm (QEA) has gained significant attention in optimization research over recent years as an intelligent computational method that integrates quantum computing concepts with traditional evolutionary algorithms, demonstrating powerful global search capabilities. Particularly for discrete optimization problems like the knapsack problem, QEA shows remarkable advantages due to its unique quantum bit encoding mechanism.

The core concept of QEA leverages the superposition property of quantum bits (Qubits) to represent solution diversity. Unlike conventional evolutionary algorithms, QEA individuals consist of multiple quantum bits where each qubit can simultaneously represent both 0 and 1 states. This implementation enables the population to explore the solution space more efficiently during evolution. In code implementation, qubits are typically represented as probability amplitudes using arrays of complex numbers or pairs of real-valued parameters.

For typical discrete binary optimization problems like the knapsack problem, QEA's processing flow involves these key algorithmic steps: First, the problem is encoded into quantum bit format where each item's selection corresponds to a qubit's state. Second, quantum gate rotation operations update the probability amplitudes of qubits, guiding the population toward better solutions. The rotation gate operation in code is often implemented through angle adjustment functions that modify qubit probabilities based on fitness comparisons. Finally, observation operations collapse the qubit superposition states into concrete 0 or 1 solutions through probabilistic measurement functions that convert quantum states to binary solutions.

QEA's advantages lie in its parallel search capability and effective exploration of high-dimensional solution spaces. Compared to traditional methods like genetic algorithms, QEA typically converges faster and shows reduced susceptibility to local optima in complex optimization problems. Recent research has explored integrating QEA with deep learning techniques, further enhancing its application potential in industrial optimization, logistics scheduling, and other practical scenarios through hybrid algorithms that combine quantum-inspired optimization with neural network architectures.