Duyuru • May 16
MicroAlgo Inc. Proposes Multi-Objective Evolutionary Algorithm for Quantum Circuit Design
MicroAlgo Inc. announced the proposal of a powerful solution—a multi-objective evolutionary search strategy, which is an innovative automated tool that can assist in designing quantum circuits, thereby bringing breakthroughs to quantum algorithm development. The Multi-Objective Evolutionary Algorithm (MOEA) is a class of optimization algorithms based on evolution, specifically designed to address problems involving multiple conflicting objectives. Its working principle mimics the process of natural selection by randomly generating a set of candidate solutions in the solution space and, through iterative processes across multiple generations, continuously improving the quality of solutions through operations such as crossover, mutation, and selection. Ultimately, this evolutionary process can generate a solution set with higher fitness, i.e., optimal solutions that satisfy multiple objectives. The innovation of the Multi-Objective Evolutionary Algorithm technology developed by MicroAlgo lies in its ability to automatically design quantum circuits from "zero." In other words, this technology does not require a pre-defined specific circuit design but instead gradually constructs quantum circuits capable of achieving the target functionality by combining search and optimization methods with a universal library of quantum circuit components. One of the key features of MicroAlgo's algorithm is its task-universal library. This library contains a large number of different quantum circuit components, whose combinations and parameterization can construct circuits that implement complex functions. This design approach means that developers do not need to manually design circuits; instead, the algorithm automatically searches for the optimal circuit configuration based on the input/output requirements of the task. More importantly, this algorithm is not only capable of designing circuits but also, through its multi-objective characteristics, can balance trade-offs among various performance metrics. For example, during the design process, the algorithm considers not only the accuracy of the quantum circuit but also other critical metrics such as the circuit's width, depth, and the number of gates used. This is particularly important for the current stage of quantum computing hardware development, as first-generation quantum processors are extremely limited in resources (such as the number of gates and qubits), and the algorithm must achieve optimal performance within these limited resources. To validate the effectiveness of the multi-objective evolutionary algorithm, MicroAlgo applied it to the automated design of classic quantum algorithms. Specifically, the Quantum Fourier Transform and Grover's Search Algorithm were selected as test cases. The Quantum Fourier Transform is a widely used transformation in quantum computing, playing a significant role in many algorithms, such as Shor's factorization algorithm. Meanwhile, Grover's Search Algorithm is considered another foundational algorithm in quantum computing, capable of finding target data in an unsorted dataset at a faster speed than classical search algorithms. In these two tests, the multi-objective evolutionary algorithm was able to find circuit structures that meet the input/output mapping requirements of these algorithms by combining components from the quantum circuit component library. After multiple iterations, the algorithm not only discovered textbook-style classic quantum circuit designs but also found alternative structures that achieve the same functionality. This demonstrates that the algorithm has the capability to efficiently design quantum circuits and can provide multiple alternative circuit solutions, offering great flexibility for the optimization of quantum computing algorithms. The technical implementation behind the multi-objective evolutionary algorithm involves several key steps and processes. First, in the initial stage, the algorithm generates a set of random quantum circuits. These circuits are composed of quantum components from the library and include adjustable parameters. Subsequently, the algorithm simulates each quantum circuit and evaluates its performance. The evaluation metrics include the circuit's accuracy, the number of gates used, the circuit's width, and its depth. Next, the algorithm filters and optimizes the circuits based on these metrics. Through crossover operations (similar to genetic recombination in biological evolution), the algorithm "crosses" two high-performing circuits to generate new candidate circuits; through mutation operations, the algorithm randomly modifies certain parts of the circuits to introduce new design possibilities. This process is repeated continuously, with each generation eliminating poorly performing circuits while retaining and optimizing high-performing circuits until the optimal solution is found. The core advantage of the multi-objective evolutionary algorithm lies in its ability to optimize multiple metrics simultaneously. For example, in quantum computing, circuit depth and accuracy are often conflicting objectives: deeper circuits may offer higher accuracy but increase the complexity of execution and hardware requirements. Through this algorithm, developers can find the optimal balance point between these objectives, ensuring that the circuit meets the demands of efficient computation while being implementable under existing hardware conditions. The multi-objective evolutionary algorithm developed by MicroAlgo is not only a significant technical breakthrough but also has the potential to change the development direction of the quantum computing industry in multiple ways. First, the introduction of automated tools greatly reduces the difficulty of quantum algorithm development. Currently, the barrier to quantum computing development is high, typically requiring experts with deep backgrounds in quantum physics, quantum information science, and computer science to design effective quantum algorithms. However, with this multi-objective evolutionary algorithm, developers only need to define the objectives of the computational task, and the algorithm can automatically generate circuit designs that meet the requirements, thereby lowering the technical barriers to quantum algorithm development. Second, this algorithm significantly enhances the efficiency and quality of quantum algorithms. Traditional quantum algorithm design relies on the experience and intuition of experts, whereas this evolutionary algorithm can explore a broader design space, even discovering optimization solutions that humans might not easily find. Especially on resource-constrained quantum hardware, this algorithm can find optimal solutions for different tasks, effectively improving the computational performance of the hardware. Finally, the multi-objective evolutionary algorithm paves the way for future applications of quantum computing.