# MicroAlgo Inc. Develops Multi-Objective Evolutionary Algorithm to Advance Quantum Circuit Innovation

- Link: https://www.thailand-business-news.com/pr-news/microalgo-inc-develops-multi-objective-evolutionary-algorithm-to-advance-quantum-circuit-innovation
- Published: 2026-05-14T22:40:00+07:00
- Author: PR Newswire

SHENZHEN, China, May 14, 2026 /PRNewswire/ — MicroAlgo Inc. (the "Company" or "MicroAlgo")(
NASDAQ: MLGO), today 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. As quantum computing gradually moves from the laboratory to
practical applications, automated tools will become increasingly important. The 
technology developed by MicroAlgo is not only suitable for existing quantum computing
tasks but also capable of addressing the more complex application demands of the
future. Whether in fields such as chemical simulation, financial risk analysis, 
or cryptography, the design of quantum algorithms can be significantly enhanced 
through this evolutionary algorithm.

The multi-objective evolutionary algorithm represents a major breakthrough in quantum
algorithm development. By combining a task-universal library, automated design, 
and multi-objective optimization, this algorithm not only simplifies the quantum
circuit design process but also improves the efficiency and flexibility of circuits.
The introduction of this technology marks a new stage in quantum computing, providing
a solid foundation for the widespread application of quantum computers across multiple
industries. In the future, as quantum hardware continues to advance, there is reason
to believe that this multi-objective evolutionary algorithm will have an even more
profound impact in the field of quantum computing and drive the emergence of more
breakthrough achievements.

**About MicroAlgo Inc.**

MicroAlgo Inc. (the "MicroAlgo"), a Cayman Islands exempted company, is dedicated
to the development and application of bespoke central processing algorithms. MicroAlgo
provides comprehensive solutions to customers by integrating central processing 
algorithms with software or hardware, or both, thereby helping them to increase 
the number of customers, improve end-user satisfaction, achieve direct cost savings,
reduce power consumption, and achieve technical goals. The range of MicroAlgo’s 
services includes algorithm optimization, accelerating computing power without the
need for hardware upgrades, lightweight data processing, and data intelligence services.
MicroAlgo’s ability to efficiently deliver software and hardware optimization to
customers through bespoke central processing algorithms serves as a driving force
for MicroAlgo’s long-term development.

**Forward-Looking Statements**

This press release contains statements that may constitute "forward-looking statements."
Forward-looking statements are subject to numerous conditions, many of which are
beyond the control of MicroAlgo, including those set forth in the Risk Factors section
of MicroAlgo’s periodic reports on Forms 10-K and 8-K filed with the SEC. Copies
are available on the SEC’s website, [www.sec.gov](http://www.sec.gov/). Words such
as "expect," "estimate," "project," "budget," "forecast," "anticipate," "intend,""
plan," "may," "will," "could," "should," "believes," "predicts," "potential," "continue,"
and similar expressions are intended to identify such forward-looking statements.
These forward-looking statements include, without limitation, MicroAlgo’s expectations
with respect to future performance and anticipated financial impacts of the business
transaction.

MicroAlgo undertakes no obligation to update these statements for revisions or changes
after the date of this release, except as may be required by law.

 

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