SHENZHEN, China, April 24,
2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or
"MicroAlgo") (NASDAQ: MLGO), announced today the
development of an innovative technology, the Classical Boosted
Quantum Optimization Algorithm (CBQOA). This algorithm integrates
the search capabilities of classical computing with the
parallel computing characteristics of quantum computing,
effectively addressing constrained optimization problems without
modifying the cost function. It ensures that the evolution of
quantum states remains confined within the feasible subspace,
providing a more efficient solution for combinatorial optimization
problems.
Combinatorial optimization problems are widely prevalent in
practical applications, such as portfolio optimization, logistics
scheduling, network routing, and protein folding. In recent years,
quantum computing has been regarded as a crucial tool for tackling
these complex optimization challenges. Notable among these are
heuristic algorithms like the Quantum Approximate Optimization
Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE).
However, these algorithms often face significant challenges when
dealing with constrained optimization problems:
For instance, classical optimization problems typically involve
numerous constraints. Standard quantum optimization algorithms need
to indirectly incorporate these constraints by modifying the cost
function, which sharply increases the complexity of the solution
process. Moreover, existing quantum algorithms struggle to ensure
that the optimization search remains within the feasible solution
space, resulting in wasted computational resources and the
emergence of non-physical solutions. Classical optimization
techniques, having matured over many years, already possess
formidable problem-solving capabilities. Thus, effectively
combining the strengths of classical and quantum computing has
become a critical issue. MicroAlgo's CBQOA, by integrating the
efficient search capabilities of classical optimization algorithms
with the global search characteristics of quantum computing, paves
a new path in the field of combinatorial optimization.
The core idea of MicroAlgo's CBQOA is to first leverage
classical optimization methods to quickly identify high-quality
feasible solutions, then utilize quantum computing techniques to
further refine these solutions within their neighborhoods, aiming
to find even better outcomes.
Under the CBQOA framework, efficient classical optimization
algorithms—such as greedy algorithms, heuristic algorithms,
simulated annealing, or local search—are initially employed to
tackle the optimization problem. These classical methods, which
have been extensively studied, can deliver relatively optimal
feasible solutions within polynomial time, laying the groundwork
for subsequent quantum computing steps. The primary task of
classical optimization is to generate an initial solution and
construct a feasible solution subspace. Different classical
optimization strategies can be selected based on the problem type.
For example:
Maximum Cut Problem (Max-Cut): A heuristic algorithm can first
generate an initial partition, followed by quantum computing to
identify a superior cut.
Maximum Independent Set Problem (MIS): A greedy algorithm can be
used to find a sizable independent set, with quantum computing then
exploring better independent set configurations.
Minimum Vertex Cover (MVC): A classical algorithm can determine
a preliminary coverage scheme, which is then fine-tuned using
quantum computing.
After obtaining feasible solutions from classical optimization,
MicroAlgo CBQOA employs Continuous-Time Quantum Walk (CTQW) to
search the solution space. CTQW is a random walk model in quantum
computing, well-suited for efficiently searching feasible solutions
in combinatorial optimization problems.
In CBQOA, quantum states propagate efficiently within the
feasible solution space. Since CTQW employs Hamiltonian evolution,
its search paths align with the problem's structure, reducing the
likelihood of ineffective searches. Additionally, search efficiency
is enhanced through coherent superposition; the quantum
superposition property allows the system to explore multiple
solutions simultaneously, increasing the probability of identifying
the global optimum. Furthermore, CBQOA reduces reliance on indexing
feasible solutions. Unlike QAOA, which requires explicit encoding
of feasible solutions, CTQW evolves directly within the feasible
subspace, avoiding dependence on solution indexing.
After the quantum optimization search, the optimal solution is
obtained by measuring the quantum state. At this stage, CBQOA
integrates the evaluation mechanisms of classical optimization to
filter the measurement results, ensuring that the final solution
satisfies the constraints and achieves optimality.
The introduction of MicroAlgo's Classical Boosted Quantum
Optimization Algorithm (CBQOA) marks the dawn of a new era in the
fusion of quantum and classical computing for optimization. For a
long time, while quantum optimization algorithms have demonstrated
immense potential, their practical application in solving
constrained optimization problems has been hampered by challenges
related to hardware development and algorithmic complexity. CBQOA
cleverly combines classical optimization methods with quantum
computing techniques, successfully circumventing the traditional
quantum optimization algorithms' heavy reliance on cost functions.
It ensures that the search process remains confined to the feasible
solution subspace, thereby improving optimization efficiency and
solution quality. This innovative approach not only leverages the
mature techniques of classical optimization to lower the hardware
demands on quantum computing but also utilizes Continuous-Time
Quantum Walk (CTQW) to efficiently explore the solution space. This
provides a more practical and feasible solution for combinatorial
optimization problems. The breakthrough of this algorithm lies in
its departure from purely quantum optimization; instead, it employs
classical techniques to overcome the current limitations of quantum
computing, marking a significant step forward in the application of
quantum computing to optimization challenges.
MicroAlgo's CBQOA not only provides a practical and feasible
development path for quantum optimization but also further propels
quantum computing from theoretical research into real-world
applications. As the hardware and software ecosystems of quantum
computing continue to mature, CBQOA is expected to exert a profound
impact across multiple industries, particularly in addressing
complex optimization problems, potentially becoming a core
component of next-generation optimization algorithms. At the same
time, the development of this technology offers fresh perspectives
for interdisciplinary research, fostering the integration of fields
such as computer science, operations research, physics, and
artificial intelligence. In the forthcoming era of quantum
computing, hybrid optimization approaches like CBQOA will serve as
a critical driving force for industry transformation, providing
humanity with unprecedentedly powerful tools to tackle complex
computational challenges.
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. 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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SOURCE Microalgo.INC