SHENZHEN, China, Jan. 21,
2025 /PRNewswire/ -- MicroCloud Hologram Inc.
(NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, have developed a quantum
algorithm technology for deep convolutional neural network
(CNN) exchange submissions, aimed at overcoming the computational
bottlenecks of traditional CNNs and achieving performance
improvements through the advantages of quantum computing. The core
innovation of this technology lies in the design and implementation
of the Quantum Convolutional Neural Network (QCNN). The QCNN not
only fully replicates the output of classical CNNs but also
overcomes common challenges in quantum computing, such as the
difficulty of implementing nonlinear operations. Through carefully
designed quantum circuits, HOLO has successfully implemented
nonlinear activation functions and pooling operations within the
quantum framework, opening a new door for quantum deep learning.
More importantly, the architecture of the QCNN significantly
improves computational efficiency in both forward and backward
propagation, providing strong support for the training process of
deep neural networks.
From a technical perspective, the implementation of QCNN
consists of several key modules. First, it designs an input method
based on quantum state encoding, which maps high-dimensional data
into quantum states. This encoding technique leverages the
properties of quantum state superposition and entanglement,
allowing the convolution operation to be performed in parallel
within high-dimensional space, significantly reducing computational
complexity. Next, HOLO developed a set of quantum convolution
kernels, which are implemented as unitary operations and can
efficiently extract local features from the input data. By
combining the inner product calculations of quantum states, the
convolution process is completed at quantum speed.
For the implementation of nonlinear activation functions, HOLO
introduces a measurement-based nonlinear operation. By performing
partial measurements on quantum states, this approach achieves
nonlinear mapping while preserving quantum superposition. This
method overcomes the bottleneck of implementing nonlinear
operations in quantum computing, while maintaining the unitarity of
the computational process. Furthermore, QCNN also supports pooling
operations, which are performed through reduction measurements of
quantum states, making the feature dimension reduction process more
efficient.
In terms of training, HOLO proposes an optimization algorithm
based on quantum gradient computation. This method utilizes the
parameterized representation of quantum states and combines it with
the gradient descent method, enabling efficient updates of network
parameters. To validate the performance of QCNN, numerical
simulations of classification tasks were conducted on relevant
datasets. The results show that, compared to classical CNNs, QCNN
achieves comparable classification accuracy, but with significant
advantages in computational speed and resource utilization
efficiency. Particularly when handling large-scale datasets and
high-dimensional inputs, the potential of QCNN is fully
demonstrated.
The development of this technology is not only theoretically
groundbreaking but also shows great potential in practical
applications. In the field of image recognition, the performance
improvements of QCNN enable it to handle more complex tasks in
various scenarios. For instance, in medical image analysis, QCNN
can quickly and accurately detect abnormal lesions, providing
doctors with reliable diagnostic support. In the autonomous driving
domain, QCNN's efficient computational capabilities allow real-time
processing of environmental information around the vehicle,
enhancing driving safety. Furthermore, QCNN also holds potential
value in fields such as natural language processing and financial
data analysis.
Although HOLO's QCNN has made significant progress, future
research directions remain full of challenges and opportunities.
First, further optimizing quantum circuits to handle larger
datasets and more complex tasks is an issue worth exploring.
Additionally, limitations in quantum computing hardware, such as
noise and the constraints on the number of qubits, remain major
bottlenecks for the technology's development. To address these
issues, it is essential to continue exploring more robust quantum
algorithm designs while closely monitoring developments in quantum
hardware to ensure the practical feasibility of the technology.
Quantum Convolutional Neural Networks (QCNN), as an innovative
deep learning framework, not only provide new ideas for the
practical application of quantum computing but also bring infinite
possibilities for the future development of deep learning. The
implementation of HOLO's quantum algorithm technology for deep
convolutional neural network exchange submissions not only
demonstrates the immense potential of combining quantum computing
with machine learning but also marks an important step toward a new
era of intelligent computing.
Looking ahead, the potential of quantum convolutional neural
networks will continue to be explored with the further advancements
in quantum computing hardware. The breakthrough significance of
this technology lies not only in its ability to address current
computational bottlenecks but also in the new perspective it brings
to the field of deep learning. The parallelism and superposition
capabilities of quantum computing enable QCNN to efficiently
process high-dimensional data, showing exceptional adaptability,
especially when faced with increasingly complex data environments.
By deeply integrating with industry needs, QCNN is expected to play
an irreplaceable role in fields such as healthcare, transportation,
finance, and fundamental science.
More importantly, the success of this technology also lays the
foundation for the development of next-generation intelligent
systems. From quantum artificial intelligence to collaborative
frameworks for distributed quantum computing, the development of
QCNN marks our progression toward a new computing era driven by
quantum technology. This is not just a technological leap, but also
a significant driving force for socioeconomic development. The
power of quantum computing will provide entirely new solutions to
many complex problems humanity faces. The successful development of
QCNN is the starting point of this journey and is destined to
become a milestone in the future integration of quantum technology
and artificial intelligence.
About MicroCloud Hologram Inc.
MicroCloud is committed to providing leading holographic
technology services to its customers worldwide. MicroCloud's
holographic technology services include high-precision holographic
light detection and ranging ("LiDAR") solutions, based on
holographic technology, exclusive holographic LiDAR point cloud
algorithms architecture design, breakthrough technical holographic
imaging solutions, holographic LiDAR sensor chip design and
holographic vehicle intelligent vision technology to service
customers that provide reliable holographic advanced driver
assistance systems ("ADAS"). MicroCloud also provides holographic
digital twin technology services for customers and has built a
proprietary holographic digital twin technology resource library.
MicroCloud's holographic digital twin technology resource library
captures shapes and objects in 3D holographic form by utilizing a
combination of MicroCloud's holographic digital twin software,
digital content, spatial data-driven data science, holographic
digital cloud algorithm, and holographic 3D capture technology. For
more information, please visit http://ir.mcholo.com/
Safe Harbor Statement
This press release contains forward-looking statements as
defined by the Private Securities Litigation Reform Act of 1995.
Forward-looking statements include statements concerning plans,
objectives, goals, strategies, future events or performance, and
underlying assumptions and other statements that are other than
statements of historical facts. When the Company uses words such as
"may," "will," "intend," "should," "believe," "expect,"
"anticipate," "project," "estimate," or similar expressions that do
not relate solely to historical matters, it is making
forward-looking statements. Forward-looking statements are not
guarantees of future performance and involve risks and
uncertainties that may cause the actual results to differ
materially from the Company's expectations discussed in the
forward-looking statements. These statements are subject to
uncertainties and risks including, but not limited to, the
following: the Company's goals and strategies; the Company's future
business development; product and service demand and acceptance;
changes in technology; economic conditions; reputation and brand;
the impact of competition and pricing; government regulations;
fluctuations in general economic; financial condition and results
of operations; the expected growth of the holographic industry and
business conditions in China and
the international markets the Company plans to serve and
assumptions underlying or related to any of the foregoing and other
risks contained in reports filed by the Company with the Securities
and Exchange Commission ("SEC"), including the Company's most
recently filed Annual Report on Form 10-K and current report on
Form 6-K and its subsequent filings. For these reasons, among
others, investors are cautioned not to place undue reliance upon
any forward-looking statements in this press release. Additional
factors are discussed in the Company's filings with the SEC, which
are available for review at www.sec.gov. The Company undertakes no
obligation to publicly revise these forward-looking statements to
reflect events or circumstances that arise after the date
hereof.
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SOURCE MicroCloud Hologram Inc.
