SHENZHEN, China, July  30, 2026  (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ:HOLO), (&#34;HOLO&#34; or the &#34;Company&#34;), a technology service provider, launched a Deep Spiking Quantum Neural Network

HOLO

MicroCloud Hologram Inc. Launches Deep Spiking Quantum Neural Network Technology for Noisy Image Classification

HOLO unveils DSQ-Net a quantum-assisted deep spiking network for noisy images

<section class="insight" data-version="1">
  <section data-block="information">
    <h3>Information</h3>
    <p>SHENZHEN, China, July  30, 2026  (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ:HOLO), (&amp;#34;HOLO&amp;#34; or the &amp;#34;Company&amp;#34;), a technology service provider, launched a Deep Spiking Quantum Neural Network</p>
  </section>

  <section data-block="ai-summary-paragraph">
    <h3>AI Summary</h3>
    <p>MicroCloud Hologram announced DSQ-Net, a hybrid quantum-classical deep learning system for noisy image classification. The model embeds variational quantum circuits into the training of deep spiking neural networks, achieving over 90% accuracy on unseen noisy data in simulations and showing slower degradation as noise rises. Management highlights strong cash reserves and long-term potential to bridge quantum computing with neuromorphic AI.</p>
  </section>

  <section data-block="ai-summary-bullets">
    <h3>Key Points</h3>
    <ul><li>HOLO unveiled DSQ-Net, a hybrid quantum-classical system for noisy images.</li><li>DSQ-Net trains Deep Spiking Neural Networks with a variational quantum circuit.</li><li>Simulations show DSQ-Net &gt;90% accuracy on unseen noisy images.</li><li>HOLO has &gt;3B RMB cash and plans &gt;$400M USD in frontier tech.</li></ul>
  </section>

  <section data-block="companies-mentioned">
    <h3>Companies Mentioned</h3>
    <ul><li><strong>MicroCloud Hologram Inc. (HOLO)</strong>: Announced DSQ-Net; positive R&amp;D milestone with long-term potential; no immediate revenue data.</li></ul>
  </section>

  <section data-block="category">
    <h3>Industry News</h3>
    <p>Industry News: reports a new tech development and strategic direction for HOLO; underscores potential long-run moat in quantum-neuromorphic AI.</p>
  </section>

  <section data-block="market-moving">
    <h3>Market-Moving</h3>
    <ul><li>DSQ-Net &gt;90% accuracy on unseen noisy data boosts credibility.</li><li>Cash reserves &gt;3 billion RMB may enable aggressive R&amp;D.</li><li>Plan to invest &gt;$400 million USD in frontier tech.</li><li>No revenue figures; hardware migration timing uncertain.</li></ul>
  </section>

  <section data-block="key-facts">
    <h3>Key Facts</h3>
    <ul><li>Date: July 30, 2026.</li><li>DSQ-Net integrates variational quantum circuit training.</li><li>Experiment: &gt;90% accuracy on unseen noisy images.</li><li>Cash reserves exceed 3 billion RMB.</li><li>Plan to invest &gt;$400 million USD in frontier tech.</li></ul>
  </section>

  <section data-block="trading-thesis">
    <h3>Trading Thesis</h3>
    <p>Long-term bullish if DSQ-Net advances to hardware and monetization within 12–24 months.</p>
  </section>
</section>


<link type="text/css" rel="stylesheet" href="https://www.globenewswire.com/styles/gnw_nitf.css"></link><p align="justify">SHENZHEN, China, July  30, 2026  (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ:<a class="ticker" href="https://www.benzinga.com/quote/HOLO">HOLO</a>), ("HOLO" or the &#34;Company&#34;), a technology service provider, launched a Deep Spiking Quantum Neural Network (DSQ-Net) for noisy image classification, marking an important engineering exploration in the direction of deep integration between quantum computing and neuromorphic computing.</p>  <p align="justify">This technology is based on HOLO&#39;s long-accumulated experience in quantum algorithms and neural network engineering. For the first time in an enterprise-level research and development framework, it systematically introduces a Variational Quantum Circuit (VQC) auxiliary training mechanism, constructing a novel hybrid quantum-classical deep learning system. Unlike previous studies that only used quantum circuits as feature mapping modules or quantum kernel functions, the core innovation of DSQ-Net lies in: directly embedding quantum circuits into the training process of Deep Spiking Neural Networks (SNN), serving as a key computational unit to address the problems of non-differentiable spiking events and stochastic neuronal dynamics, thereby reconstructing the trainability of SNN at the system level.<br></br></p>  <p align="justify">From the perspective of overall architecture, DSQ-Net adopts a clear hybrid quantum-classical layered design. The input end first receives noisy image data, and through a classical preprocessing module, encodes the pixel information into spatio-temporal spike sequences suitable for processing by spiking neurons. This process fully leverages the expressive advantage of SNN in the temporal dimension, such that image noise is no longer simply treated as an interference term, but is modeled and absorbed as part of the temporal signal.<br></br></p>  <p align="justify">Subsequently, the deep spiking neural network is responsible for extracting high-level spatio-temporal features. Unlike traditional convolutional neural networks that rely on continuous numerical values, this SNN forms event-driven feature representations that are robust to noise through the dynamic evolution of multi-layer spiking neurons. However, in the critical stages of weight updating and feature mapping, the system does not completely rely on classical training mechanisms; instead, it introduces variational quantum circuits as auxiliary optimization modules.<br></br></p>  <p align="justify">In HOLO&#39;s DSQ-Net, the quantum layer is not simply an add-on, but is designed as a computational component tightly coupled with the SNN. Specifically, the spike statistical features from the intermediate layers of the SNN are encoded into the amplitude space of the quantum state, and the high-dimensional spike distribution is compressed and mapped into the qubit state through amplitude encoding. Compared to angle encoding or basis state encoding, amplitude encoding has significant advantages in representation efficiency and information density, allowing a limited number of qubits to carry complex spike feature structures.<br></br></p>  <p align="justify">After encoding is completed, the parameterized variational quantum circuit evolves the quantum state. This circuit consists of multiple layers of tunable quantum gates, with its parameters being collaboratively updated by the classical optimizer along with the overall training objective. The quantum measurement results are treated as a probabilistic evaluation of the current network state, and this evaluation result in turn guides the update direction of the classical SNN weights. Through this mechanism, the originally non-differentiable spike firing process is indirectly embedded into a differentiable and tunable quantum optimization framework.<br></br></p>  <p align="justify"> It is worth noting that this hybrid training strategy does not simply replace classical computation with quantum computation, but fully leverages the complementary advantages of both. The classical deep SNN is responsible for large-scale data processing and temporal feature modeling, while the quantum VQC undertakes efficient exploration of complex nonlinear relationships and stochastic structures. The two form a closed loop through parameter sharing and feedback mechanisms, enabling the entire network to automatically adapt to changes in noise distribution during the training process.<br></br></p>  <p align="justify">At the technical implementation level, in response to the current immature state of quantum hardware, HOLO adopted a high-fidelity quantum simulator to systematically validate DSQ-Net. This choice not only ensures the reproducibility of experimental results but also provides a clear technical path for future migration to real quantum hardware. In the simulation environment, multiple image datasets with different noise intensities and noise types were constructed, and the model&#39;s generalization capability was comprehensively evaluated.<br></br></p>  <p align="justify">The experimental results show that on unseen noisy test images, HOLO&#39;s DSQ-Net maintains a classification accuracy stably above 90%, significantly outperforming pure classical SNN models of equivalent scale. More importantly, as the noise level continues to increase, the performance degradation of DSQ-Net is noticeably slower, indicating that the quantum-assisted training mechanism plays a key role in suppressing noise interference and stabilizing feature representations.<br></br></p>  <p align="justify">The significance of this technology lies not only in the improvement of individual metrics, but more importantly in providing an entirely new technical paradigm for intelligent perception systems in high-noise environments. Whether in low-illumination industrial inspection, complex traffic environment perception, or edge intelligent devices under constrained computing power conditions, the robustness and structural flexibility demonstrated by HOLO&#39;s DSQ-Net possess significant engineering value.<br></br></p>  <p align="justify">In addition, this technology also holds potential advantages in terms of energy efficiency. Spiking neural networks themselves are event-driven at their core and are naturally suited for low-power implementation; meanwhile, the parallelism of quantum computing in exploring state space is expected to further reduce overall computational complexity in the future. As quantum hardware and neuromorphic chips continue to develop, the hybrid architecture represented by DSQ-Net is poised to become an important component of next-generation intelligent computing systems.<br></br></p>  <p align="justify">From a longer-term perspective, this technology also provides a new practical direction for the development of quantum machine learning. It is no longer limited to theoretical speed improvements or complexity advantages, but truly embeds quantum computing into one of the most challenging real-world learning problems—the joint modeling of noise, uncertainty, and non-differentiable dynamics. It can be foreseen that, with the continuous improvement of the relevant ecosystem, deep spiking quantum neural networks are expected to unleash greater potential in the field of intelligent computing, becoming an important bridge connecting quantum computing and neuromorphic intelligence.</p>  <p align="justify">About MicroCloud Hologram Inc.</p>  <p align="justify">MicroCloud Hologram Inc. (NASDAQ:<a class="ticker" href="https://www.benzinga.com/quote/HOLO">HOLO</a>) is committed to the research and development and application of holographic technology. Its holographic technology services include holographic light detection and ranging (LiDAR) solutions based on holographic technology, holographic LiDAR point cloud algorithm architecture design, technical holographic imaging solutions, holographic LiDAR sensor chip design, and holographic vehicle intelligent vision technology, providing services to customers offering holographic advanced driving assistance systems (ADAS). MicroCloud Hologram Inc. provides holographic technology services to global customers. MicroCloud Hologram Inc. also provides holographic digital twin technology services and owns proprietary holographic digital twin technology resource libraries. Its holographic digital twin technology resource library utilizes a combination of holographic digital twin software, digital content, space data-driven data science, holographic digital cloud algorithms, and holographic 3D capture technology to capture shapes and objects in 3D holographic form. MicroCloud Hologram Inc. focuses on developments such as quantum computing and quantum holography, with cash reserves exceeding 3 billion RMB, and plans to invest more than 400 million in USD from the cash reserves to engage in blockchain development, quantum computing technology development, quantum holography technology development, and derivatives and technology development in frontier technology fields such as artificial intelligence AR. MicroCloud Hologram Inc.&#39;s goal is to become a global leading quantum holography and quantum computing technology company.</p>  <p align="justify">Safe Harbor Statement</p>  <p align="justify">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 &#34;may,&#34; &#34;will,&#34; &#34;intend,&#34; &#34;should,&#34; &#34;believe,&#34; &#34;expect,&#34; &#34;anticipate,&#34; &#34;project,&#34; &#34;estimate,&#34; 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&#39;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&#39;s goals and strategies; the Company&#39;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 (&#34;SEC&#34;), including the Company&#39;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&#39;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.</p>  <p align="justify">Contacts</p>  <p align="justify">MicroCloud Hologram Inc.</p>  <p align="justify">Email: IR@mcvrar.com</p> <img alt="" class="__GNW8366DE3E__IMG" src="https://www.globenewswire.com/newsroom/ti?nf=OTgwMDk4MiM3NzUxNDE5IzIyOTkzNDc="></img> <br></br><img alt="" src="https://ml.globenewswire.com/media/YTBjNjIwYmMtMWQ2Mi00MmEwLTk5ZGEtM2M2MmE0NjRjZTRhLTEzMTA4OTctMjAyNi0wNy0zMC1lbg==/tiny/MicroCloud-Hologram-Inc-.png" referrerpolicy="no-referrer-when-downgrade"></img>

MicroCloud Hologram announced DSQ-Net, a hybrid quantum-classical deep learning system for noisy image classification. The model embeds variational quantum circuits into the training of deep spiking neural networks, achieving over 90% accuracy on unseen noisy data in simulations and showing slower degradation as noise rises. Management highlights strong cash reserves and long-term potential to bridge quantum computing with neuromorphic AI.

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MicroCloud Hologram Inc. Launches Deep Spiking Quantum Neural Network Technology for Noisy Image Classification

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