MicroCloud Hologram Inc. Releases Learnable Quantum Spectral Filter Technology for Hybrid Graph Neural Networks

Rhea-AI Summary

MicroCloud Hologram (NASDAQ: HOLO) released a learnable quantum spectral filter for hybrid graph neural networks on January 5, 2026. The technology maps the graph Laplacian to a trainable quantum circuit to perform spectral transformation, fusing graph convolution and pooling into a quantum process that outputs an n = log(N) probability vector for compressed features.

HOLO states the circuit can compress a 1,000,000-node graph using roughly 20 qubits, uses amplitude/probability encoding, and trains via classical-quantum hybrid optimization. The company also reports cash reserves exceeding 3 billion RMB and plans to invest over 400 million USD into frontier technologies.

Positive

• Logarithmic compression maps N-dimensional signals to n=log(N) features
• Quantum circuit claims ~20 qubits for a 1,000,000-node graph
• End-to-end hybrid quantum-classical training via parameter-shift rule
• Reported cash reserves of over 3 billion RMB available

Negative

• Approach relies on medium-scale quantum hardware availability
• Key claims lack disclosed empirical benchmarks or published readouts

Key Figures

Compressed feature dimension n = log(N) Output dimension after quantum convolution layer

Large graph example 1,000,000 nodes Network size where classical spectral convolution is described as impractical

Qubit requirement about 20 qubits Quantum circuit resources for a one-million-node graph example

Cash reserves exceeding 3 billion RMB Company cash position noted in corporate description

Planned investment more than 400 million USD Intended spending from cash reserves on frontier technologies

Market Reality Check

$2.77 Last Close

Volume Volume 1,391,782 is 2.35x the 20-day average of 593,356, indicating elevated pre-news activity. high

Technical Shares at $2.77 are trading below the 200-day MA of $7.66 and far under the $370 52-week high.

Peers on Argus 1 Up

HOLO’s pre-news move of 4.92% contrasts with mixed peers; some related names like NEON and LINK are up, while others such as DSWL and ELTK are down. Momentum scanner only flags KULR moving up, suggesting stock-specific rather than broad sector momentum.

Historical Context

Date	Event	Sentiment	Move	Catalyst
Dec 22	Quantum simulation tech	Positive	+2.7%	FPGA-based quantum computing simulation framework aimed at scalable qubit simulation.
Dec 18	Quantum 3D imaging	Positive	-1.4%	Quantum-enhanced CNN system for 3D image reconstruction across six core modules.
Dec 04	Quantum 3D model	Positive	+4.3%	Quantum-driven 3D intelligent model integrating quantum computing with AI generation.
Nov 20	Quantum synchronization	Positive	-2.3%	New "quantum degree" concept to quantify synchronization in quantum systems.
Nov 17	Quantum big data system	Positive	+2.9%	Quantum-empowered big data real-time computing system with performance improvements.

Pattern Detected

Recent quantum-tech announcements have produced mixed but slightly more positive-aligned price reactions, with both rallies and selloffs following seemingly upbeat R&D news.

Recent Company History

Over the past few months, HOLO has repeatedly highlighted quantum-focused R&D. Releases on Nov 17, 2025, Nov 20, 2025, Dec 4, 2025, Dec 18, 2025, and Dec 22, 2025 detailed quantum big data systems, synchronization metrics, 3D intelligent models, and FPGA-based quantum simulation. Several of these cited cash reserves above 3 billion RMB and planned investment of over $400 million into frontier technologies, framing today’s quantum GNN advance as part of an ongoing quantum strategy.

Market Pulse Summary

The stock moved +7.9% in the session following this news. A strong positive reaction aligns with HOLO’s pattern of occasional rallies on quantum-technology announcements, where similar news saw several 2–4% gains. However, the stock trades far below its $370 52-week high and under the $7.66 200-day MA, indicating a longer-term downtrend. Elevated volume could also reflect position covering or short-term trading, which may limit durability if fundamental adoption of these technologies lags.

Key Terms

graph laplacian operator technical

"The mathematical foundation of this technology stems from the spectral structure of the graph Laplacian operator."

A graph Laplacian operator is a mathematical tool that summarizes how nodes in a network are connected and how something (like information, risk, or influence) spreads between them. Think of it like a map of pipes showing where flow is fast or bottlenecked; analysts use it to spot tightly linked groups, weak links, or points that amplify shocks, which helps investors assess contagion risk, portfolio diversification, and structural vulnerabilities in networks.

quantum circuit technical

"The quantum circuit performs spectral transformation based on the graph structure."

A quantum circuit is a sequence of controlled operations that manipulate quantum bits (qubits) to carry out a computation, similar to the wiring and logic gates in a traditional computer but using quantum properties like superposition and entanglement. For investors it matters because the design and fidelity of quantum circuits determine what problems quantum machines can solve, how fast and reliably they perform, and therefore influence the commercial potential and technical risk of companies building quantum hardware and software — think of circuits as the blueprints that decide future performance and value.

hilbert space technical

"The Hilbert space dimension constructed by this method is 2^n, theoretically capable of one-to-one mapping"

A Hilbert space is a mathematical setting like a roomy, infinitely extendable coordinate system where each 'point' represents a possible pattern, signal, or model outcome and distances measure how different those patterns are. For investors, it underpins advanced quantitative tools—used in pricing, risk models and signal processing—by turning complex functions and time series into geometric objects so similarities, projections and optimizations become concrete and computable.

graph neural networks technical

"Traditional graph neural networks utilize the eigenvalues of L to filter signals"

Graph neural networks are a type of artificial intelligence that learns from data organized as points and the connections between them — think of it as learning from a map or a social network rather than a spreadsheet. They matter to investors because many real-world problems (supply chains, customer relationships, fraud links, drug-target interactions) are about relationships, and these models can reveal patterns or predict outcomes that traditional methods miss, potentially improving decisions and competitive advantage.

qft-structured quantum circuit technical

"HOLO proves that through the QFT-structured quantum circuit, the feature space of graphs can be approximated."

A qft-structured quantum circuit is a design for a quantum computer that uses the quantum Fourier transform as a central building block to process information, much like a radio tuner extracts frequencies from a signal. It matters to investors because this structure enables certain powerful algorithms — for example for factoring or signal analysis — so advances or patents in these circuits can accelerate practical quantum computing, change competitive positions, and affect industries like encryption and computing services.

amplitude encoding technical

"The input signal is loaded into the quantum state using amplitude encoding or probability encoding."

A method from quantum computing that stores a list of classical numbers by turning them into the strengths (amplitudes) of a quantum state so many values can be represented using far fewer physical bits. For investors, it matters because this packing can make quantum algorithms dramatically faster or more compact for certain problems, so claims about amplitude encoding affect the realistic performance, cost and scalability of quantum hardware and software.

graph convolution technical

"proposes a quantum spectral filter that fuses graph convolution and pooling operations into a complete quantum computing process."

A graph convolution is a machine-learning operation that summarizes information for a point in a network by combining that point’s data with the data of its immediate neighbors, like averaging a house’s temperature with nearby houses to see a local pattern. Investors care because it helps algorithms detect patterns in connected data—ownership links, transaction chains, or shared characteristics—that can reveal hidden risks, fraud, or investment opportunities and improve forecasting.

AI-generated analysis. Not financial advice.

SHENZHEN, China, Jan. 5, 2026 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for hybrid graph neural networks. This achievement proposes a brand-new quantum-classical hybrid graph neural network foundational architecture. By mapping the graph Laplacian operator to a trainable quantum circuit, it enables graph signal processing to gain exponential compression capability and a new computational perspective, representing a key step for quantum graph machine learning toward practicalization.

HOLO's this technology proposes a quantum spectral filter that fuses graph convolution and pooling operations into a complete quantum computing process. The input signal is loaded into the quantum state using amplitude encoding or probability encoding. The quantum circuit performs spectral transformation based on the graph structure. After passing through learnable rotation gates and controlled gates, the measurement results of the output state naturally form an n-dimensional probability distribution vector, where n = log(N). This property enables the quantum circuit to directly map high-dimensional graph signals to low-dimensional space, achieving a unified function of convolution + pooling.

HOLO points out that the quantum measurement process is essentially a structured nonlinear mapping, capable of overcoming the complex structural search problems in classical GNN pooling operations. In quantum circuits, nonlinear behaviors that are difficult to simulate in classical networks are automatically realized through quantum state collapse, making the pooling results both compressive and separable while preserving key spectral features of the graph structure.

This means that a graph of size N, after processing through the quantum convolution layer, can immediately obtain log(N)-dimensional compressed features, with computational costs remaining controllable even for large graphs. For a network with one million nodes, classical spectral convolution is almost impossible to run in terms of memory and time, whereas this quantum circuit requires only about 20 qubits.

The mathematical foundation of this technology stems from the spectral structure of the graph Laplacian operator. The Laplacian operator L = D - A has a natural coupling relationship with the graph structure, and its eigenvalues reflect important properties such as graph connectivity, clustering structure, and smoothness. Traditional graph neural networks utilize the eigenvalues of L to filter signals, but spectral computation must rely on complex numerical linear algebra.

HOLO proves that through the QFT-structured quantum circuit, the feature space of graphs can be approximated. This conclusion relies on two key discoveries: first, an effective mapping can be constructed between the graph's adjacency matrix and quantum gates—by building controlled rotation gates corresponding to graph edges, the coupling structure of the circuit simulates local adjacency relationships on the graph; second, the hierarchical rotation logic in QFT naturally contains a multi-scale filtering structure, consistent with the decoupling capability of high-frequency and low-frequency components in the graph spectrum. When the depth of the quantum circuit is designed to be polynomial level, it is only necessary to trainably adjust the rotation angles and phases to approximate the eigenbasis of the Laplacian matrix.

To reduce the number of qubits, HOLO adopts a spectral approximation method based on logarithmic encoding, that is, representing the original N-dimensional feature space using n = log(N) qubits. The Hilbert space dimension constructed by this method is 2^n, theoretically capable of one-to-one mapping with the N-dimensional space.

In engineering implementation, the training of the quantum circuit is completed through classical-quantum hybrid optimization. The classical optimizer computes the gradients of the loss function with respect to circuit parameters and calculates the differentiability of the quantum circuit through the parameter shift rule. The quantum circuit extracts spectral features from high-dimensional input encoded signals and outputs low-dimensional features that can be further processed by classical networks. The entire system forms an end-to-end trainable hybrid GNN.

Large-scale graph learning has always been a difficult problem in the industrial field. Domains such as social media, traffic flow networks, and internet connectivity graphs each have tens of millions or even hundreds of millions of nodes. Classical GNNs typically require large amounts of video memory, long-duration matrix multiplications, complex sparse matrix management, and massive convolution filter parameters.

In contrast, quantum spectral filters provide a disruptive solution. As the number of nodes grows exponentially, the required qubits grow only logarithmically, making it a natural choice for future quantum-classical GNNs. Particularly in the current stage where quantum hardware is about to enter the medium-scale phase, this method with low qubit demand and high structural utilization offers excellent implementation possibilities.

HOLO believes that rather than waiting for the full maturation of quantum hardware, it is more important to build quantum frontier algorithm infrastructure in advance. This quantum spectral filter has established a complete research route, deeply integrating graph structures with quantum learnable models, laying an algorithmic foundation for future hardware development.

With the official release of HOLO's learnable quantum spectral filter for hybrid graph neural networks, the fusion of quantum computing and graph neural networks has taken a key step forward. HOLO not only demonstrates the enormous potential of quantum circuits in complex structure learning but also opens up a practical and scalable technical path for future quantum machine learning.

The successful implementation of this technology is driving graph neural networks toward a true quantum era. In the future, as quantum hardware gradually matures, such learnable quantum filters will become core components in numerous practical applications, constituting a brand-new cornerstone for the integrated development of graph computing, artificial intelligence, and physical computing.

About MicroCloud Hologram Inc.

MicroCloud Hologram Inc. (NASDAQ: HOLO) 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.'s goal is to become a global leading quantum holography and quantum computing technology company.

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.

FAQ

What is HOLO's learnable quantum spectral filter announced January 5, 2026?

A trainable quantum-circuit architecture that maps the graph Laplacian to perform spectral convolution and pooling, producing log(N)-dimensional compressed features.

How many qubits does HOLO say are needed to process a 1,000,000-node graph (HOLO)?

HOLO states the quantum circuit requires about 20 qubits to compress a graph with one million nodes.

How is the hybrid training performed for HOLO's quantum spectral filter (HOLO)?

Training uses classical-quantum hybrid optimization with classical gradient steps and the quantum parameter-shift rule for circuit differentiability.

What encoding methods does HOLO use to load graph signals into the quantum circuit (HOLO)?

HOLO uses amplitude encoding or probability encoding to load input graph signals into quantum states.

Does HOLO disclose financial resources to develop this quantum technology (HOLO)?

HOLO reports cash reserves exceeding 3 billion RMB and plans to invest more than 400 million USD in frontier technology development.

What limitations does HOLO note for practical adoption of its quantum GNN filter (HOLO)?

HOLO indicates practical use depends on the arrival of medium-scale quantum hardware and high structural utilization.