WiMi Develops Single-Qubit Quantum Neural Network Technology for Multi-Task Design

Rhea-AI Summary

WiMi (NASDAQ: WIMI) on October 20, 2025 announced development of a single-qudit quantum neural network (SQ-QNN) for multi-class/multi-task design that maps categories to dimensions of a high-dimensional qudit.

The release highlights three technical layers: quantum state encoding, unitary evolution via the Cayley transform of skew-symmetric matrices, and a hybrid quantum-classical training combining extended activation functions with SVM optimization. Claimed benefits include reduced circuit depth, lower training overhead, simplified feature steps, and improved representational efficiency for high-dimensional classification.

Insights

Quantum machine‑learning specialist

WiMi reports a novel single‑qudit QNN architecture claiming compact multi‑class classification via high‑dimensional unitary evolution.

The announcement describes a three‑part design: quantum state encoding into a d‑dimensional qudit, unitary evolution constructed via the Cayley transform of skew‑symmetric matrices, and a hybrid quantum‑classical training loop combining extended activation functions with an SVM optimization framework. The text claims this yields shallow circuits, reduced training overhead, and direct mapping from quantum states to category labels.

Key dependencies and risks include the absence of empirical benchmarks, hardware implementation details, and measured accuracy or resource comparisons; those factors determine practical viability. Watch for published experiments, circuit depth metrics, and an implementation date such as October 20, 2025 to validate claims within a short‑to‑medium term horizon.

Technology commercialization analyst

WiMi positions a research prototype as an efficiency pathway linking quantum computing and AI, but provides no performance or commercialization milestones.

The release frames the single‑qudit QNN as solving multi‑class scaling by mapping each class to a qudit dimension and using a Cayley‑based unitary to evolve states; hybrid training pairs extended activation expansions with SVM optimization. The statement explains architecture and training at a conceptual level without disclosing datasets, accuracy, hardware platform, or timeline to deployment.

For practical assessment, monitor demonstration data, hardware platform compatibility, and any peer‑reviewed publication or code release; absent those, treat the announcement as an early technical claim to verify over the next several quarters rather than immediate commercial impact.

BEIJING, Oct. 20, 2025 (GLOBE NEWSWIRE) -- BEIJING, Oct. 20, 2025––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the development of single-qubit quantum neural network technology for multi-task design. This technology has extremely disruptive significance; this technology, by demonstrating the feasibility of high-dimensional quantum systems in efficient learning, provides a realistic path for the deep integration of future quantum computing and artificial intelligence.
Nowadays, training large neural networks often requires billions of parameters and massive data center resources, and the sharp rise in power consumption and hardware costs has become a real bottleneck in the development of artificial intelligence. At the same time, although traditional neural networks have achieved high accuracy in multi-class classification problems, as the number of categories increases, the model structure also expands accordingly, leading to a decline in inference latency and computational efficiency.
The rise of quantum computing provides new possibilities for this dilemma. Quantum bits (qubits) and quantum multi-level systems (qudits) can utilize superposition and entanglement to achieve natural representation of high-dimensional data spaces, thereby breaking the resource limitations of classical computing. In this field, Quantum Neural Networks (QNN) have become a frontier direction of research. Compared to traditional deep learning, QNN can achieve complex mappings through shallow quantum circuits, greatly improving model compactness and computational efficiency.
In the wave of quantum machine learning, the single-qudit quantum neural network technology proposed by WiMi not only meets the actual needs of high-dimensional data classification but also breaks through the implementation bottlenecks under the constraints of quantum hardware, becoming an important step in promoting industrial progress.
The core idea of the single-qudit quantum neural network technology proposed by WiMi is to use the state space of a single high-dimensional qudit to directly handle multi-class classification tasks. Unlike classical neural networks that rely on thousands of neurons and complex hierarchical structures, SQ-QNN leverages the high-dimensional characteristics of quantum systems to efficiently encode and distinguish category information within a compact circuit scale.
In this design, each category corresponds to one dimension of the quantum system, and the overall classification process is completed through the action of a high-dimensional unitary operator. WiMi uses the Cayley transform of skew-symmetric matrices to construct the unitary operator; this method not only possesses good mathematical stability but also ensures efficiency in quantum circuit implementation. In this way, the evolution of the quantum state directly establishes a mapping relationship with the category labels, greatly reducing the circuit depth and training overhead.
Additionally, this technology introduces a hybrid training method when optimizing network parameters. It combines extended activation functions with the optimization framework of Support Vector Machines (SVM). The extended activation function originates from the truncated multivariate Taylor series expansion and can effectively introduce nonlinear representational capabilities in the quantum state space, while SVM optimization further ensures the stability of parameter optimization and the acquisition of global optimal solutions.
The entire technical logic of WiMi's SQ-QNN can be divided into three levels: quantum state encoding, unitary evolution design, and hybrid training optimization.
First is the quantum state encoding. In multi-class classification problems, assuming the number of categories is $d$, a $d$-dimensional qudit system is constructed to carry the data. After appropriate data preprocessing, the input samples are mapped to the amplitude or phase information of the quantum state. In this process, traditional feature extraction steps are greatly simplified, allowing data to directly enter the neural network in quantum form.
Second is the unitary evolution design. WiMi proposes using the Cayley transform of skew-symmetric matrices to generate $d$-dimensional unitary operators. The properties of skew-symmetric matrices make their Cayley transform results naturally satisfy unitarity, thereby ensuring the physical rationality and implementability of quantum state evolution. Through this unitary operator, the input quantum state completes the mapping and differentiation of category information in the high-dimensional Hilbert space. Unlike the multi-layer propagation in classical neural networks, this scheme can achieve complex decision boundaries through a single-step evolution, significantly reducing the circuit depth.
Finally, it is the hybrid training optimization. In the parameter training phase, this scheme does not solely rely on quantum computing but adopts a hybrid quantum-classical training method. The introduction of extended activation functions enables the quantum neural network to possess nonlinear classification capabilities while maintaining a shallow structure. At the same time, the support vector machine optimization mechanism provides an efficient path for parameter search, allowing the network to quickly converge to the global optimal solution. Under this training framework, the burden on quantum hardware is effectively shared, and training efficiency is significantly improved.

About WiMi Hologram Cloud Inc.
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) focuses on holographic cloud services, primarily concentrating on professional fields such as in-vehicle AR holographic HUD, 3D holographic pulse LiDAR, head-mounted light field holographic devices, holographic semiconductors, holographic cloud software, holographic car navigation, metaverse holographic AR/VR devices, and metaverse holographic cloud software. It covers multiple aspects of holographic AR technologies, including in-vehicle holographic AR technology, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR virtual advertising technology, holographic AR virtual entertainment technology, holographic ARSDK payment, interactive holographic virtual communication, metaverse holographic AR technology, and metaverse virtual cloud services. WiMi is a comprehensive holographic cloud technology solution provider. For more information, please visit http://ir.wimiar.com.
Translation Disclaimer
The original version of this announcement is the officially authorized and only legally binding version. If there are any inconsistencies or differences in meaning between the Chinese translation and the original version, the original version shall prevail. WiMi Hologram Cloud Inc. and related institutions and individuals make no guarantees regarding the translated version and assume no responsibility for any direct or indirect losses caused by translation inaccuracies.
Investor Inquiries, please contact:
WIMI Hologram Cloud Inc.
Email: pr@wimiar.com

ICR, LLC
Robin Yang
Tel: +1 (646) 975-9495
Email: wimi@icrinc.com

FAQ

What did WiMi announce about quantum neural networks on October 20, 2025 (WIMI)?

WiMi announced a single-qudit quantum neural network approach for multi-class tasks using Cayley-transformed unitary operators and hybrid SVM-based training.

How does WiMi's SQ-QNN reduce quantum circuit depth for WIMI technology?

The design maps categories to qudit dimensions and uses a single-step unitary evolution, which the company says reduces circuit depth versus multi-layer classical nets.

What training method did WiMi describe for the WIMI SQ-QNN?

WiMi described a hybrid quantum-classical training that uses extended activation functions and an SVM optimization framework to improve convergence stability.

What role does the Cayley transform play in WiMi's October 20, 2025 WIMI announcement?

WiMi uses the Cayley transform of skew-symmetric matrices to construct d-dimensional unitary operators that ensure mathematical stability and implementability.

Will WiMi's SQ-QNN announcement on October 20, 2025 (WIMI) immediately change product offerings?

The announcement describes a technical development and implementation approach; it does not state immediate commercial product launches or revenue effects.

How might WiMi's SQ-QNN affect AI hardware requirements for WIMI?

WiMi claims the SQ-QNN can encode high-dimensional data within a compact circuit, potentially reducing training overhead and hardware depth compared with large classical networks.