WiMi Developed a Quantum Computing-Based Feedforward Neural Network (QFNN) Algorithm

Rhea-AI Summary

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) has announced the development of a Quantum Computing-Based Feedforward Neural Network (QFNN) algorithm to address computational bottlenecks in traditional neural network training.

The algorithm's core innovations include:

• Quantum Feedforward Propagation using quantum state superposition
• Enhanced Quantum Backpropagation utilizing Quantum Fourier Transform
• Efficient Quantum Random Access Memory (QRAM) for intermediate value storage

The QFNN reduces computational complexity from O(M) to O(N), where M represents connections and N represents neurons. This breakthrough offers at least quadratic speedup in training time and demonstrates natural resistance to overfitting through quantum state uncertainty. The technology shows promise in financial analysis, autonomous driving, biomedical research, and quantum computer vision applications.

Positive

• Development of breakthrough QFNN algorithm with significant computational advantages
• Achievement of at least quadratic speedup in neural network training
• Natural overfitting prevention capability through quantum uncertainty
• Broad application potential in high-value markets

Negative

• Technology requires quantum computers which are not yet widely available
• Quantum-inspired classical implementations face quadratic computational overhead

Insights

Quantum Computing Technology Expert

WiMi's quantum neural network algorithm shows theoretical promise but faces significant implementation challenges with today's quantum hardware capabilities.

WiMi's announcement of a Quantum Feedforward Neural Network (QFNN) algorithm represents an interesting theoretical advancement in quantum machine learning. The described approach leverages three key quantum computing principles: quantum state superposition for matrix operations, quantum backpropagation using Quantum Fourier Transform, and Quantum Random Access Memory (QRAM) for efficient data storage and retrieval.

The claimed computational advantage—reducing complexity from O(M) to O(N) where M represents connections and N represents neurons—would indeed provide significant acceleration for large neural networks where connections vastly outnumber neurons. This quadratic speedup aligns with known quantum advantages in specific computational domains.

Particularly interesting is the assertion about inherent regularization through quantum uncertainty. Classical neural networks require explicit regularization techniques to prevent overfitting, so a natural regularization effect would be valuable if demonstrable.

However, several significant technical hurdles remain unaddressed. Current quantum hardware faces severe limitations in qubit coherence time, gate fidelity, and error rates. The described QRAM implementation requires particularly advanced quantum capabilities not yet available in commercial systems. The article mentions quantum-inspired classical algorithms as a transitional solution, acknowledging that fully quantum implementations may not be immediately practical.

While theoretically sound, the practical implementation timeline for such algorithms depends heavily on quantum hardware advancement. Most quantum algorithms today operate in simulation environments or on very qubit systems, making large-scale neural network implementation challenging.

Technology Investment Analyst

WiMi's quantum algorithm announcement positions it in emerging quantum AI market with uncertain commercialization timeline and no specified business impact.

WiMi's development of a quantum computing neural network algorithm represents strategic positioning in the emerging quantum AI sector. The company appears to be diversifying beyond its core holographic AR business into quantum computing applications, potentially opening new market opportunities in financial analysis, autonomous driving, biomedical research, and computer vision as specified in the announcement.

The market context is important here: quantum computing represents a nascent industry with significant future potential but current commercial applications. Major technology companies and specialized startups are racing to establish intellectual property and capabilities in this space, anticipating the eventual transition from theoretical to practical quantum advantage.

What's notably absent from this announcement is any discussion of commercialization timeline, specific customer engagements, or expected financial impact. This suggests the technology remains in research stage rather than product development. The mention of "quantum-inspired classical algorithms" indicates awareness that true quantum implementations may face hardware limitations in the near term.

For a company with WiMi's market capitalization ($39.87 million), investment in quantum computing research represents a significant commitment of resources. Without clear monetization pathways, this should be viewed as long-term R&D rather than near-term revenue generation.

This announcement aligns with broader technology industry trends toward quantum computing exploration but provides insufficient information to assess potential return on investment or timeline to market. While positioning in quantum ML could eventually yield competitive advantages, the path from algorithm development to commercial application remains lengthy and uncertain.

Beijing, April 23, 2025 (GLOBE NEWSWIRE) -- WiMi Developed a Quantum Computing-Based Feedforward Neural Network (QFNN) Algorithm

BEIJING, Apr. 23, 2025––WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced the development of a Quantum Computing-Based Feedforward Neural Network (QFNN) algorithm aimed at overcoming computational bottlenecks in traditional neural network training. The core innovation of this algorithm lies in efficiently approximating the inner product between vectors while utilizing Quantum Random Access Memory (QRAM) to store intermediate computational values, enabling rapid retrieval.
WiMi's QFNN training algorithm relies on several key quantum computing subroutines, with the most critical components being the quantized feedforward and backpropagation processes. In classical neural networks, feedforward propagation is used to compute the activation values of input data, while backpropagation adjusts weights to minimize the loss function. WiMi's quantum algorithm provides exponential speedup in both stages, enabling neural networks to achieve convergence in significantly less time.
Quantum Feedforward Propagation: Classical feedforward propagation involves multiple matrix-vector multiplications. WiMi's quantum algorithm leverages quantum state superposition and coherence to perform these operations. Specifically, it encodes neuron weights and input data in quantum coherent states and completes matrix-vector operations through the evolution of quantum states. This approach can perform computations in logarithmic time, greatly reducing the computational load.
Quantum Backpropagation: In neural network training, error backpropagation (BP) is the most critical component. The BP algorithm involves computing the gradient of the loss function and propagating it back to earlier layers of the network to update weights. WiMi's quantum algorithm leverages quantum coherent states to compute gradients and accelerates gradient calculations using the Quantum Fourier Transform (QFT), enabling gradient updates that are quadratically faster than traditional methods.
Quantum Random Access Memory (QRAM): In classical neural network training, each weight update requires accessing and storing a large number of intermediate computation results. QRAM allows these intermediate results to be stored in quantum states and retrieved efficiently for subsequent calculations. The advantage of QRAM lies in its ability to avoid redundant computations and provide exponential speedup.
A core advantage of WiMi's quantum algorithm is its reduced computational complexity. The computational complexity of classical neural networks typically depends on the number of connections between neurons, whereas our quantum algorithm depends only on the number of neurons. This means that for a network with N neurons and M connections, the computational complexity of classical algorithms is typically O(M), while our quantum algorithm reduces it to O(N).
More intuitively, in large-scale neural networks, the number of connections often far exceeds the number of neurons, so this quantum algorithm achieves at least a quadratic speedup. This breakthrough has significant implications for training deep learning models, particularly when handling ultra-large-scale datasets, as it can substantially reduce training time.
Overfitting is a common issue in deep learning, where a model performs well on training data but generalizes poorly on test data. WiMi has discovered that quantum algorithms naturally exhibit inherent resilience to overfitting during training. This is due to the intrinsic uncertainty of quantum computing, which makes the training process resemble regularization techniques used in classical deep learning.
In WiMi's quantum algorithm, the superposition and coherence of quantum states introduce a degree of noise in each computation's results. While this noise is typically considered an error in classical computing, in the context of machine learning, it acts like a random perturbation that prevents the model from overfitting to the training data. As a result, this quantum neural network can naturally achieve better generalization without requiring additional regularization techniques.
WiMi's Quantum Feedforward Neural Network (QFNN) holds broad application prospects, particularly in scenarios with extremely high demands for computational speed and data scale, such as financial market analysis, autonomous driving, biomedical research, and quantum computer vision. Beyond direct applications on quantum computers, WiMi's research also lays the foundation for quantum-inspired classical algorithms. These classical algorithms draw on the design principles of QFNN and achieve similar computational complexity optimizations on traditional computers. Although these quantum-inspired classical algorithms incur an additional quadratic computational overhead compared to true quantum algorithms, they provide a transitional solution for the current era where quantum computers are not yet widely available, enabling businesses to experience the advantages of quantum algorithms in advance.
Quantum computing is reshaping the future of machine learning, and WiMi's QFNN quantum algorithm is a significant milestone in this trend. By efficiently leveraging the advantages of quantum computing, it not only accelerates neural network training but also enhances generalization capabilities, opening new directions for the development of deep learning. With the continuous advancement of quantum hardware, there is reason to believe that quantum neural networks will become a critical component of the machine learning field in the coming years, ushering artificial intelligence into a new era of computation.

About WiMi Hologram Cloud

WiMi Hologram Cloud, Inc. (NASDAQ:WiMi) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.

Safe Harbor Statements

This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services.

Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.

Contacts
WiMi Hologram Cloud Inc.
Email: pr@WiMiar.com

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

FAQ

What is the key innovation in WIMI's new Quantum Computing-Based Neural Network algorithm?

WIMI's QFNN algorithm uses quantum state superposition, enhanced backpropagation with Quantum Fourier Transform, and QRAM for efficient data storage, reducing computational complexity from O(M) to O(N).

How does WIMI's QFNN algorithm prevent overfitting in neural networks?

The algorithm's quantum state uncertainty introduces natural noise during computations, acting as a built-in regularization technique without requiring additional measures.

What are the main applications for WIMI's Quantum Feedforward Neural Network?

The QFNN is designed for high-computational scenarios including financial market analysis, autonomous driving, biomedical research, and quantum computer vision.

How much faster is WIMI's quantum algorithm compared to traditional neural networks?

The algorithm achieves at least a quadratic speedup compared to classical neural networks, particularly effective when handling ultra-large-scale datasets.