WiMi Has Developed a Scalable Quantum Neural Network (SQNN) Technology Based on Multi-quantum-device Collaborative Computing
WiMi Hologram Cloud (NASDAQ: WIMI) has announced the development of a Scalable Quantum Neural Network (SQNN) technology that leverages multi-quantum-device collaborative computing. The system consists of three key components: quantum feature extractors for local data processing, classical communication channels for information transfer, and a quantum predictor for final classification tasks.
The SQNN technology aims to overcome current quantum computing hardware limitations by enabling multiple small quantum devices to work together, achieving classification accuracy comparable to traditional Quantum Neural Networks. The system's architecture allows for improved data utilization, enhanced computational scale, and optimized computing resources through its modular approach.
- Innovative solution enabling multiple small quantum devices to work collaboratively
- Achieves classification accuracy comparable to traditional Quantum Neural Networks
- Improved data utilization and computational efficiency through parallel processing
- Scalable architecture that can adapt to different quantum device sizes
- Enhanced resource optimization through flexible allocation of quantum devices
- Challenges remain in optimizing quantum device interconnection
- Need for further refinement to reduce noise interference
- Requires complex communication infrastructure between quantum devices
- Limited by current quantum hardware capabilities
Insights
WiMi's SQNN technology represents a clever workaround for current quantum computing limitations, though practical implementation remains speculative.
WiMi's announced Scalable Quantum Neural Network (SQNN) technology addresses a fundamental challenge in quantum computing: the limited number of qubits in today's quantum processors. By distributing computation across multiple smaller quantum devices that act as feature extractors, with results combined via classical communication channels, they've proposed a pragmatic architecture for handling larger datasets than would be possible on a single quantum device.
The approach is conceptually similar to ensemble methods in classical machine learning, where multiple simpler models collaborate to solve complex problems. Their architecture consists of three main components: quantum feature extractors (using Variational Quantum Circuits), classical communication channels, and a quantum predictor that makes final classifications.
What's technically noteworthy is their implementation of a distributed quantum system that doesn't require quantum entanglement between separate devices—a capability beyond current technology. Instead, they're using classical communications between quantum processors, significantly lowering the technical barriers.
The claimed advantages—improved data utilization, enhanced computational scale, and optimized resource allocation—are theoretically sound. However, the press release lacks specifics on performance metrics, error rates, or hardware specifications used in their experiments. The mention of "experiments conducted on multiple benchmark datasets" without naming these datasets or quantifying "comparable accuracy" raises questions about the technology's current maturity.
This represents an interesting architectural approach to quantum machine learning rather than a fundamental breakthrough in quantum computing itself. While the distributed computing model shows promise, the real-world performance will depend heavily on the quality of quantum-classical interfaces and communication overhead, factors not addressed in detail.
This technology aims to overcome the limitations of current quantum computing hardware by enabling multiple small quantum devices to work collaboratively, thereby building an efficient and scalable quantum neural network system. The technology not only achieves classification accuracy comparable to traditional Quantum Neural Networks (QNN) in theory but also introduces a novel approach to optimizing the utilization of quantum computing resources and enhancing data efficiency.
The core architecture of WiMi's SQNN system consists of three main components:
Quantum Feature Extractor: The quantum feature extractor is responsible for extracting local features from input data. Each quantum device can independently perform feature extraction tasks using Variational Quantum Circuits (VQC) to encode and transform input data. Since these devices operate independently, they can flexibly adapt to quantum devices of different sizes. For instance, larger quantum devices can handle more complex data patterns, while smaller quantum devices can process simpler local features.
Classical Communication Channel: In the SQNN framework, quantum feature extractors transmit the extracted local features to a central computing node via a classical communication channel. This communication process is similar to the concept of Federated Learning, where different computing units process data independently, but the final decision-making process relies on the integration of global information.
Quantum Predictor: The quantum predictor serves as the core computational unit of the entire SQNN system. It receives feature information from multiple quantum feature extractors and performs the final classification decision using quantum circuits. The quantum predictor can employ more complex quantum circuits to optimize classification accuracy and dynamically adjust its computational approach based on the scale of the data.
The technical implementation of WiMi's SQNN involves the following steps: Frist, data Preprocessing and Quantum Encoding: Before entering the quantum system, input data undergoes classical preprocessing operations such as standardization and dimensionality reduction. The data is then mapped to quantum states using encoding methods such as Amplitude Encoding or Angle Encoding. Then, sub-feature Extraction: Each quantum device performs independent feature extraction tasks using Parameterized Quantum Circuits (PQC) to transform features and generate local feature representations. Besides, feature Aggregation and Classification: The output of quantum feature extractors is transmitted to a central node via a classical communication channel. The quantum predictor then aggregates the features and performs the final classification task. Finally, parameter Optimization and Training: SQNN employs Variational Quantum Optimization for training. A classical optimizer, such as gradient descent, is used to adjust the quantum circuit parameters to minimize classification error.
Compared to traditional QNNs, WiMi's SQNN offers the following significant advantages:
Improved Data Utilization: Since SQNN leverages multiple quantum devices for collaborative computing, it can utilize data more efficiently without compromising data integrity due to the qubit limitations of a single device.
Enhanced Computational Scale: By coordinating multiple small quantum devices, SQNN can handle larger-scale computational tasks without relying on a single high-performance quantum computer. This modular approach also makes SQNN more scalable.
Optimized Computing Resources: SQNN allows different types of quantum devices to work together, enabling more flexible resource allocation. For example, when the workload is small, only a subset of quantum feature extractors can be activated, whereas for large-scale computing tasks, more quantum devices can be utilized to improve computational efficiency.
Experiments conducted on multiple benchmark datasets demonstrate that WiMi's SQNN achieves classification accuracy comparable to traditional QNNs at the same scale. Additionally, since SQNN utilizes multiple quantum devices for parallel computing, its training efficiency is significantly improved compared to QNNs that rely on a single quantum device.
Furthermore, experimental results show that as the number of participating quantum devices increases, both the classification accuracy and computation speed of SQNN improve significantly. This indicates that the approach has strong scalability as quantum computing hardware continues to evolve.
Despite the promising experimental results achieved under current hardware conditions, several key challenges remain to be addressed. For example, optimizing the interconnection of quantum devices to enhance efficiency while minimizing communication costs, and further refining SQNN's quantum circuit design to reduce noise interference and improve computational accuracy.
WiMi's Scalable Quantum Neural Network (SQNN) provides an innovative solution for quantum machine learning, enabling multiple small quantum devices to work collaboratively for efficient classification tasks. Experimental results indicate that SQNN offers strong computational performance and scalability, laying a solid foundation for the integration of quantum computing and artificial intelligence. As quantum hardware continues to advance, SQNN is expected to become a crucial component of large-scale quantum machine learning systems, driving revolutionary changes in AI and data science.
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.
View original content:https://www.prnewswire.com/news-releases/wimi-has-developed-a-scalable-quantum-neural-network-sqnn-technology-based-on-multi-quantum-device-collaborative-computing-302561599.html
SOURCE WiMi Hologram Cloud Inc.
FAQ
What is WiMi's new Scalable Quantum Neural Network (SQNN) technology?
What are the main components of WiMi's SQNN system?
How does WIMI's SQNN technology improve upon traditional Quantum Neural Networks?
What are the main challenges facing WiMi's SQNN technology?
How does the SQNN technology impact WiMi's position in quantum computing?
