Wimi Hologram Cloud Inc. - WIMI STOCK NEWS

WiMi Hologram Cloud (NASDAQ: WIMI) has announced the development of a holographic quantum algorithm for simulating correlated spin systems. The technology enables efficient ground state preparation and dynamic evolution while reducing quantum bit resources significantly.

The algorithm features three key innovations: a Quantum Bit Reuse Strategy that requires only (D-1) quantum bits for D-dimensional systems, a Holographic Variational Quantum Eigensolver (holoVQE) for ground state preparation, and an Efficient Time Evolution Implementation for studying thermalization dynamics.

The technology has been successfully tested on ion-trap quantum computers, simulating antiferromagnetic Heisenberg chains using just two pairs of quantum bits. The algorithm leverages matrix product states (MPS) and quantum channels, making the required quantum bits proportional to the logarithm of entanglement entropy.

WiMi Hologram Cloud (NASDAQ: WIMI) has announced the development of an FPGA-based digital quantum computer verification technology. The innovation treats quantum bits (qubits) as discrete entities called 'digital qubits,' which operate through specific digital quantum gates similar to classical computing logic gates.

The company has designed a prototype implementing digital qubits on FPGA using Hardware Description Language (HDL), with quantum states discretized into different digital states. The technology enables simulation and validation of quantum computing behavior within classical computing architecture, significantly reducing hardware implementation complexity and costs.

The system includes a verification platform that allows real-time monitoring of digital qubits and interaction with classical computing systems. WiMi's solution leverages FPGA's dynamic reconfiguration capabilities and optimizes resource utilization through timing optimization and parallel processing, making quantum computing more accessible and practical for various applications.

WiMi Hologram Cloud (NASDAQ: WIMI) announced the development of AI applications based on the DeepSeek open-source model. The initiative leverages DeepSeek's R1 and V3 models, which have achieved leading performance in mathematics, code generation, and natural language reasoning.

The company plans to integrate DeepSeek's API interfaces and algorithms to implement various functions including natural language processing and mathematical reasoning. WiMi will develop intelligent programming tools that offer features like automatic code completion, quality analysis, and optimization suggestions.

This strategic investment aims to enhance WiMi's service offerings across multiple industry sectors, focusing on improving work efficiency and reducing costs through AI-powered solutions. The company commits to continued investment in AI research and maintaining partnerships with AI companies to promote widespread adoption of AI technologies.

WiMi Hologram Cloud (NASDAQ: WIMI) has announced the development of a binary string polynomial encoding for Quantum Random Access Memory (QRAM). This breakthrough technology enhances quantum computing efficiency through significant improvements in key metrics:

The new QRAM architecture achieves an exponential improvement in T-depth through polynomial encoding of binary strings, compared to traditional bucket brigade architectures where T-depth grows linearly. The design maintains optimal T-count efficiency while reducing T-depth, ensuring efficient resource utilization. Additionally, WiMi's design maintains qubit count while optimizing other computational resources.

The technology introduces a quantum Look-Up Table (qLUT) concept, which, despite being read-only, provides rapid data access at lower computational costs for specific applications. This advancement is expected to impact various fields, including chemical molecular simulations, financial market predictions, cryptography decryption, and artificial intelligence.

WiMi Hologram Cloud (NASDAQ: WIMI) has announced the development of a hybrid CPU-FPGA quantum AI simulator designed to simulate quantum computer behavior and optimize quantum algorithms on classical computers. The simulator combines CPU versatility with FPGA parallel processing capabilities through a dual-component framework:

The CPU component handles high-level logic and complex algorithmic tasks, while the FPGA component executes parallel computing tasks like quantum state simulation and gate operations. The system features a three-layer structure: hardware layer (high-performance CPUs and FPGA chips), software layer (quantum algorithms and AI model simulation framework), and interface layer (ensuring compatibility with existing platforms).

The simulator employs software-hardware co-design optimization and aims to provide businesses and research institutions with tools for exploring quantum algorithms and developing AI applications.

WiMi Hologram Cloud (NASDAQ: WIMI) has announced the development of an innovative FPGA-based digital quantum coprocessor featuring both homogeneous and heterogeneous architectures. This technology aims to overcome limitations of traditional quantum hardware by using FPGA's digital logic to simulate qubit behavior, offering improved stability and scalability compared to conventional quantum accelerators.

The solution implements quantum computing functions through carefully designed architectures: the homogeneous design processes all qubits uniformly, simplifying system management, while the heterogeneous architecture allows different types of qubits to coexist for varied computational needs. The technology utilizes an IP core generator and VHDL programming to create reusable quantum computing elements and control FPGA hardware behavior.

The development represents a significant advancement in quantum computing, potentially impacting various industries by solving complex problems traditional computers struggle with.

WiMi Hologram Cloud (NASDAQ: WIMI) has developed a Quantum Technology-Based Random Access Memory Architecture (QRAM). The architecture implements fundamental logical operations using quantum gates like CNOT, V, and V+ gates. QRAM's key features include:

- Utilization of quantum superposition and entanglement for parallel processing
- Quantum error correction mechanisms to preserve data integrity
- Seamless integration with quantum processing units
- Near real-time data read/write capabilities

The technology aims to enhance quantum computing performance, particularly in applications like molecular simulations, climate modeling, and quantum machine learning. QRAM's architecture enables efficient parallel data access while maintaining quantum system stability.

WiMi Hologram Cloud (NASDAQ: WIMI) has developed Machine Learning-based Quantum Error Suppression Technology (MLQES), addressing error challenges in quantum computing without requiring additional quantum resources. The technology uses supervised learning models to predict potential errors in quantum circuits and implements a circuit segmentation mechanism when errors exceed predetermined thresholds.

MLQES combines classical and quantum computing capabilities by splitting large quantum circuits into smaller sub-circuits when necessary, controlling errors within acceptable ranges. The system processes results on classical computers using reconstruction algorithms, making quantum computing more efficient particularly for NISQ (noisy intermediate-scale quantum) devices.

This innovation offers a scalable framework that reduces reliance on quantum error-correcting codes while enhancing computational efficiency, particularly beneficial for applications in quantum chemistry, optimization problems, and cryptography.

WiMi Hologram Cloud (NASDAQ: WIMI) announced the development of the Holographic Quantum Linear Solver (HQLS), a new quantum algorithm for solving linear systems of equations. The HQLS combines Variational Quantum Algorithms (VQA) with the classical shadow framework to overcome hardware resource limitations of traditional quantum solvers.

The algorithm reduces quantum bit requirements and gate complexity compared to traditional methods like the HHL algorithm, making it more suitable for current noisy intermediate-scale quantum (NISQ) computers. HQLS operates through a process of initialization, parameterized quantum circuit design, iterative optimization, shadow framework approximation, and convergence checking.

The technology shows promise for applications in climate modeling, quantum chemistry, machine learning, and financial modeling, with potential for integration with other quantum algorithms for complex problem-solving.