HOLO MicroCloud Hologram Inc

MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, announced the proposal of a new method based on Matrix Product States (MPS) that enables high-precision quantum state preparation with a mirror-symmetric probability distribution. This research not only reduces the entanglement of the probability distribution but also significantly improves the accuracy of the matrix product state approximation, resulting in a computational efficiency increase by two orders of magnitude.

This new technology adopts a shallow quantum circuit design, primarily composed of nearest-neighbor qubit gates, and features linear scalability with respect to the number of qubits, greatly enhancing its feasibility on current noisy quantum devices. Furthermore, the study found that in tensor networks, approximation accuracy mainly depends on the bond dimension, with minimal dependence on the number of qubits, laying the foundation for future large-scale adoption. This research not only provides innovative optimization methods in theory but also demonstrates superior precision in experimental tests, foreshadowing broad prospects for quantum computing in practical applications.

Probability distributions play a critical role in quantum computing. Many quantum algorithms rely on the efficient loading of probability distributions, such as quantum Monte Carlo methods, quantum financial modeling, and quantum machine learning. However, traditional methods for loading probability distributions often face high levels of entanglement, causing the depth of quantum circuits to increase rapidly. This leads to reduced computational efficiency and heightened susceptibility to quantum noise.

HOLO constructs quantum states based on Matrix Product States (MPS) and leverages mirror symmetry to optimize the loading of probability distributions. Mirror symmetry implies that the probability distribution can, to some extent, reduce redundant information through symmetric transformations, thereby lowering the system's entanglement. This optimization approach enables more efficient quantum state preparation in shallow quantum circuits, making it particularly suitable for current Noisy Intermediate-Scale Quantum (NISQ) computers.

MPS is a tensor network model commonly used in quantum information and computation. It represents high-dimensional probability distributions in a low-rank decomposed form, thus reducing computational complexity. By exploiting mirror symmetry, this study successfully reduced redundant parameters, improving the approximation accuracy of MPS by two orders of magnitude. This means that, under the same computational resource constraints, this method can load probability distributions more accurately than existing MPS approaches, thereby enhancing the overall performance of quantum algorithms.

Another key advantage of HOLO’s method lies in its optimized shallow quantum circuit design. Traditional quantum state preparation methods typically require deep quantum circuits involving a large number of global gate operations, which lead to noise accumulation and pose significant challenges for current NISQ devices.

This study employs a novel quantum circuit design primarily composed of nearest-neighbor qubit gates. This design approach offers the following advantages:

Reduced Circuit Depth: By minimizing global gate operations, it avoids complex non-local entanglement operations, making the circuit easier to implement on current quantum hardware.Improved Computational Stability: Since errors in noisy quantum devices increase with circuit depth, using shallower circuits reduces error accumulation and enhances computational accuracy.Linear Scalability: The computational complexity of this method grows linearly with the number of qubits, enabling the technology to adapt to larger-scale quantum systems.

Under the same hardware conditions, this method achieves a precision improvement of two orders of magnitude compared to existing matrix product state-based quantum state preparation techniques, while significantly reducing computation time. This lays a foundation for large-scale quantum computing applications.

The core idea of using MPS for quantum state preparation is to represent high-dimensional probability distributions as low-rank tensor decompositions, thereby reducing computational load and optimizing storage structures.

Low Entanglement Representation: Since the entanglement of quantum states determines computational difficulty, the MPS method reduces computational complexity through low-rank approximations, making quantum states easier to implement on quantum hardware.Suitable for High-Dimensional Probability Distributions: The MPS method is particularly well-suited for compressing and storing high-dimensional probability distributions, making it an ideal tool for fields such as quantum finance and quantum machine learning.Controllable Computational Complexity: Compared to traditional global quantum state preparation methods, the MPS approach can control computational complexity while maintaining high computational accuracy across different qubit scales.

However, the HOLO method still faces some challenges. For instance, the accuracy of MPS depends to some extent on the bond dimension, and an increase in bond dimension introduces additional computational costs. Therefore, in practical applications, it is necessary to balance computational accuracy and resource demands to achieve optimal performance. Additionally, different quantum hardware architectures may impact the implementation of the MPS method. Future research could further optimize the implementation of MPS to make it adaptable to a wider range of quantum computing platforms.

The quantum state preparation method proposed by HOLO, based on matrix product states with mirror-symmetric probability distributions, achieves a computational accuracy two orders of magnitude higher than existing methods by reducing entanglement, optimizing shallow quantum circuit designs, and enhancing the approximation accuracy of MPS. This breakthrough not only provides a more feasible quantum state preparation solution for current NISQ devices but also lays the groundwork for future large-scale quantum computing applications.

Future research directions include further optimizing the computational complexity of matrix product states, improving their adaptability across different quantum hardware platforms, and exploring additional potential application areas. Moreover, as quantum computing hardware continues to advance, this method is expected to demonstrate even greater computational capabilities on real quantum devices, driving quantum computing toward a new stage of practical utility.

About MicroCloud Hologram Inc.

MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud’s holographic technology services include high-precision holographic light detection and ranging (“LiDAR”) solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems (“ADAS”). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud’s holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud’s holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit http://ir.mcholo.com/

Safe Harbor Statement

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ContactsMicroCloud Hologram Inc.Email: IR@mcvrar.com