SHENZHEN, China, March 4, 2026 /PRNewswire/ — MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released a core quantum machine learning technology oriented toward sequential learning tasks—the Quantum Recurrent Neural Network (QRNN) for sequential learning. This technology revolves around the hardware-efficient construction of the Quantum Recurrent Block (QRB), and through an interleaved stacking network design paradigm, it systematically addresses the engineering bottleneck that makes quantum recurrent models difficult to run on noisy intermediate-scale quantum devices (NISQ), marking a key step forward in the standardization and deployability of quantum deep learning models.

For a long time, quantum neural networks have been widely regarded as an important bridge connecting quantum computing and artificial intelligence. Compared with classical neural networks, quantum neural networks can utilize quantum superposition, entanglement, and high-dimensional Hilbert spaces to express more complex function structures under constrained parameter scales. However, in the field of sequential modeling, although recurrent neural networks have become classical architectures in tasks such as natural language processing, time series prediction, and signal analysis, how to effectively map core mechanisms such as "recurrence," "memory," and "temporal dependency" into the quantum computing framework has always lacked a unified, reproducible, and hardware-friendly solution. This status quo has, to a large extent, constrained the application of quantum machine learning in real-world sequential data scenarios.

HOLO, precisely based on this industry pain point, has systematically re-examined the construction logic of quantum recurrent neural networks. HOLO researchers point out that existing partial quantum recurrent models either overly rely on idealized assumptions about quantum operations or are difficult to adapt to current quantum hardware in terms of circuit depth and entanglement structure, resulting in good performance in simulation but difficulty in running on real devices. To address this, the team, starting from the three engineering principles of "modularity," "repeatability," and "low coherent time consumption," proposed redefining the construction of quantum recurrent networks by using the Quantum Recurrent Block (QRB) as the basic unit.

The Quantum Recurrent Block is one of the core innovations of this technology. Unlike the holistic variational circuits commonly seen in traditional quantum neural networks, QRB is designed as a highly structured, parameter-controlled quantum subcircuit module for characterizing the information update process at a single time step in a sequence. Each QRB adopts a hardware-efficient gate set structure in its physical implementation, fully taking into account the limitations of current mainstream superconducting and ion-trap quantum computing platforms regarding the number of two-qubit gates, connectivity topology, and noise characteristics. In this way, while maintaining sufficient expressive power, QRB avoids unnecessary deep entanglement operations, thereby reducing dependence on qubit coherence time from the source.

In terms of information flow mechanism, this QRNN model draws on the concept of hidden states in classical recurrent neural networks but does not simply perform a one-to-one mapping. Instead, HOLO leverages the natural advantage of quantum states as hidden states, encoding historical information into the amplitude and phase structure of quantum states, and realizing state updates through parameterized quantum operations within the QRB. After quantum encoding, the input data of the current time step interacts with the quantum hidden state retained from the previous time step within the QRB, thereby modeling temporal dependency relationships. This process is mathematically equivalent to a form of quantum state evolution, but in engineering implementation, it is strictly constrained within the operational complexity range that NISQ devices can tolerate.

To further reduce the overall circuit depth, HOLO adopted a network structure design of interleaved stacking of quantum recurrent blocks. Unlike the layer-by-layer stacking approach in traditional deep neural networks, QRNN alternately reuses QRB across the time dimension and feature dimension, allowing the same quantum circuit structure to be reused across multiple time steps. As a result, this not only significantly reduces the number of quantum gates that need to be actually executed but also avoids the problem of circuit depth growing linearly with the number of time steps. This design is particularly critical for NISQ devices, as coherence time is usually the primary factor limiting the executable scale of quantum algorithms.

At the training level, HOLO’s QRNN adopts a hybrid quantum-classical variational optimization framework. The quantum circuit is responsible for the high-dimensional mapping and dynamic evolution of sequential features, while the parameter optimization process is handled by classical computing resources. By measuring the quantum state and constructing a differentiable loss function, the classical optimizer can gradually update the variational parameters in the QRB, continuously improving the model’s performance on prediction or classification tasks. This training approach not only aligns with the current development status of the quantum computing software and hardware ecosystem, but also provides a realistic path for future large-scale deployment.

In a variety of typical sequential learning tasks, including time series classification, trend prediction, and fine-grained change capture scenarios, this model comprehensively outperforms classical recurrent neural networks in prediction accuracy. Particularly noteworthy is that this QRNN demonstrates stronger sensitivity in predicting subtle change details in time series, enabling it to more accurately capture nonlinear dynamic features within sequences.

HOLO’s quantum recurrent neural network technology for sequential learning not only provides a standard paradigm in model structure that is expected to be widely adopted for quantum recurrent networks, but also fully considers the practical constraints of the NISQ era in engineering implementation. Through the modular design of quantum recurrent blocks, the interleaved stacking network construction method, and the hybrid quantum-classical training mechanism, this technology achieves a good balance between performance, scalability, and hardware adaptability. With the continuous evolution of quantum computing hardware, this QRNN model is expected to become one of the first learning models to achieve quantum advantage in the near future, laying a solid foundation for the industrialization of quantum artificial intelligence.

About MicroCloud Hologram Inc.

MicroCloud Hologram Inc. (NASDAQ: HOLO) is committed to the research and development and application of holographic technology. Its holographic technology services include holographic light detection and ranging (LiDAR) solutions based on holographic technology, holographic LiDAR point cloud algorithm architecture design, technical holographic imaging solutions, holographic LiDAR sensor chip design, and holographic vehicle intelligent vision technology, providing services to customers offering holographic advanced driving assistance systems (ADAS). MicroCloud Hologram Inc. provides holographic technology services to global customers. MicroCloud Hologram Inc. also provides holographic digital twin technology services and owns proprietary holographic digital twin technology resource libraries. Its holographic digital twin technology resource library utilizes a combination of holographic digital twin software, digital content, space data-driven data science, holographic digital cloud algorithms, and holographic 3D capture technology to capture shapes and objects in 3D holographic form. MicroCloud Hologram Inc. focuses on developments such as quantum computing and quantum holography, with cash reserves exceeding 3 billion RMB, and plans to invest more than 400 million in USD from the cash reserves to engage in blockchain development, quantum computing technology development, quantum holography technology development, and derivatives and technology development in frontier technology fields such as artificial intelligence AR. MicroCloud Hologram Inc.’s goal is to become a global leading quantum holography and quantum computing technology company.

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