MicroCloud Hologram Inc Stock Price, News & Analysis

MicroCloud Hologram (NASDAQ: HOLO) announced a next-generation quantum convolutional neural network (QCNN) multi-class classification method using a hybrid quantum-classical training framework on Nov 14, 2025.

The system encodes MNIST samples using 8 qubits plus 4 auxiliary qubits, introduces a quantum perceptron and optimized entanglement layers, and reports QCNN accuracy comparable to classical CNNs at the same parameter scale for a four-class task. The company also disclosed cash reserves >3 billion RMB and plans to invest over $400 million in frontier tech including quantum computing.

MicroCloud Hologram (NASDAQ: HOLO) on Nov 10, 2025 described research into a theoretical framework treating time as a quantum operator, introducing POVM to map spatio-temporal coordinates and extending Lüder projection ideas to quantum evolution. The release says HOLO is developing ultra-precise quantum clocks, exploring zero-delay quantum communication, and optimizing quantum computing algorithms.

The company states it has cash reserves exceeding 3 billion RMB and plans to invest more than $400 million (USD) from those reserves into blockchain, quantum computing, quantum holography, AI/AR and related frontier technologies.

MicroCloud Hologram (NASDAQ: HOLO) expects full-year 2025 net income to exceed RMB 350 million, reversing a RMB 63 million net loss in 2024. The company says 2025 profitability will strengthen its cash position; cash, cash equivalents, and short-term investments currently exceed RMB 3 billion. MicroCloud plans to deploy over US$400 million from those reserves to invest in quantum computing, blockchain, and quantum holography, aiming to establish a leading position in those sectors.

The release contains forward-looking statements and notes related risks and SEC filings.

MicroCloud Hologram (NASDAQ: HOLO) announced a hybrid quantum-classical Quantum Convolutional Neural Network (QCNN) applied to the MNIST multi-class classification task on Oct 24, 2025. The company says the QCNN achieved accuracy comparable to classical CNNs using a quantum circuit with 8 data qubits and 4 auxiliary qubits, a novel Quantum Perceptron model, and a hybrid training loop that combines quantum feature extraction with classical optimization (softmax + cross-entropy). HOLO positions this work as a practical NISQ-era pathway and states plans to invest over $400 million in quantum, quantum holography, AI, AR, and related technologies.

MicroCloud Hologram (NASDAQ: HOLO) on October 23, 2025 announced an improved Grover quantum search algorithm and an FPGA-based implementation for dynamic multi-mode search. The company reports efficient simulation of up to 22 qubits on a single FPGA and a performance prediction model projecting scalability to 32 qubits. Key innovations described include dynamic phase modulation via a Configurable Lookup Table (CLUT), a Reconfigurable Logic Element (RLE) for hierarchical diffusion control, and an oracle-diffusion joint execution module enabling single-cycle execution and pipelineable search. HOLO says these advances reduce gate depth, wiring complexity, and FPGA latency, and that the approach supports multi-mode search applications in cryptanalysis, pattern recognition, and database retrieval.

MicroCloud Hologram (NASDAQ: HOLO) on October 15, 2025 proposed a quantum secure tripartite computing protocol based on blind quantum computing (BQC) to protect client data in multi-client collaborative computing.

The protocol uses BQC's blindness so a remote quantum server can process encrypted quantum inputs from two or more quantum-limited clients without accessing input meaning, output mapping, or client algorithms. HOLO says it extended the method to support multi-party scenarios by adjusting data paths and server workflows, and plans continued optimization as quantum technology advances. The company also disclosed plans to invest over $400 million in related technology sectors.

MicroCloud Hologram (NASDAQ: HOLO) on Oct 13, 2025 proposed a short-term, full-cycle path toward practical quantum computing advantage centered on multi-QPU circuit knitting, error suppression/mitigation, and heuristic algorithms with asymptotic speedup. The company advocates a quantum-centric supercomputing architecture that tightly integrates quantum and classical processors to assign quantum-computable tasks to QPUs and classical tasks to CPUs. HOLO said future software must automate compilation, algorithm design, and user interfaces to deliver a "frictionless" experience and broaden real-world use cases. The release also states plans to invest over $400 million across quantum computing, quantum holography, blockchain, AI, and AR development.

MicroCloud Hologram (NASDAQ: HOLO) announced a proposed universal “quantum variable” computing method and a specific implementation model on October 7, 2025. The method is designed to work with traditional qubits, high‑dimensional qubits (d>2), and quantum continuous variables, using an auxiliary‑mediated mechanism and adaptive local measurements.

The proposed implementation relies on three elements: repeated use of a single two‑body auxiliary‑register interaction gate, auxiliaries prepared in a single state, and local measurements of auxiliaries. The company also said it plans to invest over $400 million in quantum computing, quantum holography, blockchain, AI and AR technology sectors.

MicroCloud Hologram (NASDAQ: HOLO) on October 3, 2025 reported research defining C-NOT gate cost bounds and near‑optimal decompositions for quantum channels using three circuit models: QCM, RandomQCM, and MeasuredQCM. The study proves lower bounds on C-NOT counts via entanglement‑entropy and circuit complexity, showing bounds scale with qubit dimensions and entanglement needs. HOLO reports practical decompositions: QCM within 1.5× the lower bound, RandomQCM within 1.2×, and MeasuredQCM often approaching the theoretical lower bound. The work proposes a measurement–feedback paradigm to reduce C-NOT resources while noting implementation challenges in measurement timing and classical control.