SHENZHEN, China, June 25, 2026 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, has announced a groundbreaking achievement of great theoretical and engineering significance: its proprietary technology for approximate quantum state preparation alongside an entanglement-dependent complexity algorithm. By systematically restructuring the quantum state preparation workflow, this technology effectively shifts the exponentially growing computational complexity of conventional quantum circuits to classical computing systems. Combined with entanglement structure analysis, the firm has built a controllable-depth approximate state generation framework, which delivers overall performance superior to traditional exact state initialization methods on existing noisy intermediate-scale quantum devices.
Quantum state preparation serves as a fundamental building block in quantum computing. Whether deployed for quantum machine learning, quantum optimization, quantum simulation, or high-dimensional data analysis tasks based on amplitude encoding, all these applications hinge on the critical step of mapping classical data into quantum states. Mathematically, a system of n qubits can span a 2n-dimensional complex vector space, which theoretically enables the encoding of intricate data structures within an exponentially expanded space. Nevertheless, this powerful expressive capability comes with substantial engineering overhead. For any arbitrary unstructured dataset, the exact preparation of its corresponding quantum state generally demands an exponential number of controlled rotation gates and multi-qubit entanglement operations. As a result, circuit depth and total gate counts quickly exceed the operational limits of current-generation quantum hardware.
The technical framework developed by HOLO consists of three tightly coupled layers. To begin with, the classical computing layer executes structural analysis and amplitude rearrangement on input data. The team adopts tensor decomposition to represent datasets, analyzes their inherent low-rank features and correlation distributions, and further identifies the regions that dominate amplitude contributions. When processing high-dimensional image or vector data, this layer leverages matrix and tensor decomposition techniques to extract principal components and generate compressed data representations. Within this workflow, the algorithm introduces an entanglement-dependent complexity metric to quantify the minimum entanglement resources required to construct the target quantum state.