MicroCloud Hologram Advances Quantum Computing with Spin‑Qubit Technology

MicroCloud Hologram Inc. – Date not provided

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FPGA Accelerates Quantum Tensor Network Computing

MicroCloud Hologram Inc. has developed a system that leverages field‑programmable gate arrays (FPGAs) to accelerate quantum tensor‑network computations, addressing the limitations of traditional CPU and GPU processing. The approach maps core computational modules—such as tensor contraction and matrix multiplication—directly into FPGA hardware circuits. This enables deep pipelined, high‑density parallel computing and dramatically reduces memory‑access and control‑overhead bottlenecks.

The technology uses a Hierarchical Tensor Contraction Pipeline with three layers:

1. Input scheduling – breaks large tensors into smaller blocks and orders data dependencies.

2. Core computing – employs MAC (multiply‑accumulate) arrays to perform tensor contractions in a pipelined fashion.

3. Output reduction – merges and normalizes results, caching intermediate states for subsequent iterations.

Using Verilog and high‑level synthesis tools, the tensor‑operation circuits are automatically generated and optimized for the target FPGA architecture.

Testing showed performance 1.7 × faster than CPUs, with more than a 2 × improvement in energy efficiency, demonstrating the FPGA’s potential for quantum simulation and future quantum accelerators.

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Tensor‑Network Algorithms & Quantum Spin Models

Tensor‑network (TN) algorithms efficiently manage the exponential growth of data in quantum many‑body systems by decomposing complex states into networks of smaller tensors. Models such as matrix product states (MPS) and projected entangled‑pair states (PEPS) are fundamental to condensed‑matter physics and quantum‑spin‑model simulations. However, increasing precision or entanglement quickly escalates computational demands: expanding the entanglement rank from χ = 8 to χ = 32 can increase floating‑point operations per iteration by nearly two orders of magnitude, creating bottlenecks for conventional platforms.

MicroCloud Hologram’s Hierarchical Tensor Contraction Pipeline addresses these limits by parallelizing the core operations across FPGA resources, allowing higher‑rank simulations without the prohibitive cost of CPU‑ or GPU‑based approaches.

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Hierarchical Tensor Contraction Pipeline Architecture

The pipeline is organized into three distinct levels:

• Input & Scheduling Layer – decomposes large tensors into manageable blocks and analyzes data dependencies for optimal processing order.

• Core Computing Layer – consists of multiple MAC arrays that execute tensor contractions of varying dimensions using custom logic for pipelined, parallel floating‑point operations.

• Output & Reduction Layer – merges and normalizes tensor results, caching intermediate states for reuse in subsequent iterations.

Static scheduling and data‑reuse strategies maximize throughput within the FPGA’s limited logic resources, delivering a substantial performance increase over traditional CPU methods.

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7× Performance Gain & FPGA Quantum Potential

HOLO’s FPGA‑based implementation delivers a 1.7 × speed‑up over CPUs and more than 2 × improvement in energy efficiency. The deep‑pipelined architecture enables high‑density parallel computing essential for complex quantum simulations. By reconstructing algorithms with Verilog and high‑level synthesis tools, the company created a scalable, on‑chip parallel array that maximizes throughput.

MicroCloud Hologram anticipates extending this technology to accelerate other quantum algorithms such as the Variational Quantum Eigensolver (VQE) and Quantum Linear System Algorithm (QLSA), thereby bridging classical and quantum computing.

“It is believed that, through continuous research in this direction, FPGA will become an important bridge between quantum computing and classical computing, providing solid technical support for the industrialization development of quantum technology.”

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