#China #Shenzhen #FPGA Technology #MicroCloud #Quantum Simulation

MicroCloud Hologram Inc.'s Revolutionary Use of FPGA Technology

MicroCloud Hologram Inc., trading under Nasdaq as HOLO, is making significant strides in the field of quantum computations with a new hardware acceleration technology aimed at optimizing quantum spin model simulations. This breakthrough effectively transforms quantum tensor network algorithms into parallel computing circuits compatible with field programmable gate arrays (FPGA), paving a novel approach for quantum physics research and algorithm verification on classical hardware.

The Importance of Tensor Networks in Quantum Research

In the realm of quantum many-body systems, tensor network (TN) algorithms serve as a vital numerical tool. These algorithms cleverly circumvent the challenge of exponential state space expansion by decomposing complex quantum states into a network format based on multiple low-dimensional tensors. Common tensor network models such as matrix product states (MPS), projected entangled pair states (PEPS), and multi-scale entanglement renormalization (MERA) are pivotal in areas like condensed matter physics and quantum phase transitions.

However, enhancing system characterization precision increases tensor dimensions and connectivity, leading to a steep rise in computational complexity. For instance, in a two-dimensional spin system, enhancing the entanglement rank from χ=8 to χ=32 can result in nearly a hundredfold increase in floating-point operations per iteration, ultimately causing substantial demands on storage and memory bandwidth. This challenge results in even high-end CPU and GPU platforms struggling to perform simulations in a timely manner.

Breaking Traditional Limits

In response, MicroCloud Hologram seeks to transcend the constraints of conventional processing architectures by investigating hardware-level paths for algorithm reconstruction and logic mapping. FPGAs are instrumental in this endeavor due to their reconfigurability, parallel capability, and low latency. By routing core computational modules—like tensor contraction and matrix operations—directly into hardware circuits, they aim to minimize memory consumption and control overhead, resulting in efficient high-density parallel computing.

The essence of MicroCloud's technology lies in the co-design of algorithms and hardware. It breaks down the tensor network algorithm from software logic into computational units that can be implemented in hardware, constructing a highly scalable architecture driven by FPGA.

The systematic analysis of quantum spin models through tensor networks showed that systems represented by the Heisenberg spin chain can have their Hamiltonians decomposed into localized interaction terms. These terms are encoded into local tensors within tensor networks, allowing for effective simulation of their dynamics.

The Architecture of Innovation

MicroCloud has devised a Hierarchical Tensor Contraction Pipeline that consists of three core layers:

1. Input and Scheduling Layer: This layer focuses on breaking down high-dimensional tensors into manageable blocks while conducting data flow scheduling and dependency analysis.

2. Core Computing Layer: Comprising multiple MAC Arrays, this layer supports tensor contraction for various dimensions. Each unit utilizes customized logic to facilitate pipeline-level parallelism for performing floating-point additions and multiplications simultaneously.

3. Output and Reduction Layer: Responsible for merging and normalizing tensor results, this layer ensures that intermediate states are cached appropriately and ready for subsequent iterations.

By utilizing a combination of Verilog and high-level synthesis (HLS) tools, MicroCloud automates the generation of tensor operation circuits, employing strategies suited for diverse tensor connectivity graphs. This design approach achieves high-density parallelization on-chip, optimizing computational throughput within limited logic resources.

Performance Breakthroughs

Leveraging its FPGA-centric architecture, MicroCloud successfully accelerates quantum tensor network computations. The shifts in algorithm structure, logic circuit mapping, and pipelined design have led to performance improvements, achieving speeds 1.7 times faster than traditional CPU computations and doubling energy efficiency. This development not only showcases the capabilities of FPGA in quantum simulation but also lays the groundwork for future quantum algorithm hardware implementations.

Future Directions

Looking ahead, MicroCloud intends to continue its commitment to bridging algorithms and hardware, progressing towards industrialization efforts in quantum computing. This will encompass further development of quantum variational algorithms (VQE), quantum linear system solvers (QLSA), and the FPGA-ization of quantum machine learning models. These initiatives aim to solidify FPGA's role as a critical link between quantum and classical computing realms, enhancing the maturation of quantum technologies in industry.

About MicroCloud Hologram Inc.

MicroCloud Hologram Inc. (NASDAQ: HOLO) specializes in holographic technology research and application. Their services encompass holographic light detection and ranging (LiDAR) solutions, imaging technologies, and advanced driving assistance systems (ADAS), revealing their commitment to pioneering technology advancement globally. With substantial financial reserves, MicroCloud is poised to invest significantly in the progressive fields of blockchain and quantum technologies, striving to position itself at the forefront of quantum computing and holography.

Through its innovative work, MicroCloud is not just reimagining the landscape of quantum simulations but is actively preparing the infrastructure for the technological breakthroughs of tomorrow.