Closing the "quantum supremacy" gap

Liu, Yong; Liu, Xin; Li, Fang; Fu, Haohuan; Yang, Yibo; Song, Jiawei; Zhao, Pengpeng; Wang, Zhen; Peng, Dajia; Chen, Huarong; Guo, Chu; Huang, He-Liang; Wu, Wencheng; Chen, Dexun

doi:10.1145/3458817.3487399

Cited by 90 publications

(33 citation statements)

References 20 publications

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“…In the most general case, as for other types of quantum simulators, the time required to simulate quantum circuits via tensor network contraction is exponential in the number of qubits. However, in special cases -including many of practical relevance -tensor network contraction can offer significant advantages since it avoids the memory bottleneck that plagues full state simulators and, to date, the largest scale quantum computing simulations such as the simulation of random circuits used in quantum supremacy experiments [38,39] have all been performed via this approach [40,41,42,43,44,45].…”

Section: Tensor Network Enginementioning

confidence: 99%

TensorCircuit: a Quantum Software Framework for the NISQ Era

Zhang

Allcock

Wan

et al. 2023

Quantum

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TensorCircuit is an open source quantum circuit simulator based on tensor network contraction, designed for speed, flexibility and code efficiency. Written purely in Python, and built on top of industry-standard machine learning frameworks, TensorCircuit supports automatic differentiation, just-in-time compilation, vectorized parallelism and hardware acceleration. These features allow TensorCircuit to simulate larger and more complex quantum circuits than existing simulators, and are especially suited to variational algorithms based on parameterized quantum circuits. TensorCircuit enables orders of magnitude speedup for various quantum simulation tasks compared to other common quantum software, and can simulate up to 600 qubits with moderate circuit depth and low-dimensional connectivity. With its time and space efficiency, flexible and extensible architecture and compact, user-friendly API, TensorCircuit has been built to facilitate the design, simulation and analysis of quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era.

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Section: Tensor Network Enginementioning

confidence: 99%

TensorCircuit: a Quantum Software Framework for the NISQ Era

Zhang

Allcock

Wan

et al. 2023

Quantum

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“…The computation of CNN can be significantly accelerated by heterogeneous hardware, such as Intel Xeon Phi, NVIDIA (AMD) GPUs, sw26010pro CPUs, etc. The sw26010pro many-core processor is the upgraded version of sw26010 released with Sunway TaihuLight [43][44][45]. Taking sw26010pro as an example, there are six core groups on one chip.…”

Section: Hybrid Implementation In Ai and Hpcmentioning

confidence: 99%

Deep learning representations for quantum many-body systems on heterogeneous hardware

Liang

Li²,

Xiao³

et al. 2023

Mach. Learn.: Sci. Technol.

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The quantum many-body problems are important for condensed matter physics, however solving the problems are challenging because the Hilbert space grows exponentially with the size of the problem. The recently developed deep learning methods provide a promising new route to solve long-standing quantum many-body problems. We report that a deep learning based simulation can achieve solutions with competitive precision for the spin $J1$-$J2$ model and fermionic $t$-$J$ model, on rectangular lattices within periodic boundary conditions. The optimizations of the deep neural networks are performed on the heterogeneous platforms, such as the new generation Sunway supercomputer and the multi graphical-processing-unit clusters. Both high scalability and high performance are achieved within an AI-HPC hybrid framework. The accomplishment of this work opens the door to simulate spin and fermionic lattice models with state-of-the-art lattice size and precision.

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“…Quantum supremacy is achieved when algorithms running on quantum computers can produce results that surpass those generated on conventional computing resources in accuracy and/or resources required [115]. It was demonstrated on a tailored sampling task by several research teams [20][21][22], although the magnitude of the advantage was also contested [116,117]. Quantum advantage is in general used interchangeably with quantum supremacy, however we propose to use quantum advantage to refer to an instance of quantum supremacy where the advantage has relevant, (a) Hamiltonian representation: First pre-processing step of the VQE in which a set of basis functions is defined for the Hamiltonian to be expressed as a quantum observable of the electronic wave function (Sec.…”

Section: Advantage Argument Assumptions and Limitations Of The Vqementioning

confidence: 99%