1Tencent Quantum Laboratory, Tencent, Shenzhen, Guangdong 518057, China
2Tencent Quantum Laboratory, Tencent, Hong Kong, China
3Institute for Advanced Study, Tsinghua University, Beijing 100084, China
4Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
5Department of Electrical and Computer Engineering, McGill University, Quebec H3A 0E9 , Canada
6Shenzhen Middle School, Shenzhen, Guangdong 518025, China
7Tencent America, Palo Alto, California 94306, USA
8Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
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Abstract
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.
Popular summary
TensorCircuit is an open source quantum simulation framework in Python designed for speed, flexibility and elegance. The simulation is powered by an advanced tensor network engine and is implemented with the popular TensorFlow, JAX, and PyTorch machine learning frameworks in a backend agnostic way. TensorCircuit is compatible with modern machine learning engineering paradigms — automatic differentiation, just-in-time compilation, vectorized parallelism and GPU acceleration — which make it especially suited to simulating variational algorithms based on parameterized quantum circuits.
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[6] Weitang Li, Jiajun Ren, Sainan Huai, Tianqi Cai, Zhigang Shuai, and Shengyu Zhang, “Efficient Quantum Simulation of Electron-Phonon Systems by Variational Basis State Encoder”, arXiv:2301.01442, (2023).
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