TensorCircuit: a Quantum Software Framework for the NISQ Era

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.

Whitepaper for TensorCircuit, 43 pages, 11 figures, 9 tables

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• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• The density-matrix renormalization group in the age of matrix product states
• Strong quantum computational advantage using a superconducting quantum processor
• Finite-depth scaling of infinite quantum circuits for quantum critical points

Cited by in corpus (51)

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• Benchmarking Quantum Computer Simulation Software Packages: State Vector Simulators
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• Distributed quantum architecture search
• TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era
• GSQAS: Graph Self-supervised Quantum Architecture Search
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• Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics
• Efficient Quantum Simulation of Electron-Phonon Systems by Variational Basis State Encoder
• Quantum Vision Transformers for Quark-Gluon Classification
• Symmetry Breaking Dynamics in Quantum Many-Body Systems
• Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic
• Efficient and Robust Parameter Optimization of the Unitary Coupled-Cluster Ansatz
• Subsystem Information Capacity in Random Circuits and Hamiltonian Dynamics
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• Skin Effect Induced Anomalous Dynamics from Charge-Fluctuating Initial States
• Universal imaginary-time critical dynamics on a quantum computer
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• Holographic deep thermalization for secure and efficient quantum random state generation
• Learning the expressibility of quantum circuit ansatz using transformer
• Determination of Molecular Energies via Quantum Imaginary Time Evolution in a Superconducting Qubit System
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• Correlated states in super-moiré materials with a kernel polynomial quantics tensor cross interpolation algorithm
• Enhancing Variational Quantum Circuit Training: An Improved Neural Network Approach for Barren Plateau Mitigation
• Variational post-selection for ground states and thermal states simulation
• Variational LOCC-assisted quantum circuits for long-range entangled states
• Quantum Machine Learning of Molecular Energies with Hybrid Quantum-Neural Wavefunction
• Quantum Pontus-Mpemba Effects in Real and Imaginary-time Dynamics
• Entanglement growth and information capacity in a quasiperiodic system with a single-particle mobility edge
• Effective temperature in approximate quantum many-body states
• Multi-set variational quantum dynamics algorithm for simulating nonadiabatic dynamics on quantum computers
• Supersymmetry dynamics on Rydberg atom arrays
• The Dual Role of Low-Weight Pauli Propagation: A Flawed Simulator but a Powerful Initializer for Variational Quantum Algorithms
• Quantum-data-driven dynamical transition in quantum learning
• Robustness of the projected squeezed state protocol
• Non-Markovianity Benefits Quantum Dynamics Simulation
• Scaling Laws of Quantum Information Lifetime in Monitored Quantum Dynamics
• Quantum generative classification with mixed states
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• Learning complexity gradually in quantum machine learning models