TensorCircuit: a Quantum Software Framework for the NISQ Era
- URL: http://arxiv.org/abs/2205.10091v2
- Date: Fri, 27 Jan 2023 07:49:26 GMT
- Title: TensorCircuit: a Quantum Software Framework for the NISQ Era
- Authors: Shi-Xin Zhang, Jonathan Allcock, Zhou-Quan Wan, Shuo Liu, Jiace Sun,
Hao Yu, Xing-Han Yang, Jiezhong Qiu, Zhaofeng Ye, Yu-Qin Chen, Chee-Kong Lee,
Yi-Cong Zheng, Shao-Kai Jian, Hong Yao, Chang-Yu Hsieh, Shengyu Zhang
- Abstract summary: Written purely in Python,Circuit supports automatic differentiation, just-in-time compilation, vectorized parallelism and hardware acceleration.
Circuit can simulate up to 600 qubits with moderate depth and low-dimensional connectivity.
- Score: 18.7784080447382
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- A Scalable FPGA Architecture for Quantum Computing Simulation [0.0]
A quantum computing simulation provides the opportunity to explore the behaviors of quantum circuits.
Simulating quantum circuits requires geometric time and space complexities.
A scalable accelerator architecture is proposed to provide a high performance, highly parallel, accelerator.
arXiv Detail & Related papers (2024-07-08T21:48:28Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - State of practice: evaluating GPU performance of state vector and tensor
network methods [2.7930955543692817]
This article investigates the limits of current state-of-the-art simulation techniques on a test bench made of eight widely used quantum subroutines.
We highlight how to select the best simulation strategy, obtaining a speedup of up to an order of magnitude.
arXiv Detail & Related papers (2024-01-11T09:22:21Z) - cuQuantum SDK: A High-Performance Library for Accelerating Quantum
Science [7.791505883503921]
We present the NVIDIA cuQuantum SDK, a state-of-the-art library of composable primitives for GPU-accelerated quantum circuit simulations.
The cuQuantum SDK was created to accelerate and scale up quantum circuit simulators developed by the quantum information science community.
arXiv Detail & Related papers (2023-08-03T19:28:02Z) - TeD-Q: a tensor network enhanced distributed hybrid quantum machine
learning framework [59.07246314484875]
TeD-Q is an open-source software framework for quantum machine learning.
It seamlessly integrates classical machine learning libraries with quantum simulators.
It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
arXiv Detail & Related papers (2023-01-13T09:35:05Z) - QuDiet: A Classical Simulation Platform for Qubit-Qudit Hybrid Quantum
Systems [7.416447177941264]
textbfQuDiet is a python-based higher-dimensional quantum computing simulator.
textbfQuDiet offers multi-valued logic operations by utilizing generalized quantum gates.
textbfQuDiet provides a full qubit-qudit hybrid quantum simulator package.
arXiv Detail & Related papers (2022-11-15T06:07:04Z) - Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem.
The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment.
We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
arXiv Detail & Related papers (2022-04-18T21:45:13Z) - Parallel Simulation of Quantum Networks with Distributed Quantum State
Management [56.24769206561207]
We identify requirements for parallel simulation of quantum networks and develop the first parallel discrete event quantum network simulator.
Our contributions include the design and development of a quantum state manager that maintains shared quantum information distributed across multiple processes.
We release the parallel SeQUeNCe simulator as an open-source tool alongside the existing sequential version.
arXiv Detail & Related papers (2021-11-06T16:51:17Z) - An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian
Simulation [55.41644538483948]
Current generation noisy intermediate-scale quantum (NISQ) computers are severely limited in chip size and error rates.
We derive localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions.
The proposed numerical circuit compression algorithm behaves backward stable and scales cubically in the number of spins enabling circuit synthesis beyond $mathcalO(103)$ spins.
arXiv Detail & Related papers (2021-08-06T19:38:03Z) - Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits
at Exascale [57.84751206630535]
We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework.
The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators.
By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
arXiv Detail & Related papers (2021-04-21T13:26:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.