书籍

书籍

论文

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VQNet: Library for a Quantum-Classical Hybrid Neural Network

Authors: Zhao-Yun Chen, Cheng Xue, Si-Ming Chen, Guo-Ping Guo

Submitted: 25 January, 2019; Joriginally announced: January 2019.

Abstract: Deep learning is a modern approach to realize artificial intelligence. Many frameworks exist to implement the machine learning task; however, performance is limited by computing resources. Using a quantum computer to accelerate training is a promising approach. The variational quantum circuit (VQC) has gained a great deal of attention because it can be run on near-term quantum computers. In this paper, we establish a new framework that merges traditional machine learning tasks with the VQC. Users can implement a trainable quantum operation into a neural network. This framework enables the training of a quantum-classical hybrid task and may lead to a new area of quantum machine learning.

论文地址: https://arxiv.org/abs/1901.09133

OriginIR: High-Level Language for Quantum-Classical Hybrid Programming

Authors: Zhao-Yun Chen, Guo-Ping Guo

Submitted: 4 January, 2019; originally announced: January 2019.

Abstract: Hybrid quantum-classical algorithms have drawn much attention because of their potential to realize the "quantum advantage" in noisy, intermediate-scale quantum (NISQ) devices. Here we introduce OriginIR, a cross-platform quantum language for hybrid programming. OriginIR can be compiled to various host backends, allowing the user to write portable quantum subprograms. The hybrid programming is based on the type system, which is used to decide where a statement should be run. We also introduce Qurator, a VSCode plugin that has OriginIR language support and two host backends.

论文地址: https://arxiv.org/abs/1901.08340

64-Qubit Quantum Circuit Simulation

Authors: Zhao-Yun Chen, Qi Zhou, Cheng Xue, Xia Yang, Guang-Can Guo, Guo-Ping Guo

Submitted: 12 July, 2018; v1 submitted 19 February, 2018; originally announced: February 2018.

Abstract: Classical simulations of quantum circuits are limited in both space and time when the qubit count is above 50, the realm where quantum supremacy reigns. However, recently, for the low depth circuit with more than 50 qubits, there are several methods of simulation proposed by teams at Google and IBM. Here, we present a scheme of simulation which can extract a large amount of measurement outcomes within a short time, achieving a 64-qubit simulation of a universal random circuit of depth 22 using a 128-node cluster, and 56- and 42-qubit circuits on a single PC. We also estimate that a 72-qubit circuit of depth 23 can be simulated in about 16 h on a supercomputer identical to that used by the IBM team. Moreover, the simulation processes are exceedingly separable, hence parallelizable, involving just a few inter-process communications. Our work enables simulating more qubits with less hardware burden and provides a new perspective for classical simulations.

论文地址: https://arxiv.org/abs/1901.08340

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