论文标题
用于大尺寸iSing问题的基于混合门的量子计算和退火量子计算
Hybrid Gate-Based and Annealing Quantum Computing for Large-Size Ising Problems
论文作者
论文摘要
大多数量子计算应用程序的主要问题之一是，解决实际问题的Qubit数量比当今的量子硬件要大得多。我们提出了一种称为大型系统抽样近似（LSSA）的算法，以求解具有最高尺寸的问题的问题，最大尺寸{\ rm {\ rm {gb}} 2^{n _ {\ rm {gb {gb}}}} $由a $ n _ { $ n _ {\ rm {an}} 2^{n _ {\ rm {gb}}} $通过$ n _ {\ rm {an}} $ qubit-qubit量子量子的混合计算体系结构，并基于$ n _ {\ rm {an}} $ qubit-quit-n _ _ {$ n _ _ {\ rm {\ rm {通过将完整系统问题分为较小的子系统问题，LSSA算法通过基于门的量子计算机或量子退火器解决子系统问题，通过通过各种量子量化的量子（VQE）的基础来确定不同子系统的解决方案的幅度贡献，并通过全程量子量的量子构成了全程量子的幅度。我们将LSSA的级别1近似于使用基于5克门的量子计算机求解最高160个变量的完全连接的随机问题，并使用100 Qubit量子量子退火器和7 Qubit的基于7 Qubit的量子计算机来解决最高4096个变量的投资组合优化问题。我们演示了LSSA级别2近似的使用来求解最高5120的投资组合优化问题（$ n _ {\ rm {gb}}} 2^{2n _ {\ rm {gb}}} $基于门的量子计算机。基于混合门和退火量子计算体系结构的全新计算概念为研究大尺寸的ISING问题和组合优化问题提供了有前途的可能性，从而在不久的将来通过量子计算实现了实用应用。
One of the major problems of most quantum computing applications is that the required number of qubits to solve a practical problem is much larger than that of today's quantum hardware. We propose an algorithm, called large-system sampling approximation (LSSA), to solve Ising problems with sizes up to $N_{\rm{gb}}2^{N_{\rm{gb}}}$ by an $N_{\rm{gb}}$-qubit gate-based quantum computer, and with sizes up to $N_{\rm{an}}2^{N_{\rm{gb}}}$ by a hybrid computational architecture of an $N_{\rm{an}}$-qubit quantum annealer and an $N_{\rm{gb}}$-qubit gate-based quantum computer. By dividing the full-system problem into smaller subsystem problems, the LSSA algorithm then solves the subsystem problems by either gate-based quantum computers or quantum annealers, optimizes the amplitude contributions of the solutions of the different subsystems with the full-problem Hamiltonian by the variational quantum eigensolver (VQE) on a gate-based quantum computer, and determines the approximated ground-state configuration. We apply the level-1 approximation of LSSA to solving fully-connected random Ising problems up to 160 variables using a 5-qubit gate-based quantum computer, and solving portfolio optimization problems up to 4096 variables using a 100-qubit quantum annealer and a 7-qubit gate-based quantum computer. We demonstrate the use of the level-2 approximation of LSSA to solve the portfolio optimization problems up to 5120 ($N_{\rm{gb}}2^{2N_{\rm{gb}}}$) variables with pretty good performance by using just a 5-qubit ($N_{\rm{gb}}$-qubit) gate-based quantum computer. The completely new computational concept of the hybrid gate-based and annealing quantum computing architecture opens a promising possibility to investigate large-size Ising problems and combinatorial optimization problems, making practical applications by quantum computing possible in the near future.
