Quantum Many-Body Computation on a Small Quantum Computer

报告题目：Quantum Many-Body Computation on a Small Quantum Computer

报 告 人: 王磊  研究员（中科院物理所）

邀 请 人：杨丽平                                         

时    间：2019年4月12号（星期五）上午10:00

地    点：物理学院LE201（理论物理平台）

Abstract : 

What can we do on a small quantum computer with O(10) qubits?

We propose a variational scheme to study ground state properties of quantum many-body systems on small scale near-term noisy quantum computers. One can obtain a matrix product state (MPS) representation of the variational ground state using a number of qubits smaller than the problem size. By increasing the qubits number, one can exponentially increase the bond dimension of the variational MPS on the quantum computer. To demonstrate the practical feasibility of the proposed scheme, we perform a first-principle classical simulation of differentiable circuit learning. Using only 6 qubits one can obtain the ground state of a 4 x 4 square lattice frustrated Heisenberg model with fidelity over 97%. Arbitrarily long ranged correlations can also be measured on the same circuit after variational optimization. Studying ground state of quantum magnets and quantum chemistry problems in this way will be one of few killer applications of a near-term quantum computer.

Ref：

Variational Quantum Eigensolver with Fewer Qubits,

Jin-Guo Liu, Yi-Hong Zhang, Yuan Wan, Lei Wang, arXiv:1902.02663

个人简介:

2006年本科毕业于南京大学，2011年获中科院物理研究所博士学位。此后在瑞士苏黎世联邦理工学院从事计算量子物理的博士后研究。从2016年3月起在中科院物理研究所工作。主要的研究方向是深度学习的理论与应用以及量子多体计算。热爱编程、崇尚技术。