China

Quantum 2020

Machine learning for photonics quantum information processing

The Institute of Physics and IOP Publishing, in partnership with the Chinese Physical Society (CPS) and the University of Science and Technology of China (USTC) are proud to have brought together the international quantum science community online for this inaugural conference.


Early career and leading researchers from universities, industry and government worldwide came together to share, learn and collaborate on the latest research and emerging areas of high interest across quantum science and technology. The broad themes covered by Quantum 2020 included:

Quantum communication;
Quantum computing and quantum simulation;
Quantum sensing, metrology and imaging;
Quantum information theory, software and algorithms;
Quantum foundations;
Quantum technology in industry.
The four-day event featured talks from world-renowned scientists, interactive poster sessions and two exciting panel discussions on the themes of Quantum Technology in Industry and Quantum Technology Programmes Around the World.


You will need to [register](https://www.iopconferences.org/iop/frontend/reg/tRegisterEmailNew.csp?pageID=978403&eventID=1552&tempPersonID=536224) to obtain a ticket. . If you have already registered you should have been sent an email from Underline (to the email address that you registered with) with the subject line ‘Welcome to Quantum 2020’. This email includes your ticket for the event. 

If you have a ticket and are experiencing problems in accessing the event, please contact us. 

The Institute of Physics and IOP Publishing, in partnership with the Chinese Physical Society (CPS) and the University of Science and Technology of China (USTC) are proud to have brought together the international quantum science community online for this inaugural conference.

Early career and leading researchers from universities, industry and government worldwide came together to share, learn and collaborate on the latest research and emerging areas of high interest across quantum science and technology. 

invited talk

"Computing the electronic structure of molecules is a central problem. Despite the enormous success of approximate methods, facing this problem exactly with conventional computers is still a tremendous challenge. This has triggered theoretical and experimental efforts to solve chemistry problems using quantum computers, with proof-of-concept results using a few qubits. An appealing alternative to this digital approach is analog quantum simulation, which relies on a highly controllable device to mimic the system of interest and gain information about the desired solution, and does not require a fully operative computer.

Here we propose how it is possible to simulate quantum chemistry problems using ultra-cold atoms as a simulator [1]. In this simulator, fermionic atoms hopping in an optical potential play the role of electrons, additional optical potentials provide the nuclear attraction, and an additional atomic species mediates the electronic Coulomb repulsion. We analyze the impact of discretization and finite size effects of the lattice, and provide the working conditions required for the precise determination of the electronic energy of simple molecules. We also show how simplified models (e.g. two-dimensional [2]) can lead to the first experiments in this analog simulation approach.

[1] Nature 574 (7777), 215-218 (2019). 
[2] Phys. Rev. Research. (Accepted, 2020)."

Quantum simulation of quantum chemistry in optical lattices

James Robinson: Quantum technologies device fabrication challenges and solutions - Oxford Instruments

Tomi Elovaara: The Cryogenic Wafer Prober - Bluefors

IOP Publishing Academy: How to get Published

Spin-photon interfaces for quantum repeater networks

Improving the Performance of IBM Quantum Hardware

Quantum Technology and Industry I

Quantum Simulations and Quantum Error Correction in Circuit QED

The classical complexity of Gaussian Boson Sampling

Quantum Technology and Industry II

technical paper

panel

keynote

https://d3smihljt9218e.cloudfront.net/transcript/3564/subtitle/9efc2711e63ef010db1a081d0ac42578.vtt

English (automatic)

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Machine learning for photonics quantum information processing

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