Quantum Computing

The U.S. Quantum Information Science Summer School (USQIS) is the first joint summer school facilitated by the five DOE National Quantum Information Science Research Centers: C2QA, QSA, QSC, Q-NEXT, and SQMS. This first edition of the school is a ten-day program co-organized by SQMS and QSC and will be hosted at Fermilab. Please visit the Wesbite to know more. Dates: August 6, 2023 – Aug 15, 2023 at Fermi National Accelerator Laboratory in Batavia, Illinois

Applications Open during the month of June. The school is addressed to PhD students in quantum information science, particle physics and condensed matter. The students will be introduced to concepts in digital quantum computation and quantum field theory through lectures and hands-on programming exercises. The school will also cover main topics in the simulation of the Hamiltonian evolution of spin systems and lattice gauge theory, error mitigation, numerical methods and correction for these systems. Through seminars, the summer school will cover some recent work in these areas as well as additional theoretical themes of quantum information that are relevant for the work conducted at the Superconducting Quantum Systems and Materials (SQMS) Center and their collaborators. The program will include a mini-workshop of more advanced research presented in an accessible way.

The school is organised in collaboration with the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Superconducting Quantum Materials and Systems Center (SQMS).

USRA and Rigetti announced today that they entering Phase-2 of their 4-year long operation on the DARPA Optimization with NISQ Devices program (ONISQ). The project "Scheduling Applications with Advanced Mixers" (SAAM) aims to push the implementation of quantum optimization algorithms to a scale and complexity relevant for DARPA constrained optimization problems.

The paper "Information scrambling in quantum circuits" (https://www.doi.org/10.1126/science.abg5029) involving USRA researcher Jeffrey Marshall and collaborators published in Science. In this paper, the authors experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor.

The paper "Classical symmetries and the Quantum Approximate Optimization Algorithm" (https://doi.org/10.1007/s11128-021-03298-4) by Ruslan Shaydulin, Stuart Hadfield, Tad Hogg, and Ilya Safro published in Quantum Information Processing. In this paper, the authors study the relationship between the Quantum Approximate Optimization Algorithm (QAOA) and the underlying symmetries of the objective function to be optimized.