UCSD To Create a New School to Advance Data Science and Prepare Graduates to Propel Innovations in AI

Researchers at the US-based Lawrence Berkeley National Lab (LBNL) recently reported a new approach to error mitigation in a quantum computer (QC) that targets error-producing noise, a ubiquitous problem that can severely limit the performance and utility of existing and near-future quantum computers. The method developed at LBNL consists of taking an initial noisy target circuit and constructing an analogous estimation circuit that is configured specifically for accurate noise characterization. The information gathered from running the estimation circuit is then applied to correct the noise in the original target circuit.

The US Office of Science and Technology Policy recently released its second annual National Quantum Initiative (NQI) report, a supplement to the President's FY22 Budget Request that outlines the major US government quantum information science (QIS) research activities and related funding levels out to FY 2022. As seen in Figure 1, the proposed FY2022 budget, which is targeted for about $880 million, calls for an increase of nearly 11% from the previous year. Roughly half of the funding is to come from the NQI and the other half from base agency-specific QIS R&D budgets. The figure represents the sum of Federal budgets for U.S. QIS R&D efforts in over a dozen agencies including NIST, NSF, DOE, NASA, DOD, and DHS, and it also aggregates several QIS subtopics such as computing, networking, sensing, fundamental science, and end quantum-related use cases