Gonzales wins poster award at Seed LDRD session MCS Menu Alvin Gonzales, a postdoctoral appointee in the Mathematics and Computer Science division at the U.S. Department of Energy’s Argonne National Laboratory, received an Outstanding Poster Presentation Award at the lab’s 2026 Seed LDRD Poster Session in January. The annual event gives early-career scientists a chance to share their work with the lab community. Participants are judged based on presentation skills and the quality, clarity and organization of their poster. Gonzales’s poster introduced a new method called quantum distribution error mitigation (DEM) that corrects the output distribution of a quantum circuit by classical postprocessing. Most error mitigation techniques focus on improving the estimated value of an observable averaged over many runs. Gonzales instead focuses on correcting the full measured output distribution. A key part of his approach is estimating what’s called the noise vector. “Quantum circuits are noisy, and the error channels are generally difficult to characterize,” Gonzales said. To tackle this difficulty, he devised a tomography scheme that estimates the noise vector using just a single logical circuit. He then uses that scheme in DEM to classically adjust the noisy output distribution so that it better matches the ideal one. DEM also takes advantage of the circulant matrix structure, avoiding the need for expensive matrix inversion. “It opens new avenues of research, such as integration with error correction,” Gonzales said. Tests on quantum hardware with 5 to 30 qubits showed clear improvements in output distribution accuracy. In a 30-qubit GHZ test, for example, the method boosted the distribution fidelity to 97.7%, up from 23.2% without DEM correction. While this does not prepare the actual state, it demonstrates the effectiveness of DEM. “DEM dramatically increases the utility of pre-fault-tolerant quantum computers,” Gonzales said. “I think this work is a significant step