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Twesh Upadhyaya, William F. Braasch, Jr., Gabriel T. Landi, and Nicole Yunger Halpern PRX Quantum 5, 030355 – Published 23 September 2024 https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.5.


As an ice cube melts in water, the heat exchange d by the two produces disorder. Imagine measuring the heat flow while the ice melts in each of many trials. From the measurement results, one can compute the disorder generated in each trial—the stochastic entropy production (SEP). The SEP is well understood in the case of two classical systems interacting; there is one widely accepted SEP definition that can be expressed equivalently via three formulas. But the situation is far murkier for quantum analogues, such as two atoms exchanging components of spin.

Generalizing the three SEP formulas to accommodate quantum systems, we prove that quantum effects render the three SEP formulas inequivalent. Each formula reasonably quantifies entropy production and highlights a different aspect of the underlying physics. The inequivalence of the formulas stems from the inability to simultaneously measure the exchange d quantities of the quantum systems, i.e., the uncertainty principle. This quantumness leads to negative and even nonreal entropy production. Though unusual, these entropy values herald notable physical phenomena. A negative entropy production signals superposition in the thermal initial states of the quantum systems. An imaginary entropy production witnesses contextuality, a precise notion of nonclassicality.

Our work reveals new facets of entropy production for quantum systems, with potential implications for the performance of future technologies. For example, negative entropy production could be harnessed to improve the efficiency of a quantum engine.

Next Generation Biomanufacturing Technologies — Dr. Leonard Tender, Ph.D. — Biological Technologies Office, Defense Advanced Research Projects Agency — DARPA


Dr. Leonard Tender, Ph.D. is a Program Manager in the Biological Technologies Office at DARPA (https://www.darpa.mil/staff/dr-leonar…) where his research interests include developing new methods for user-defined control of biological processes, and climate and supply chain resilience.

Prior to coming to DARPA, Dr. Tender was a principal investigator and led the Laboratory for Molecular Interfaces in the Center for Bio/Molecular Science and Engineering at the U.S. Naval Research Laboratory. There, among other accomplishments, he facilitated numerous international collaborations with key external stakeholders in academia, industry, and government and his highly interdisciplinary research team, comprised of electrochemists, microbiologists, and engineers, is widely recognized for its many contributions to the field of microbial electrochemistry.

Dr. Tender earned a doctorate degree in analytical chemistry from the University of North Carolina, Chapel Hill; a bachelor’s degree in chemistry from the Massachusetts Institute of Technology; completed a post-doctoral fellowship in the Department of Chemistry from the University of California, Berkeley; and served as a visiting scientist in the Stanford University Department of Chemistry.

Dr. Tender co-founded the International Society for Microbial Electrochemistry and Technology and is a recipient of the Arthur S. Flemming Award, which honors outstanding federal employees, by the George Washington University’s Trachtenberg School of Public Policy and Public Administration.

Quantum memory lets a quantum computer perform a task not necessarily with fewer steps, but with less data. Could this in itself be a way to prove quantum advantage?


The new papers show that quantum memory lets a quantum computer perform a task not necessarily with fewer steps, but with less data. As a result, researchers believe this in itself could be a way to prove quantum advantage. “It allows us to, in the more near term, already achieve that kind of quantum advantage,” said Hsin-Yuan Huang, a physicist at Google Quantum AI.

But researchers are excited about the practical benefits too, as the new results make it easier for researchers to understand complex quantum systems.

“We’re edging closer to things people would really want to measure in these physical systems,” said Jarrod McClean, a computer scientist at Google Quantum AI.