Researchers at the University of Basel have developed a new approach to applying thermodynamics to microscopic quantum systems.
In 1798, the officer and physicist Benjamin Thompson (a.k.a. Count Rumford) observed the drilling of cannon barrels in Munich and concluded that heat is not a substance but can be created in unlimited amounts by mechanical friction.
Rumford determined the amount of heat generated by immersing the cannon barrels in water and measuring how long it took the water to reach boiling. Based on such experiments, thermodynamics was developed in the 19th century. Initially, it was at the service of the Industrial Revolution and explained, physically, for instance, how heat can be efficiently converted into useful work in steam engines.
Quantum systems are known to be prone to dissipation, a process that entails the irreversible loss of energy and that is typically linked to decoherence. Decoherence, or the loss of coherence, occurs when interactions between a quantum system and its environment cause a loss of coherence, which is ultimately what allows quantum systems to exist in a, Super superposition of states.
While dissipation is generally viewed as a source of decoherence in quantum systems, researchers at Tsinghua University recently showed that it could also be leveraged to study strongly correlated quantum matter.
Few challenges are as formidable as building a quantum computer. It’s not just about wiring its components but also making them work together to produce accurate computation results despite the presence of noise that can introduce errors in quantum computations.
While you can write down a quantum computation as an abstract mathematical model, implementing it practically still means adjusting certain parameters, such as microwave pulses or lasers, which you can only do with limited precision. Thus, there would inevitably be a gap between the model you intended to implement and the actual outcome.
Qruise leverages machine learning to narrow this gap. By building a physical model from a quantum computer’s experimental data and comparing it to the intended behavior, Qruise helps physicists and engineers to improve their quantum computers. This also works for other quantum devices, such as quantum sensors, and other fields like photonics.
Founded as a spinoff from Forschungszentrum Jülich in late 2021 by Shai Machnes, Frank Wilhelm-Mauch, Tommaso Calarco, and Simone Montangero, Qruise raised funding from Constructor Capital and went through the Creative Destruction Lab startup program.
Learn more about the future of machine learning for quantum computing and beyond from our interview with the co-founder and CEO, Shai Machnes: Why Did You Start Qruise?
Even before Qruise, my co-founders and I were researching how to control quantum systems. At some point, more academic groups than we could handle as researchers wanted to collaborate with us, so we decided to found a company in late 2021.
Our initial focus was just quantum control, but we soon realized that the very same tools could be applied to quantum sensing or even other domains, like photonics.
When I think of Qruise today, we’re actually building a “machine learning physicist”—a system that can predict and control all kinds of physical processes. I already had this vision more than 15 years ago as a researcher, but I wasn’t able to realize it. With recent advances in computing power and data availability, machine learning has greatly improved, making it possible for us to pursue this vision. READ MORE...
Researchers investigated the Mpemba effect in quantum systems, a phenomenon where hotter water can freeze faster than cooler water. This quantum Mpemba effect retains memory of its initial conditions, affecting its thermal relaxation later. The team used two systems with quantum dots and discovered the thermal quantum Mpemba effect across various conditions, suggesting possible broader applications beyond thermal analysis.Hotter quantum systems can cool faster than initially colder equivalents.Does hot water freeze faster than cold water? Aristotle may have been the first to tackle this question that later became known as the Mpemba effect.This phenomenon originally referred to the non-monotonic initial temperature dependence of the freezing start time, but it has been observed in various systems — including colloids — and has also become known as a mysterious relaxation phenomenon that depends on initial conditions.What Is the Mpemba Effect?The Mpemba effect is a counterintuitive phenomenon where hot water can freeze faster than cold water under certain conditions. Named after Erasto Mpemba, a Tanzanian student who observed this effect in the 1960s and subsequently brought it to the attention of the scientific community, the phenomenon has been a topic of curiosity for centuries, with references dating back to the likes of Aristotle. The exact cause of the Mpemba effect is still a topic of debate among scientists.
Recent FindingsNow, a team of researchers from Kyoto University and the Tokyo University of Agriculture and Technology has shown that the temperature quantum Mpemba effect can be realized over a wide range of initial conditions.“The quantum Mpemba effect bears the memory of initial conditions that result in anomalous thermal relaxation at later times,” explains project leader and co-author Hisao Hayakawa at KyotoU’s Yukawa Institute for Theoretical Physics. READ MORE...
