CityUHK finds quantum path to lossless energy transfer

Researchers at City University of Hong Kong have demonstrated a pathway to harvest energy with certainty from fluctuating sources, defying established physical limitations. The team led by Professor Oscar Dahlsten, Associate Professor at CityUHK, initially aimed to prove a theoretical constraint on energy transfer, but instead uncovered a quantum solution using the principle of “non-unique decomposition of mixed states.” This allows for “deterministic energy harvesting (DEH),” where a quantum harvester charges reliably as long as the source induces one of multiple superposition states.

Non-Unique Decomposition of Mixed States Enables Deterministic Energy Harvesting

The research, detailed in Physical Review Letters under the title “Nonunique Decompositions of Mixed States and Deterministic Energy Transfers”, hinges on the ability to bypass entropy transfer during energy harvesting. Conventional energy conversion processes inevitably lose power to randomness, hindering efficient charging of devices. However, the CityUHK team demonstrated a theoretical pathway where energy can move from a source to a harvester without a corresponding increase in disorder.

This is achieved through the exploitation of a quantum phenomenon where a system’s state isn’t defined by a single configuration, but rather a superposition of possibilities. Central to this process is the concept of multiple intermediate superposition states within the quantum harvester. Unlike traditional methods where energy transfer relies on a specific pathway, the harvester can be charged via several distinct quantum routes.

As long as the energy source induces one of these transitions, the final outcome, a fully charged harvester, is guaranteed, a characteristic the researchers term. This certainty contrasts sharply with probabilistic energy harvesting, where charging success depends on the likelihood of a specific event occurring. The implications extend beyond fundamental physics, potentially impacting the design of low-power technologies for a range of applications. The framework developed by the CityUHK team could reduce energy losses in conventional AC-to-DC rectifiers, components that convert alternating current from power outlets into the direct current used by electronic devices.

This improved efficiency could be particularly valuable for powering Internet-of-Things devices, wearable sensors, smart-city infrastructure and the next generation of 6G wireless technology, all of which demand low-power operation and reliable energy sources. The research was supported by a Research Grants Council General Research Fund, enabling the exploration of this unconventional approach to energy harvesting.

We initially set out to show that such a protocol might be impossible. To our surprise, our efforts led us to a quantum solution that could achieve exactly the deterministic energy transfer we were looking for.

Professor Oscar Dahlsten, Associate Professor at the Department of Physics at CityUHK
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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