The field of *machine learning* on **quantum computers** got a boost from new research. The research removed a potential roadblock to the practical implementation of quantum neural networks. Theorists had previously thought an exponentially large training set would be required to train a quantum **neural network**. The *quantum* No-Free-Lunch theorem developed by Los Alamos National Laboratory shows that quantum entanglement eliminates this exponential overhead.

The research paper has been published in *Physical Review Letters. *The classical No-Free-Lunch theorem states that any machine-learning algorithm is as good as any other when their performance is averaged over all possible functions connecting the data to their labels. A direct consequence of this theorem that showcases the power of data in classical **machine learning**. The more data one has, the better the average performance. Data is the currency in machine learning that ultimately limits performance.

The new **Los Alamos No-Free-Lunch** theorem showed that in the quantum regime entanglement is also a currency. One can be exchanged for data to reduce data requirements.

Scientists used a *Rigetti quantum computer*. They entangled the quantum data set with a reference system to verify the new theorem.

Scientists demonstrated on quantum hardware. They can effectively violate the standard No-Free-Lunch theorem using *entanglement*. The new formula of the theorem held up under experimental test. The **theorem **suggests that entanglement should be considered a valuable resource in quantum machine learning with big data. Classical neural networks depend only on big data.