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Recently, scientists designed an AI agent that learns 60% faster than its peers by combining quantum and classical computing. 📈

This week, an international collaboration led by Dr. Philip Walther at the University of Vienna took the “classic” concept of reinforcement learning and gave it a quantum spin. They designed a hybrid AI that relies on both quantum and run-of-the-mill classic computing, and showed that—thanks to quantum quirkiness—it could simultaneously screen a handful of different ways to solve a problem.

The result is a reinforcement learning AI that learned over 60 percent faster than its non-quantum-enabled peers. This is one of the first tests that shows adding quantum computing can speed up the actual learning process of an AI agent, the authors explained.

Continue reading “Quantum Computing and Reinforcement Learning Are Joining Forces to Make Faster AI” »

Researchers say the new network will be unhackable and able to coordinate systems to unprecedented levels. Many of the deeper implications, however, cannot be foreseen.

US bank and QC Ware looked into use of technology to price complex derivatives.

News, analysis and comment from the Financial Times, the worldʼs leading global business publication.

Breakthrough in quantum chemistry has implications for quantum technology.

Quantum technology has a lot of promise, but several research barriers need to be overcome before it can be widely used. A team of US researchers has advanced the field another step, by bringing multiple molecules into a single quantum state at the same time.

A Bose-Einstein condensate is a state of matter that only occurs at very low temperatures – close to absolute zero. At this temperature, multiple particles can clump together and behave as though they were a single atom – something that could be useful in quantum technology. But while scientists have been able to get single atoms into this state for decades, they hadn’t yet achieved it with molecules.

Continue reading “Molecules brought in a single quantum state” »

Protocol to reverse engineer Hamiltonian models advances automation of quantum devices.

Scientists from the University of Bristol ’s Quantum Engineering Technology Labs (QETLabs) have developed an algorithm that provides valuable insights into the physics underlying quantum systems — paving the way for significant advances in quantum computation and sensing, and potentially turning a new page in scientific investigation.

In physics, systems of particles and their evolution are described by mathematical models, requiring the successful interplay of theoretical arguments and experimental verification. Even more complex is the description of systems of particles interacting with each other at the quantum mechanical level, which is often done using a Hamiltonian model. The process of formulating Hamiltonian models from observations is made even harder by the nature of quantum states, which collapse when attempts are made to inspect them.

Scientists from the University of Bristol’s Quantum Engineering Technology Labs (QETLabs) have developed an algorithm that provides valuable insights into the physics underlying quantum systems—paving the way for significant advances in quantum computation and sensing, and potentially turning a new page in scientific investigation.

Circa 2020 o.o!

Researchers have succeeded in creating an efficient quantum-mechanical light-matter interface using a microscopic cavity. Within this cavity, a single photon is emitted and absorbed up to 10 times by an artificial atom. This opens up new prospects for quantum technology, report physicists at the University of Basel and Ruhr-University Bochum in the journal Nature.

Quantum physics describes photons as light particles. Achieving an interaction between a single photon and a single atom is a huge challenge due to the tiny size of the atom. However, sending the photon past the atom several times by means of mirrors significantly increases the probability of an interaction.

Continue reading “Efficient Quantum-Mechanical Interface Leads to a Strong Interaction Between Light and Matter” »

In a major milestone for quantum physics, thousands of molecules have been induced to share the same quantum state, dancing together in unison like one huge super molecule.

This is a goal long-sought by physicists, who hope to harness complex quantum systems for technological applications — but getting a bunch of unruly molecules to work together is on a difficulty par with herding cats.

Continue reading “Physicists Harnessed Thousands of Molecules Into a Single Quantum State” »

Quantum simulators are a strange breed of systems for purposes that might seem a bit nebulous from the outset. These are often HPC clusters with fast interconnects and powerful server processors (although not usually equipped with accelerators) that run a literal simulation of how various quantum circuits function for design and testing of quantum hardware and algorithms. Quantum simulators do more than just test. They can also be used to emulate quantum problem solving and serve as a novel approach to tackling problems without all the quantum hardware complexity.

Despite the various uses, there’s only so much commercial demand for quantum simulators. Companies like IBM have their own internally and for others, Atos/Bull have created these based on their big memory Sequanna systems but these are, as one might imagine, niche machines for special purposes. Nonetheless, Nvidia sees enough opportunity in this arena to make an announcement at their GTC event about the performance of quantum simulators using the DGX A100 and its own custom-cooked quantum development software stack, called CuQuantum.

After all, it is probably important for Nvidia to have some kind of stake in quantum before (and if) it ever really takes off, especially in large-scale and scientific computing. What better way to get an insider view than to work with quantum hardware and software developers who are designing better codes and qubits via a benchmark and testing environment?