Certain areas near the moon's poles linger perpetually in shadow, never receiving direct sunlight. Recent studies suggest these so-called permanently shadowed regions (PSRs) contain rich ice reservoirs that could reveal details about the early solar system; they could also help future visitors make fuel and other resources. But these areas are hard to photograph from satellites orbiting the moon and thus are a challenge to study. The few photons PSRs do reflect are often overwhelmed by staticlike camera noise and quantum effects. Now researchers have produced a deep-learning algorithm to cut through the interference and see these dark zones.

The role of biophotons in the brain is a growing area of research in neurobiology – and where there are photons there might be quantum mechanics. The light of the mind is blue, wrote the poet Sylvia Plath ("The Moon and the Yew Tree" 1961). But it seems it may actually be red. That's because recent research suggests a link between intelligence and the frequency of biophotons in animals' brains. In 2016 Zhuo Wang and colleagues at the South-Central University for Nationalities in China studied brain slices from various animals (bullfrog, mouse, chicken, pig, monkey and human) that had been excited by glutamate, an excitatory neurotransmitter.

Machine learning, a process used to train artificial intelligences, can take an extremely long time – but a quantum trick could massively speed things up for tasks involving particles of light called photons. In the classical version of this experiment, without any added quantum effects, the AI would only be able to move the photon to one specific state at a time, being rewarded when it made a correct guess. However, in the quantum version of the experiment, the AI could put the photon in a superposition of more than one state. "If the robot goes right, it does not receive a reward, but if it goes left it receives a reward. That's the classical version of the experiment, but the quantum version would allow it to go left and right simultaneously at each guess, requiring far fewer guesses before it learns to always go left. Machine learning, a process used to train artificial intelligences, can take an extremely long time – but a quantum trick could massively speed things up for tasks involving particles of light called photons. In the classical version of this experiment, without any added quantum effects, the AI would only be able to move the photon to one specific state at a time, being rewarded when it made a correct guess. However, in the quantum version of the experiment, the AI could put the photon in a superposition of more than one state. "If the robot goes right, it does not receive a reward, but if it goes left it receives a reward.

Skoltech scientists have shown that quantum enhanced machine learning can be used on quantum (as opposed to classical) data, overcoming a significant slowdown common to these applications and opening a "fertile ground to develop computational insights into quantum systems." The paper was published in the journal Physical Review A. Quantum computers utilize quantum mechanical effects to store and manipulate information. While quantum effects are often claimed to be counterintuitive, such effects will enable quantum enhanced calculations to dramatically outperform the best supercomputers. In 2019, the world saw a prototype of this demonstrated by Google as quantum computational superiority. Quantum algorithms have been developed to enhance a range of different computational tasks; more recently this has grown to include quantum enhanced machine learning.

Skoltech scientists have shown that quantum enhanced machine learning can be used on quantum (as opposed to classical) data, overcoming a significant slowdown common to these applications and opening a "fertile ground to develop computational insights into quantum systems". The paper was published in the journal Physical Review A. Quantum computers utilize quantum mechanical effects to store and manipulate information. While quantum effects are often claimed to be counterintuitive, such effects will enable quantum enhanced calculations to dramatically outperform the best supercomputers. In 2019, the world saw a prototype of this demonstrated by Google as quantum computational superiority. Quantum algorithms have been developed to enhance a range of different computational tasks; more recently this has grown to include quantum enhanced machine learning.

As the technological progress codified as Moore's Law slows down, computer scientists are turning to alternative methods of computing, such as superconducting quantum processors to deliver computing gains in the future. Jeffrey Welser, vice president and lab director at IBM Research at Almaden, spoke about quantum computing at the 49th annual Semicon West chip manufacturing show in San Francisco last week. I caught up with him to get his take on quantum computing for the layperson. IBM also displayed a part of its IBM Q System at the show, giving us an idea of how much refrigeration technology has to be built around a current quantum processor to ensure its calculations are accurate. Binary digits -- ones and zeroes -- are the basic components of information in classical computers. Quantum bits, or qubits, are built on a much smaller scale. And qubits can be in a state of 0, 1, or both at any given time.

Intel is working on a new transistor called MESO that could be 10 to 30 times more efficient than existing transistors, a potential game-changer for the industry (see our main article here). It could help solve many of the world's biggest problems, spurring AI efforts that could help everything from fighting climate change to improving waste management. We interviewed Intel's Amir Khosrowshahi, CTO of AI, and Ian Young, Senior Fellow and circuit designer and lead researcher on the MESO project. Khosrowshahi, who is supposed to be focused on product development and thus on projects with impact within the next 2 to 5 years, says he's more excited about MESO than any other project right now -- even though it could take 10 years to get to market. Young's team wrote a paper about MESO for Nature, published in December.

DeepMind has a new paper where researchers have uncovered two "surpising findings". The paper is described in "Understanding Deep Learning through Neuron Deletion". In networks that generalize well, (1) all neurons are important and (2) are more robust to damage. Deep Learning network have behavior that reminds us of holograms. These results are further confirmation of my conjecture that Deep Learning systems are like holographic memories.

A Canadian firm has courted controversy with its claim to have built a practical quantum computer, a feat thought to be decades away. Now, independent researchers are trying to understand whether it really can tap the strange world of quantum physics. For the modest sum of $15m (£9m), a start-up near Vancouver will sell you a black box the size of a garden shed with its logo emblazoned on the side in white neon. What if I told you the contents of the box were kept colder than the temperature of interstellar space? How about this: The box contains a machine that can solve some of the thorniest mathematical problems and could revolutionise computing.

Quantum physics has some spooky, anti-intuitive effects, but it could also be essential to how actual intuition works, at least in regards to artificial intelligence. In a new study, researcher Vedran Dunjko and co-authors applied a quantum analysis to a field within artificial intelligence called reinforcement learning, which deals with how to program a machine to make appropriate choices to maximize a cumulative reward. The field is surprisingly complex and must take into account everything from game theory to information theory. Dunjko and his team found that quantum effects, when applied to reinforcement learning in artificial intelligence systems, could provide quadratic improvements in learning efficiency, reports Phys.org . Exponential improvements might even be possible over short-term performance tasks.