Lifeboat Foundation

Many machine learning algorithms on quantum computers suffer from the dreaded “barren plateau” of unsolvability, where they run into dead ends on optimization problems. This challenge had been relatively unstudied—until now. Rigorous theoretical work has established theorems that guarantee whether a given machine learning algorithm will work as it scales up on larger computers.

“The work solves a key problem of useability for quantum machine learning. We rigorously proved the conditions under which certain architectures of variational quantum algorithms will or will not have barren plateaus as they are scaled up,” said Marco Cerezo, lead author on the paper published in Nature Communications today by a Los Alamos National Laboratory team. Cerezo is a post doc researching quantum information theory at Los Alamos. “With our theorems, you can guarantee that the architecture will be scalable to quantum computers with a large number of qubits.”

“Usually the approach has been to run an optimization and see if it works, and that was leading to fatigue among researchers in the field,” said Patrick Coles, a coauthor of the study. Establishing mathematical theorems and deriving first principles takes the guesswork out of developing algorithms.

Nodes known as Alice, Bob and Charlie share entangled state across two different labs.

Graph representations can solve complex problems in natural science, as patterns of connectivity can give rise to a magnitude of emergent phenomena. Graph-based approaches are specifically important during quantum communication, alongside quantum search algorithms in highly branched quantum networks. In a new report now published on Science Advances, Max Ehrhardt and a team of scientists in physics, experimental physics and quantum science in Germany introduced a hitherto unidentified paradigm to directly realize excitation dynamics associated with three-dimensional networks. To accomplish this, they explored the hybrid action of space and polarization degrees of freedom of photon pairs inside complex waveguide circuits. The team experimentally explored multiparticle quantum walks on complex and highly connected graphs as testbeds to pave the way to explore the potential applications of fermionic dynamics in integrated photonics.

Complex networks

Complex networks can occur across diverse fields of science, ranging from biological signaling pathways and biochemical molecules to exhibit efficient energy transport to neuromorphic circuits across to social interactions across the internet. Such structures are typically modeled using graphs whose complexity relies on the number of nodes and linkage patterns between them. The physical representation of a graph is limited by their requirement for arrangement in three-dimensional (3D) space. The human brain is a marked example of scaling behavior that is unfavorable for physical simulation due to its staggering number of 80 billion neurons, dwarfed by 100 trillion synapses that allow the flow of signals between them. Despite the number of comparably miniscule volume of nodes, discrete quantum systems faced a number of challenges owing to complex network topologies, efficient multipartite quantum communications and search algorithms.

Researchers at the quantum computing firm D-Wave Systems have shown that their quantum processor can simulate the behaviour of an “untwisting” quantum magnet much faster than a classical machine. Led by D-Wave’s director of performance research Andrew King, the team used the new low-noise quantum processor to show that the quantum speed-up increases for harder simulations. The result shows that even near-term quantum simulators could have a significant advantage over classical methods for practical problems such as designing new materials.

The D-Wave simulators are specialized quantum computers known as quantum annealers. To perform a simulation, the quantum bits, or qubits, in the annealer are initialized in a classical ground state and allowed to interact and evolve under conditions programmed to mimic a particular system. The final state of the qubits is then measured to reveal the desired information.

King explains that the quantum magnet they simulated experiences both quantum fluctuations (which lead to entanglement and tunnelling) and thermal fluctuations. These competing effects create exotic topological phase transitions in materials, which were the subject of the 2016 Nobel Prize in Physics.

STUTTGART, Germany — NATO and its member nations have formally agreed upon how the alliance should target and coordinate investments in emerging and disruptive technology, or EDT, with plans to release artificial intelligence and data strategies by the summer of 2021.

In recent years the alliance has publicly declared its need to focus on so-called EDTs, and identified seven science and technology areas that are of direct interest. Now, the NATO enterprise and representatives of its 30 member states have endorsed a strategy that shows how the alliance can both foster these technologies — through stronger relationships with innovation hubs and specific funding mechanisms — and protect EDT investments from outside influence and export issues.

NATO will eventually develop individual strategies for each of the seven science and technology areas — artificial intelligence, data and computing, autonomy, quantum-enabled technologies, biotechnology, hypersonic technology, and space. But for the near future, the priority is AI and data, said David van Weel, NATO’s assistant secretary general for emerging security challenges.

By the middle of the decade, the team from PsiQuantum will have a commercial quantum computer, according to the Financial Times. The founders are also indicating they are ready to emerge from stealth.

PsiQuantum has been mostly silent about its quantum computer development but with its scientific bench composed of leading UK physicists and nearly $300 million in venture capital funding, according to The Quantum Insider, that silence has been deafening.

When physicists need to understand the quantum mechanics that describe how atomic clocks work, how your magnet sticks to your refrigerator or how particles flow through a superconductor, they use quantum field theories.

When they work through problems in quantum field theories, they do so in “imaginary” time, then map those simulations into real quantities. But traditionally, these simulations nearly always include uncertainties or unknown factors that could cause equation results to be “off.” So, when physicists interpret their simulation results into real quantities, these uncertainties amplify exponentially, making it difficult to have confidence that their results are as accurate as necessary.

Now, a pair of University of Michigan physicists have discovered that a set of functions called the Nevanlinna functions can tighten the interpretation step, showing that physicists may be able to overcome one of the major limitations of modern quantum simulation. The work, published in Physical Review Letters, was led by U-M physics undergraduate student Jiani Fei.

Photosynthetic light-harvesting antennae transfer energy toward reaction centers with high efficiency, but in high light or oxidative environments, the antennae divert energy to protect the photosynthetic apparatus. For a decade, quantum effects driven by vibronic coupling, where electronic and vibrational states couple, have been suggested to explain the energy transfer efficiency, but questions remain whether quantum effects are merely consequences of molecular systems. Here, we show evidence that biology tunes interpigment vibronic coupling, indicating that the quantum mechanism is operative in the efficient transfer regime and exploited by evolution for photoprotection. Specifically, the Fenna–Matthews–Olson complex uses redox-active cysteine residues to tune the resonance between its excitons and a pigment vibration to steer excess excitation toward a quenching site.

Photosynthetic species evolved to protect their light-harvesting apparatus from photoxidative damage driven by intracellular redox conditions or environmental conditions. The Fenna–Matthews–Olson (FMO) pigment–protein complex from green sulfur bacteria exhibits redox-dependent quenching behavior partially due to two internal cysteine residues. Here, we show evidence that a photosynthetic complex exploits the quantum mechanics of vibronic mixing to activate an oxidative photoprotective mechanism. We use two-dimensional electronic spectroscopy (2DES) to capture energy transfer dynamics in wild-type and cysteine-deficient FMO mutant proteins under both reducing and oxidizing conditions. Under reducing conditions, we find equal energy transfer through the exciton 4–1 and 4–2–1 pathways because the exciton 4–1 energy gap is vibronically coupled with a bacteriochlorophyll-a vibrational mode.

Physicists exploring the quantum world watched the birth of a quasiparticle, shedding light on the strange behavior of these strange “fake particles.”