Lifeboat Foundation

We are aware of how skewed our perception of reality is. How we see the world is shaped by our senses, our societies, and our knowledge.

And you may want to rethink your belief that science will always provide you with objective reality.

Physicists can now verify a hypothesis that Nobel Prize winner Eugen Wigner initially put out in 1961.

PhonlamaiPhoto/iStock.

The Hubbard Model

If you’ve ever tried to carry on a conversation in a noisy room, you’ll be able to relate to the scientists and engineers trying to “hear” the signals from experimental quantum computing devices called qubits. These basic units of quantum computers are early in their development and remain temperamental, subject to all manner of interference. Stray “noise” can masquerade as a functioning qubit or even render it inoperable.

That’s why physicist Christian Boutan and his Pacific Northwest National Laboratory (PNNL) colleagues were in celebration mode recently as they showed off PNNL’s first functional superconducting qubit. It’s not much to look at. Its case—the size of a pack of chewing gum—is connected to wires that transmit signals to a nearby panel of custom radiofrequency receivers. But most important, it’s nestled within a shiny gold cocoon called a dilution refrigerator and shielded from stray electrical signals. When the refrigerator is running, it is among the coldest places on Earth, so very close to absolute zero, less than 6 millikelvin (about −460 degrees F).

The extreme cold and isolation transform the sensitive superconducting device into a functional qubit and slow down the movement of atoms that would destroy the qubit state. Then, the researchers listen for a characteristic signal, a blip on their radiofrequency receivers. The blip is akin to radar signals that the military uses to detect the presence of aircraft. Just as traditional radar systems transmit radio waves and then listen for returning waves, the physicists at PNNL have used a low-temperature detection technique to “hear” the presence of a qubit by broadcasting carefully crafted signals and decoding the returning message.

Neural networks are learning algorithms that approximate the solution to a task by training with available data. However, it is usually unclear how exactly they accomplish this. Two young Basel physicists have now derived mathematical expressions that allow one to calculate the optimal solution without training a network. Their results not only give insight into how those learning algorithms work, but could also help to detect unknown phase transitions in physical systems in the future.

Neural networks are based on the principle of operation of the brain. Such computer algorithms learn to solve problems through repeated training and can, for example, distinguish objects or process spoken language.

For several years now, physicists have been trying to use neural networks to detect phase transitions as well. Phase transitions are familiar to us from everyday experience, for instance when water freezes to ice, but they also occur in more complex form between different phases of magnetic materials or quantum systems, where they are often difficult to detect.

Two milliseconds—or two thousandths of a second—is an extraordinarily long time in the world of quantum computing. On these timescales the blink of an eye—at one 10th of a second—is like an eternity.

Now a team of researchers at UNSW Sydney has broken new ground in proving that ‘spin qubits’—properties of electrons representing the basic units of information in quantum computers—can hold information for up to two milliseconds. Known as ‘coherence time’, the duration of time that qubits can be manipulated in increasingly complicated calculations, the achievement is 100 times longer than previous benchmarks in the same quantum processor.

Continue reading “For the longest time: Quantum computing engineers set new standard in silicon chip performance” »

This year’s Breakthrough Prize in Life Sciences has a strong physical sciences element. The prize was divided between six individuals. Demis Hassabis and John Jumper of the London-based AI company DeepMind were awarded a third of the prize for developing AlphaFold, a machine-learning algorithm that can accurately predict the 3D structure of proteins from just the amino-acid sequence of their polypeptide chain. Emmanuel Mignot of Stanford University School of Medicine and Masashi Yanagisawa of the University of Tsukuba, Japan, were awarded for their work on the sleeping disorder narcolepsy.

The remainder of the prize went to Clifford Brangwynne of Princeton University and Anthony Hyman of the Max Planck Institute of Molecular Cell Biology and Genetics in Germany for discovering that the molecular machinery within a cell—proteins and RNA—organizes by phase separating into liquid droplets. This phase separation process has since been shown to be involved in several basic cellular functions, including gene expression, protein synthesis and storage, and stress responses.

The award for Brangwynne and Hyman shows “the transformative role that the physics of soft matter and the physics of polymers can play in cell biology,” says Rohit Pappu, a biophysicist and bioengineer at Washington University in St. Louis. “[The discovery] could only have happened the way it did: a creative young physicist working with an imaginative cell biologist in an ecosystem where boundaries were always being pushed at the intersection of multiple disciplines.”

Researchers have calculated the likelihood that a quantum state will decay when its evolution is inhibited by a dearth of final states.

Quantum systems are fragile, meaning a specific quantum state generally decays into other states over time. This decay process is formalized by Fermi’s golden rule (FGR), which in its traditional formalization applies when there exists an infinite continuum of states for the quantum system state to decay to—for example, when an excited atom emits a photon into a vacuum. Now Tobias Micklitz at the Brazilian Center for Research in Physics and colleagues have developed and solved a model showing how a quantum system evolves when its initial state is instead coupled to a finite set of states spread across discrete energy levels [1]. Micklitz says that their model could be the foundation for models of more complex, many-body quantum systems.

FGR-obeying systems occupy one end of a scale, where the coupling strength between the systems’ initial and final states is large relative to the energy gap between the various final states (zero for a continuum of states). At the other end of the scale, the coupling strength is much lower relative to this gap. A system that sits in this second regime remains in its initial state, as there are too few available final states for it to decay into.

The concept of “symmetry” is essential to fundamental physics: a crucial element in everything from subatomic particles to macroscopic crystals. Accordingly, a lack of symmetry—or asymmetry—can drastically affect the properties of a given system.

Qubits, the quantum analog of computer bits for quantum computers, are extremely sensitive—the barest disturbance in a qubit system is enough for it to lose any quantum information it might have carried. Given this fragility, it seems intuitive that qubits would be most stable in a symmetric environment. However, for a certain type of qubit—a molecular qubit—the opposite is true.

Researchers from the University of Chicago’s Pritzker School of Molecular Engineering (PME), the University of Glasgow, and the Massachusetts Institute of Technology have found that molecular qubits are much more stable in an asymmetric environment, expanding the possible applications of such qubits, especially as biological quantum sensors.

Researchers at QuTech—a collaboration between the Delft University of Technology and TNO—have engineered a record number of six, silicon-based, spin qubits in a fully interoperable array. Importantly, the qubits can be operated with a low error-rate that is achieved with a new chip design, an automated calibration procedure, and new methods for qubit initialization and readout. These advances will contribute to a scalable quantum computer based on silicon. The results are published in Nature today.

Different materials can be used to produce qubits, the quantum analog to the bit of the classical computer, but no one knows which material will turn out to be best to build a large-scale quantum computer. To date there have only been smaller demonstrations of silicon quantum chips with high quality qubit operations. Now, researchers from QuTech, led by Prof. Lieven Vandersypen, have produced a six qubit chip in silicon that operates with low error-rates. This is a major step towards a fault-tolerant quantum computer using silicon.

To make the qubits, individual electrons are placed in a linear array of six “quantum dots” spaced 90 nanometers apart. The array of quantum dots is made in a silicon chip with structures that closely resemble the transistor—a common component in every computer chip. A quantum mechanical property called spin is used to define a qubit with its orientation defining the 0 or 1 logical state. The team used finely-tuned microwave radiation, magnetic fields, and electric potentials to control and measure the spin of individual electrons and make them interact with each other.