Lifeboat Foundation

Scientists have long studied the work of Subrahmanyan Chandrasekhar, the Indian-born American astrophysicist who won the Nobel Prize in 1983, but few know that his research on stellar and planetary dynamics owes a deep debt of gratitude to an almost forgotten woman: Donna DeEtte Elbert.

From 1948 to 1979, Elbert worked as a “computer” for Chandrasekhar, tirelessly devising and solving mathematical equations by hand. Though she shared authorship with the Nobel laureate on 18 papers and Chandrasekhar enthusiastically acknowledged her seminal contributions, her greatest achievement went unrecognized until a postdoctoral scholar at UCLA connected threads in Chandrasekhar’s work that all led back to Elbert.

Elbert’s achievement? Before anyone else, she predicted the conditions argued to be optimal for a planet or star to generate its own magnetic field, said the scholar, Susanne Horn, who has spent half a decade building on Elbert’s work.

How do you find novel materials with very specific properties—for example, special electronic properties which are needed for quantum computers? This is usually a very complicated task: various compounds are created, in which potentially promising atoms are arranged in certain crystal structures, and then the material is examined, for example in the low-temperature laboratory of TU Wien.

Now, a cooperation between Rice University (Texas), TU Wien and other international research institutions has succeeded in tracking down suitable materials on the computer. New theoretical methods are used to identify particularly promising candidates from the vast number of possible materials. Measurements at TU Wien have shown the materials do indeed have the required properties and the method works. This is an important step forward for research on quantum materials. The results have now been published in the journal Nature Physics.

For the memory prosthetic, the team focused on two specific regions: CA1 and CA3, which form a highly interconnected neural circuit. Decades of work in rodents, primates, and humans have pointed to this neural highway as the crux for encoding memories.

The team members, led by Drs. Dong Song from the University of Southern California and Robert Hampson at Wake Forest School of Medicine, are no strangers to memory prosthetics. With “memory bioengineer” Dr. Theodore Berger—who’s worked on hijacking the CA3-CA1 circuit for memory improvement for over three decades—the dream team had their first success in humans in 2015.

The central idea is simple: replicate the hippocampus’ signals with a digital replace ment. It’s no easy task. Unlike computer circuits, neural circuits are non-linear. This means that signals are often extremely noisy and overlap in time, which bolsters—or inhibits—neural signals. As Berger said at the time: “It’s a chaotic black box.”

Scientists simulated an organic molecule at the atomic scale. What does this mean for the future of quantum computing?

Parting the veil of mystery on quantum superposition using waves. Professor Phil Moriarty takes us through it.

Phil’s blogpost on the subject: https://muircheartblog.wpcomstaging.com/2021/10/26/superposi…erstition/

Continue reading “Superposition in Quantum Computers — Computerphile” »

Researchers at Toshiba Corporation have achieved a breakthrough in quantum computer architecture: the basic design for a double-transmon coupler that will improve the speed and accuracy of quantum computation in tunable couplers. The coupler is a key device in determining the performance of superconducting quantum computers.

Tunable couplers in a superconducting quantum computer link two qubits and perform quantum computations by turning on and off the coupling between them. Current technology can turn off the coupling of transmon qubits with close frequencies, but this is prone to crosstalk errors that occur on one of the qubits when the other qubit is irradiated with electromagnetic waves for control. In addition, current technology cannot completely turn off coupling for qubits with significantly different frequencies, resulting in errors due to residual coupling.

Toshiba has recently devised a double-transmon coupler that can completely turn on and off the coupling between qubits with significantly different frequencies. Completely turning on enables high-speed quantum computations with strong coupling, while completely turning off eliminates residual coupling, which improves quantum computation speeds and accuracy. Simulations with the new technology have shown it realizes two-qubit gates, basic operations in quantum computation, with an accuracy of 99.99% and a processing time of only 24 ns.

Solutions are needed early as thermal becomes a systems issue.

Heat has emerged as a major concern for semiconductors in every form factor, from digital watches to data centers, and it is becoming more of a problem at advanced nodes and in advanced packages where that heat is especially difficult to dissipate.

Temperatures at the base of finFETs and GAA FETs can differ from those at the top of the transistor structures. They also can vary depending on how devices are used, how often and where they are used, and by the diameter of the wires used in a particular design, or even a particular area of a chip or package. It’s not unusual for systems to throttle back performance because some circuits are running too hot.

Quantum Computers:

The Quantum computer is the next generation tech that works not with bits but with quantum bits (qubits) with optimized performance. Its working principle is based on the superposition i.e. Unlike the dualistic processing systems based on High and LOWs(0s), it can simultaneously be 1 and 0, or a mixture of both HIGH and LOW. Quantum computers are based on the laws of quantum mechanics to solve problems that are too complex for classical computers. Here are some key takeaways on how quantum computers assist DeFi to get boosted.

When quantum computers become powerful enough, they could theoretically crack the encryption algorithms that keep us safe. The race is on to find new ones.