Lifeboat Foundation

This paper discusses the quantum mechanics of closed timelike curves (CTC) and of other potential methods for time travel. We analyze a specific proposal for such quantum time travel, the quantum description of CTCs based on post-selected teleportation (P-CTCs). We compare the theory of P-CTCs to previously proposed quantum theories of time travel: the theory is physically inequivalent to Deutsch’s theory of CTCs, but it is consistent with path-integral approaches (which are the best suited for analyzing quantum field theory in curved spacetime). We derive the dynamical equations that a chronology-respecting system interacting with a CTC will experience. We discuss the possibility of time travel in the absence of general relativistic closed timelike curves, and investigate the implications of P-CTCs for enhancing the power of computation.

Look past the details of a wonky discovery by a group of California scientists — that a quantum state is now observable with the human eye — and consider its implications: Time travel may be feasible. Doc Brown would be proud.

The strange discovery by quantum physicists at the University of California Santa Barbara means that an object you can see in front of you may exist simultaneously in a parallel universe — a multi-state condition that has scientists theorizing that traveling through time may be much more than just the plaything of science fiction writers.

And it’s all because of a tiny bit of metal — a “paddle” about the width of a human hair, an item that is incredibly small but still something you can see with the naked eye.

John Wheeler, who is mentor to many of today’s leading physicists, and the man who coined the term “black hole”, suggested that the nature of reality was revealed by the bizarre laws of quantum mechanics. According to the quantum theory, before the observation is made, a subatomic particle exists in several states, called a superposition (or, as Wheeler called it, a ‘smoky dragon’). Once the particle is observed, it instantaneously collapses into a single position (a process called ‘decoherence’).

The so-called “mirror-circuit” testing method will help scientists advance the technology behind these super powerful processors. https://bit.ly/3snkgR8

Quantum and biological systems are seldom discussed together as they seemingly demand opposing conditions. Life is complex, “hot and wet” whereas quantum objects are small, cold and well controlled. Here, we overcome this barrier with a tardigrade — a microscopic multicellular organism known to tolerate extreme physiochemical conditions via a latent state of life known as cryptobiosis. We observe coupling between the animal in cryptobiosis and a superconducting quantum bit and prepare a highly entangled state between this combined system and another qubit. The tardigrade itself is shown to be entangled with the remaining subsystems. The animal is then observed to return to its active form after 420 hours at sub 10 mK temperatures and pressure of $6\times 10^{-6}$ mbar, setting a new record for the conditions that a complex form of life can survive.

Created as an analogy for Quantum Electrodynamics (QED) — which describes the interactions due to the electromagnetic force carried by photons — Quantum Chromodynamics (QCD) is the theory of physics that explains the interactions mediated by the strong force — one of the four fundamental forces of nature.

A new collection of papers published in The European Physical Journal Special Topics and edited by Diogo Boito, Instituto de Fisica de Sao Carlos, Universidade de Sao Paulo, Brazil, and Irinel Caprini, Horia Hulubei National Institute for Physics and Nuclear Engineering, Bucharest, Romania, brings together recent developments in the investigation of QCD.

The editors explain in a special introduction to the collection that due to a much stronger coupling in the strong force — carried by gluons between quarks, forming the fundamental building blocks of matter — described by QCD, than the electromagnetic force, the divergence of perturbation expansions in the mathematical descriptions of a system can have important physical consequences. The editors point out that this has become increasingly relevant with recent high-precision calculations in QCD, due to advances in the so-called higher-order loop computations.

NTT, University of Tokyo and Riken aim for full-fledged system by 2030.

TOKYO — A Japanese team of scientists on Wednesday announced a key step in the development of a quantum computer using photons, or particles of light, that eliminates the need for an ultracold environment used to cool existing machines.

An international study shows which factors determine the speed limit for quantum computations.

Which factors determine how fast a quantum computer can perform its calculations? Physicists at the University of Bonn and the Technion – Israel Institute of Technology have devised an elegant experiment to answer this question. The results of the study are published in the journal Science Advances.

Quantum computers are highly sophisticated machines that rely on the principles of quantum mechanics to process information. This should enable them to handle certain problems in the future that are completely unsolvable for conventional computers. But even for quantum computers, fundamental limits apply to the amount of data they can process in a given time.

Scientists and institutions dedicate more resources each year to the discovery of novel materials to fuel the world. As natural resources diminish and the demand for higher value and advanced performance products grows, researchers have increasingly looked to nanomaterials.

Nanoparticles have already found their way into applications ranging from energy storage and conversion to quantum computing and therapeutics. But given the vast compositional and structural tunability nanochemistry enables, serial experimental approaches to identify new materials impose insurmountable limits on discovery.

Now, researchers at Northwestern University and the Toyota Research Institute (TRI) have successfully applied machine learning to guide the synthesis of new nanomaterials, eliminating barriers associated with materials discovery. The highly trained algorithm combed through a defined dataset to accurately predict new structures that could fuel processes in clean energy, chemical and automotive industries.