Lifeboat Foundation

Programming the Universe: A Quantum Computer Scientist Takes on the Cosmos. Seth Lloyd. xii + 221 pp. Alfred A. Knopf, 2006. $25.95.

In the 1940s, computer pioneer Konrad Zuse began to speculate that the universe might be nothing but a giant computer continually executing formal rules to compute its own evolution. He published the first paper on this radical idea in 1967, and since then it has provoked an ever-increasing response from popular culture (the film The Matrix, for example, owes a great deal to Zuse’s theories) and hard science alike.

The manifestation of the quantum gravitational field becomes evident when observing phenomena at the Planck’s length scale. Hence, the tiniest constituent of our cosmos, which is not even perceptible, must exist at the scale of Planck’s length.

Black holes are renowned and frightening phenomena—areas characterized by infinite gravitational force, rendering escape impossible. The process of forming a black hole is relatively uncomplicated: it involves compressing a sufficient amount of mass below a specific size threshold. Once this threshold is surpassed, gravity prevails over all other forces, resulting in the creation of a black hole.

The critical threshold varies depending on the quantity of mass being condensed. For an average human, this threshold is comparable to the size of an atomic nucleus. Conversely, for the Earth, compressing its entirety into the volume of a chickpea would generate a black hole of comparable size. Similarly, for a typical star with several times the mass of the Sun, the resulting black hole would span a few miles—a dimension akin to an average city.

Interestingly, amalgamating all the matter in the universe in an attempt to create the largest possible black hole would yield a black hole roughly the size of the universe itself.

🔗 Top quark and top antiquark entanglement 🔗

The CMS experiment has just reported the observation and confirms the existence of #entanglement between the top #quark and its #Antiparticle beyond reasonable doubt.

The CMS experiment has just reported the observation of quantum entanglement between a top quark and a top antiquark, simultaneously produced at the LHC.

Continue reading “Entangled Titans: unraveling the mysteries of Quantum Mechanics with top quarks” »

Quantum cognition is a new research program that uses mathematical principles from quantum theory as a framework to explain human cognition, including judgment and decision making, concepts, reasoning, memory, and perception. This research is not concerned with whether the brain is a quantum computer. Instead, it uses quantum theory as a fresh conceptual framework and a coherent set of formal tools for explaining puzzling empirical findings in psychology. In this introduction, we focus on two quantum principles as examples to show why quantum cognition is an appealing new theoretical direction for psychology: complementarity, which suggests that some psychological measures have to be made sequentially and that the context generated by the first measure can influence responses to the next one, producing measurement order effects, and superposition, which suggests that some psychological states cannot be defined with respect to definite values but, instead, that all possible values within the superposition have some potential for being expressed. We present evidence showing how these two principles work together to provide a coherent explanation for many divergent and puzzling phenomena in psychology. (PsycInfo Database Record © 2020 APA, all rights reserved)

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This second position, while certainly not inconsistent with realism per se, turns upon a distinction involving a notion of “observation”, “measurement”, “test”, or something of this sort—a notion that realists are often at pains to avoid in connection with fundamental physical theory. Of course, any realist account of a statistical physical theory such as quantum mechanics will ultimately have to render up some explanation of how measurements are supposed to take place. That is, it will have to give an account of which physical interactions between “object” and “probe” systems count as measurements, and of how these interactions cause the probe system to evolve into final “outcome-states” that correspond to—and have the same probabilities as—the outcomes predicted by the theory. This is the notorious measurement problem.

In fact, Putnam advanced his version of quantum-logical realism as offering a (radical) dissolution of the measurement problem: According to Putnam, the measurement problem (and indeed every other quantum-mechanical “paradox”) arises through an improper application of the distributive law, and hence disappears once this is recognized. This proposal, however, is widely regarded as mistaken.^[4]

As mentioned above, realist interpretations of quantum mechanics must be careful in how they construe the phrase “the observable A” A A has a value in the set B” B B”. The simplest and most traditional proposal—often dubbed the “eigenstate-eigenvalue link” (Fine [1973])—is that (• holds if and only if a measurement of A” A A yields a value in the set B” B B with certainty, i.e., with (quantum-mechanical!) probability 1. While this certainly gives a realist interpretation of (•,^[5] it does not provide a solution to the measurement problem. Indeed, we can use it to give a sharp formulation of that problem: even though A” A A is certain to yield a value in B” B B when measured, unless the quantum state is an eigenstate of the measured observable A” A A, the system does not possess any categorical property corresponding to A” A A ’s having a specific value in the set B” B B.

Humans can sometimes be hard to understand, much like quantum physics — unless you watch this channel regularly of course. That’s why a mathematician has come out with an idea of “quantum cognition”. What is this so-called quantum cognition? Does it explain why humans make irrational decisions? Let’s have a look.

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Continue reading “Quantum Cognition: Does it explain irrational decisions?” »

Combinatorial optimization problems (COPs) have applications in many different fields such as logistics, supply chain management, machine learning, material design and drug discovery, among others, for finding the optimal solution to complex problems. These problems are usually very computationally intensive using classical computers and thus solving COPs using quantum computers has attracted significant attention from both academia and industry.