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Category: computing – Page 43

The Schrödinger Equation Gets Practical: New Quantum Tool Simulates the Physics of the Real World

Quantum computers have the potential to solve certain problems far more efficiently than classical computers. In a recent development, researchers have designed a quantum algorithm to simulate systems of coupled masses and springs, known as coupled oscillators. These systems are fundamental in modeling a wide range of physical phenomena, from molecules to mechanical structures like bridges.

To simulate these systems, the researchers first translated the behavior of the coupled oscillators into a form of the Schrödinger equation, which describes how the quantum state of a system evolves over time. They then used advanced Hamiltonian simulation techniques to model the system on a quantum computer.

Hamiltonian methods provide a framework for understanding how physical systems evolve, connecting principles of classical mechanics with those of quantum mechanics. By leveraging these techniques, the researchers were able to represent the dynamics of N coupled oscillators using only about log(N) quantum bits (qubits), a significant reduction compared to the resources required by classical simulations.

Thomas Polger “The Puzzling Resilience of Multiple Realization” (#DDLS)

This talk by Professor Thomas Polger (Professor of Philosophy at the Department of Philosophy University of Cincinnati) was given on Thursday 24 March 2022 as part of the Dutch Distinguished Lecture Series in Philosophy and Neuroscience (#DDLS).

Title:
Thomas Polger “The Puzzling Resilience of Multiple Realization” (#DDLS).

Caption.

Abstract.

According to the multiple realization argument, mental states or processes can be realized in diverse and heterogeneous physical systems; and that fact implies that mental states or processes can not be identified with any one particular kind of physical state or process. In particular, mental processes can not be identified with of brain processes. Moreover, the argument provides a general model for the autonomy of the “special” sciences. The multiple realization argument is widely influential. But over the last thirty years it has also faced serious objections. Despite those objections, most philosophers regard that fact of multiple realization and the cogency of the multiple realization argument as obviously correct. Why is that? What is it about the multiple realization argument that makes it so resilient? One reason that the multiple realization argument is deeply intertwined with a view that minds are, in some sense, computational. But I argue that the sense in which minds are computational does not support the conclusion that they are obviously multiply realized. I argue that the sense in which brains compute does not imply that brains implement computational processes that are multiply realizable, and it does not provide a general model for the autonomy of the special sciences.

Computational Theory of Mind

The mind is a lot like a computer — but what if this metaphor was more than just a metaphor? According to the philosopher Andy Clark, human minds aren’t just like computers, human minds are computers! In this video, we’ll get into the consequences of this seemingly radical framework and what it means for cognitive science as a whole.

0:00 — Intro.
1:09 — The conceivability argument.
2:17 — Behaviorism revisited.
5:14 — Identity theory.
7:54 — Functionalism revisited.
8:56 — Computational theory of mind.
12:09 — Formal systems.
13:26 — Games.
15:20 — Language.
17:19 — Wrapping up.
18:55 — Key concepts.

Scientists Are Mapping the Boundaries of What Is Knowable and Unknowable

“I give you God’s view,” said Toby Cubitt, a physicist turned computer scientist at University College London and part of the vanguard of the current charge into the unknowable, and “you still can’t predict what it’s going to do.”

Eva Miranda, a mathematician at the Polytechnic University of Catalonia (UPC) in Spain, calls undecidability a “next-level chaotic thing.”

Undecidability means that certain questions simply cannot be answered. It’s an unfamiliar message for physicists, but it’s one that mathematicians and computer scientists know well. More than a century ago, they rigorously established that there are mathematical questions that can never be answered, true statements that can never be proved. Now physicists are connecting those unknowable mathematical systems with an increasing number of physical ones and thereby beginning to map out the hard boundary of knowability in their field as well.

Computational platform helps unlock A20’s dual role in cancer immunotherapy

There is an urgent need for precision immunotherapy strategies that simultaneously target both tumor cells and immune cells to enhance treatment efficacy. Identifying genes with dual functions in both cancer and immune cells opens new possibilities for overcoming tumor resistance and improving patient survival.

Professor Zeng Zexian’s team from the Center for Quantitative Biology at the Peking University Academy for Advanced Interdisciplinary Studies, in collaboration with the Peking University-Tsinghua University Joint Center for Life Sciences, has developed ICRAFT, an innovative computational platform for identifying cancer targets. Their study has been published in Immunity.

ICRAFT integrates 558 CRISPR screening datasets, 2 million single-cell RNA sequencing datasets, and 943 RNA-Seq datasets from clinical immunotherapy samples.

Microchip Magic: Twisted Crystals Unleash a New Era in Light Control

A new on-chip sensor using twisted moiré photonic crystals can precisely tune light properties in real time. This could replace bulky optical systems with one compact, powerful chip. Twisted moiré photonic crystals — a cutting-edge type of optical metamaterial — hold significant promise for build

Mechanistic understanding could enable better fast-charging batteries

Fast-charging lithium-ion batteries are ubiquitous, powering everything from cellphones and laptops to electric vehicles. They’re also notorious for overheating or catching fire.

Now, with an innovative computational model, a University of Wisconsin–Madison has gained new understanding of a phenomenon that causes lithium-ion batteries to fail.

Developed by Weiyu Li, an assistant professor of mechanical engineering at UW–Madison, the model explains lithium plating, in which fast charging triggers metallic lithium to build up on the surface of a battery’s anode, causing the battery to degrade faster or catch fire.