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It is still unclear whether and how quantum computing might prove useful in solving known large-scale classical machine learning problems. Here, the authors show that variants of known quantum algorithms for solving differential equations can provide an advantage in solving some instances of stochastic gradient descent dynamics.

Before delving into the prospects of the Fifth Industrial Revolution, let’s reflect on the legacy of its predecessor. The Fourth Industrial Revolution, characterised by the fusion of digital, physical, and biological systems, has already transformed the way we live and work. It brought us AI, blockchain, the Internet of Things, and more. However, it also raised concerns about automation’s impact on employment and privacy, leaving us with a mixed legacy.

The promise of the Fifth Industrial Revolution.

The Fifth Industrial Revolution represents a quantum leap forward. At its core, it combines AI, advanced biotechnology, nanotechnology, and quantum computing to usher in a new era of possibilities. One of its most compelling promises is the extension of human life. With breakthroughs in genetic engineering, regenerative medicine, and AI-driven healthcare, we are inching closer to not just treating diseases but preventing them altogether. It’s a vision where aging is not an inevitability, but a challenge to overcome.

The Quantum Insider (TQI) is the leading online resource dedicated exclusively to Quantum Computing.

A new study in Physical Review Letters illuminates the intricacies of energy exchanges within bipartite quantum systems, offering profound insights into quantum coherence, pure dephasing effects, and the potential impact on future quantum technologies.

In quantum systems, the behavior of particles and energy transfer are governed by probability distributions and wave functions, adding layers of complexity to the understanding of energy exchanges.

The exploration of energy exchanges in quantum systems inherently involves tackling the complexities arising from quantum decoherence and the scales at which quantum systems operate, introducing sensitivity.

A study led by the University of Oxford has used the power of machine learning to overcome a key challenge affecting quantum devices. For the first time, the findings reveal a way to close the “reality gap”: the difference between predicted and observed behavior from quantum devices. The results have been published in Physical Review X.

Quantum computing could supercharge a wealth of applications, from climate modeling and financial forecasting to drug discovery and artificial intelligence. But this will require effective ways to scale and combine individual quantum devices (also called qubits). A major barrier against this is inherent variability, where even apparently identical units exhibit different behaviors.

Functional variability is presumed to be caused by nanoscale imperfections in the materials from which quantum devices are made. Since there is no way to measure these directly, this internal disorder cannot be captured in simulations, leading to the gap in predicted and observed outcomes.

Scientists overcame a limitation in graphene to harness the material as a working semiconductor at terahertz frequencies with 10 times the mobility of silicon.

Although chaos theory can solve nearly anything that is unknown I basically think that in an infinite universe as made real from the infinite microchip that uses superfluid processing power is the real answer and we are off by factor of infinite parameters still.

When we look at scientific progress, especially in physics, it can seem like all the great discoveries lie behind us. Since the revolutions of Einstein’s theory of relativity and quantum mechanics, physicists have been struggling to find a way to make them fit together with little to no success. Tim Palmer argues that the answer to this stalemate lies in chaos theory.

Continue reading “Chaos theory and the end of physics” »

The first functional semiconductor made from graphene has been created at the Georgia Institute of Technology. This could enable smaller and faster electronic devices and may have applications for quantum computing.

Credit: Georgia Institute of Technology.

Continue reading “First functional semiconductor made from graphene” »

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