Lifeboat Foundation

Quantum sensing represents one of the most promising applications of quantum technologies, with the aim of using quantum resources to improve measurement sensitivity. In particular, sensing of optical phases is one of the most investigated problems, considered key to developing mass-produced technological devices.

Optimal usage of quantum sensors requires regular characterization and calibration. In general, such calibration is an extremely complex and resource-intensive task—especially when considering systems for estimating multiple parameters, due to the sheer volume of required measurements as well as the computational time needed to analyze those measurements. Machine-learning algorithms present a powerful tool to address that complexity. The discovery of suitable protocols for algorithm usage is vital for the development of sensors for precise quantum-enhanced measurements.

A particular type of machine-learning algorithm known as “reinforcement learning” (RL) relies on an intelligent agent guided by rewards: Depending on the rewards it receives, it learns to perform the right actions to achieve the desired optimization. The first experimental realizations using RL algorithms for the optimization of quantum problems have been reported only very recently. Most of them still rely on prior knowledge of the model describing the system. What is desirable is instead a completely model-free approach, which is possible when the agent’s reward does not depend on the explicit system model.

Quantum simulations of the hydroxide anion and hydroxyl radical are reported, employing variational quantum algorithms for near-term quantum devices. The energy of each species is calculated along the dissociation curve, to obtain information about the stability of the molecular species being investigated. It is shown that simulations restricted to valence spaces incorrectly predict the hydroxyl radical to be more stable than the hydroxide anion. Inclusion of dynamical electron correlation from nonvalence orbitals is demonstrated, through the integration of the variational quantum eigensolver and quantum subspace expansion methods in the workflow of N-electron valence perturbation theory, and shown to correctly predict the hydroxide anion to be more stable than the hydroxyl radical, provided that basis sets with diffuse orbitals are also employed.

Individual atoms trapped by optical ‘tweezers’ are emerging as a promising computational platform.

This life of yours which you are living is not merely a piece of the entire existence, but is in a certain sense the whole.

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Continue reading “Brian Greene: Quantum Gravity, The Big Bang, Aliens, Death, and Meaning | Lex Fridman Podcast #232” »

Architectures based on artificial neural networks (ANNs) have proved to be very helpful in research settings, as they can quickly analyze vast amounts of data and make accurate predictions. In 2020, Google’s British AI subsidiary DeepMind used a new ANN architecture dubbed the Fermionic neural network (FermiNet) to solve the Schrodinger equation for electrons in molecules, a central problem in the field of chemistry.

The Schroedinger equation is a partial differential equation based on well-established theory of energy conservation, which can be used to derive information about the behavior of electrons and solve problems related to the properties of matter. Using FermiNet, which is a conceptually simple method, DeepMind could solve this equation in the context of chemistry, attaining very accurate results that were comparable to those obtained using highly sophisticated quantum chemistry techniques.

Researchers at Imperial College London, DeepMind, Lancaster University, and University of Oxford recently adapted the FermiNet architecture to tackle a quantum physics problem. In their paper, published in Physical Review Letters, they specifically used FermiNet to calculate the ground states of periodic Hamiltonians and study the homogenous electron gas (HEG), a simplified quantum mechanical model of electrons interacting in solids.

Over eighty years ago, Rabi oscillations were proposed to describe the strong coupling and population transfer in a two-level quantum system exposed to an oscillatory driving field. As compared to atoms, molecules have an extra degree of vibration, which adds an additional knob to the Rabi oscillations in light-molecule interactions. However, how such a laser-driven Rabi oscillation during the stretching of molecular bonds determines the kinetic energy release (KER) spectrum of dissociative fragments is still an open question.

In a new article published in Light: Science & Applications, a joint team of scientists, led by Professor Feng He from Shanghai Jiao Tong University and Professor Jian Wu from East China Normal University has investigated Rabi oscillations in a stretching molecule and discovered the strong-field-induced dissociation dynamics beyond the well-accepted resonant one-photon dissociation scenario. During the dissociation of the simplest molecular ion of H₂⁺, coupled with the laser field, the electron hops between the 1sσ_g and 2pσ_u states, forming the Rabi oscillations.

The ionization-created nuclear wave packet (NWP) may propagate alternatively along the two potential energy curves towards a larger internuclear distance monotonically, termed as the rolling process, or may propagate outwards along the 2pσ_u curve followed by the inward propagation in the 1sσ_g curve and then be relaunched to 2pσ_u state again followed by subsequent dissociation, termed as the looping process. The rolling and looping dissociation pathways lead to different KERs of the ejected dissociative fragments, which have been verified by comparing experimental measurements with quantum simulation results.

The emerging quantum technology industry offers a dynamic career pathway for creative and adaptable physical scientists, as Stuart Woods of Oxford Instruments NanoScience explains.

As quantum technology companies shift gears to translate their applied research endeavours into commercial opportunities – at scale – they’re going to need ready access to a skilled and diverse quantum workforce of “all the talents”. A case study in this regard is Oxford Instruments NanoScience, a division of parent group Oxford Instruments, the long-established UK provider of specialist technologies and services to research and industry.

The NanoScience business unit, for its part, designs and manufactures research tools to support the development, scale-up and commercialization of next-generation quantum technologies. Think cryogenic systems (operating at temperatures as low as 5 mK) and high-performance magnets that enable researchers to harness the exotic properties of quantum mechanics – entanglement, tunnelling, superposition and the like – to yield practical applications in quantum computing, quantum communications, quantum metrology and quantum imaging.

The michael shermer show # 294

What is time? Does the past still exist? How did the universe begin and how will it end? Do particles think? Was the universe made for us? Why doesn’t anyone ever get younger? Has physics ruled out free will? Will we ever have a theory of everything? According to Sabine Hossenfelder, it is not a coincidence that quantum entanglement and vacuum energy have become the go-to explanations of alternative healers, or that people believe their deceased grandmother is still alive because of quantum mechanics. Science and religion have the same roots, and they still tackle some of the same questions: Where do we come from? Where do we go to? How much can we know? The area of science that is closest to answering these questions is physics. Over the last century, physicists have learned a lot about which spiritual ideas are still compatible with the laws of nature. Not always, though, have they stayed on the scientific side of the debate.

Continue reading “Free Will and Determinism from a Physicist’s Perspective (Sabine Hossenfelder)” »