Lifeboat Foundation

Researchers have achieved long-distance entanglement between two calcium ions, each of which lies in a different building, showing that trapped ions could be used to create quantum networks.

Among the many candidate platforms for quantum-information applications, trapped-ion qubits are promising because of their long coherence times and their potential for multiqubit operations (see Viewpoint: Trapped Ions Make Impeccable Qubits). Alone, those properties are insufficient for some quantum applications, however: to build quantum communication networks, for example, requires the qubits’ delicate quantum states be shared over long distances. Demonstrations of this ability have been lacking for trapped-ion systems. Now a team led by Benjamin Lanyon at the Institute for Quantum Optics and Quantum Information, Austria, and Tracy Northup at the University of Innsbruck, Austria, have addressed this shortfall by entangling two trapped-ion qubits residing in different buildings [1].

Lanyon, Northup, and colleagues used trapped-ion qubits inside optical cavities. For each qubit, they excited the ion using a dual-wavelength laser, prompting the ion to emit a single photon. The photon’s polarization depended on which of the two laser wavelengths the ion absorbed, entangling the photon with the ion’s final state. To entangle the two ions, the team then transmitted the photon from one ion through 510 m of optical fiber to a beam splitter near the other ion, where the two photons interacted. The researchers claimed successful entanglement when they subsequently detected a pair of photons with specific individual polarizations.

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.

Brian Greene is a theoretical physicist. Please support this podcast by checking out our sponsors:
- The Prisoner Wine Company: https://theprisonerwine.com/lex to get 20% off & free shipping.
- Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium.
- LMNT: https://drinkLMNT.com/lex to get free sample pack.
- BetterHelp: https://betterhelp.com/lex to get 10% off.
- NI: https://www.ni.com/perspectives.

EPISODE LINKS:
Brian’s Twitter: https://twitter.com/bgreene.
Brian’s Website: http://www.briangreene.org/
Until the End of Time (book): https://amzn.to/2XuqXUi.

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.