Lifeboat Foundation

The same day Microsoft invested billions in OpenAI, McKinsey snatched up enterprise-focused AI firm Iguazio for a relative steal.

The consulting giant reportedly paid around $50 million for Iguazio, a Tel Aviv–based company offering an MLOps platform for large-scale businesses — “MLOps” refers to a set of tools to deploy and maintain machine learning models in production. In a press release, McKinsey says it plans to use the startup’s tech and team of 70 data scientists to bolster its QuantumBlack platform, McKinsey’s data analytics–focused group, with “industry-specific” AI solutions.

“We analyzed more than a 1,000 AI companies worldwide and identified Iguazio as the best fit to significantly accelerate our AI offering — from the initial concept to production, in a simplified, scalable and automated manner,” McKinsey senior partner Ben Ellencweig said in a statement. Over time, he added, the Iguazio and QuantumBlack teams will be fully integrated and work from a single product roadmap, combining the best of both worlds (with any luck).

Physicists at the University of Bonn have experimentally proven that an important theorem of statistical physics applies to so-called “Bose-Einstein condensates.” Their results now make it possible to measure certain properties of the quantum “superparticles” and deduce system characteristics that would otherwise be difficult to observe. The study has now been published in Physical Review Letters.

Suppose in front of you there is a container filled with an unknown liquid. Your goal is to find out by how much the particles in it (atoms or molecules) move back and forth randomly due to their thermal energy. However, you do not have a microscope with which you could visualize these position fluctuations known as “Brownian motion”.

It turns out you do not need that at all: You can also simply tie an object to a string and pull it through the liquid. The more force you have to apply, the more viscous your liquid. And the more viscous it is, the lesser the particles in the liquid change their position on average. The viscosity at a given temperature can therefore be used to predict the extent of the fluctuations.

A superconducting qubit-metamaterial system creates a scalable lattice quantum simulator.

Quantum materials are materials with unique electronic, magnetic or optical properties, which are underpinned by the behavior of electrons at a quantum mechanical level. Studies have showed that interactions between these materials and strong laser fields can elicit exotic electronic states.

In recent years, many physicists have been trying to elicit and better understand these exotic states, using different material platforms. A class of materials that was found to be particularly promising for studying some of these states are monolayer transition metal dichalcogenides.

Monolayer transition metal dichalcogenides are 2D materials that consist in single layers of atoms from a transition metal (e.g., tungsten or molybdenum) and a chalcogen (e.g., sulfur or selenium), which are arranged into a crystal lattice. These materials have been found to offer exciting opportunities for Floquet engineering (a technique to manipulate the properties of materials using lasers) of excitons (quasiparticle electron-hole correlated states).

Quantum-enhanced single-parameter estimation is an established capability, with non-classical probe states achieving precisions beyond what can be reached by the equivalent classical resources in photonic^1,2,3, trapped-ion^4,5, superconducting⁶ and atomic^7,8 systems. This has paved the way for quantum enhancements in practical sensing applications, from gravitational wave detection⁹ to biological imaging¹⁰. For single-parameter estimation, entangled probe states are sufficient to reach the ultimate allowed precisions. However, for multi-parameter estimation, owing to the possible incompatibility of different observables, entangling resources are also required at the measurement stage. The ultimate attainable limits in quantum multi-parameter estimation are set by the Holevo Cramér–Rao bound (Holevo bound)^11,12. In most practical scenarios, it is not feasible to reach the Holevo bound as this requires a collective measurement on infinitely many copies of the quantum state^13,14,15,16 (see Methods for a rigorous definition of collective measurements). Nevertheless, it is important to develop techniques that will enable the Holevo bound to be approached, given that multi-parameter estimation is fundamentally connected to the uncertainty principle¹⁷ and has many physically motivated applications, including simultaneously estimating phase and phase diffusion^18,19, quantum super-resolution^20,21, estimating the components of a three-dimensional field^22,23 and tracking chemical processes²⁴. Furthermore, as we demonstrate, collective measurements offer an avenue to quantum-enhanced sensing even in the presence of large amounts of decoherence, unlike the use of entangled probe states^25,26.

To date, collective measurements for quantum multi-parameter metrology have been demonstrated exclusively on optical systems^{27,28,29,30,31,32}. Contemporary approaches to collective measurements on optical systems are limited in their scalability: that is, it is difficult to generalize present approaches to measuring many copies of a quantum state simultaneously. The limited gate set available can also make it harder to implement an arbitrary optimal measurement. Indeed, the collective measurements demonstrated so far have all been restricted to measuring two copies of the quantum state and, while quantum enhancement has been observed, have all failed to reach the ultimate theoretical limits on separable measurements^33,34. Thus, there is a pressing need for a more versatile and scalable approach to implementing collective measurements.

In this work, we design and implement theoretically optimal collective measurement circuits on superconducting and trapped-ion platforms. The ease with which these devices can be reprogrammed, the universal gate set available and the number of modes across which entanglement can be generated, ensure that they avoid many of the issues that current optical systems suffer from. Using recently developed error mitigation techniques³⁵ we estimate qubit rotations about the axes of the Bloch sphere with a greater precision than what is allowed by separable measurements on individual qubits. This approach allows us to investigate several interesting physical phenomena: we demonstrate both optimal single-and two-copy collective measurements reaching the theoretical limits^33,34. We also implement a three-copy collective measurement as a first step towards surpassing two-copy measurements. However, due to the circuit complexity, this measurement performs worse than single-copy measurements. We investigate the connection between collective measurements and the uncertainty principle. Using two-copy collective measurements, we experimentally violate a metrological bound based on known, but restrictive uncertainty relations³⁶. Finally, we compare the metrological performance of quantum processors from different platforms, providing an indication of how future quantum metrology networks may look.

A specific neural network based on a representation of the wave function guided by the quantum mechanical variational principle alone without reference to experimental data predicts electronic ground states in condensed matter without a priori knowledge of the system.

We can frequently find in our daily lives a localized wave structure that maintains its shape upon propagation—picture a smoke ring flying in the air. Similar stable structures have been studied in various research fields and can be found in magnets, nuclear systems, and particle physics. In contrast to a ring of smoke, they can be made resilient to perturbations. This is known in mathematics and physics as topological protection.

A typical example is the nanoscale hurricane-like texture of a magnetic field in magnetic thin films, behaving as particles—that is, not changing their shape—called skyrmions. Similar doughnut-shaped (or toroidal) patterns in 3D space, visualizing complex spatial distributions of various properties of a wave, are called hopfions. Achieving such structures with light waves is very elusive.

Recent studies of structured light revealed strong spatial variations of polarization, phase, and amplitude, which enable the understanding of—and open up opportunities for designing—topologically stable optical structures behaving like particles. Such quasiparticles of light with control of diversified topological properties may have great potential, for example as next-generation information carriers for ultra-large-capacity optical information transfer, as well as in quantum technologies.

For the first time since it was proposed more than 80 years ago, scientists from Nanyang Technological University, Singapore (NTU Singapore) have demonstrated the phenomenon of “quantum recoil,” which describes how the particle nature of light has a major impact on electrons moving through materials. The research is published online today (January 19) in the journal Nature Photonics.

Making quantum recoil a practical reality should eventually allow businesses to more accurately produce X-rays of specific energy levels, leading to superior accuracy in healthcare and manufacturing applications such as medical imaging and flaw detection in semiconductor chips.

Quantum recoil was theorized by Russian physicist and Nobel laureate Vitaly Ginzburg in 1940 to accurately account for radiation emitted when charged particles like electrons move through a medium, such as water, or materials with repeated patterns on the surface, including those on butterfly wings and graphite.

By Chuck Brooks

The quantum computing decryption threat will be here soon enough, and it is time for businesses, organizations and governments to protect their data for that inevitability.