Nature Physics - Digital braiding of Fibonacci anyons (original) (raw)

Editorial

PhD students can face many challenges, such as a lack of confidence in their newly acquired skills or the uncertainty about which career path to choose. We highlight some ways to empower students in their doctoral journey.
Editorial 12 Sept 2024 Top of page ⤴

Thesis

Top of page ⤴

News & Views

Creating entangled photon pairs often requires intense excitation of nonlinear materials or the active manipulation of quantum devices. Now, entanglement between two photons has been created by scattering a laser off a passive quantum dot.

Nonlinearity is crucial for sophisticated tasks in machine learning but is often difficult to engineer outside of electronics. By encoding the inputs in parameters of the system, linear systems can realize efficiently trainable nonlinear computations.

Acoustic and optical traps enable contactless manipulation of objects, but trapping has been limited to homogeneous environments. Wavefront shaping now extends this versatile manipulation tool to dynamic and disordered media.

Acoustic resonators are a promising candidate for making quantum computers scalable. Coupled to a qubit, they have now produced squeezed mechanical states, demonstrating that they can implement a large variety of quantum algorithms.

Recent experimental claims of quantum advantage rely on the absence of classical algorithms that can reproduce the results. A tensor network algorithm can now challenge recent optical quantum advantage experiments.

Topological quantum computers are predicted to perform calculations by manipulating quasiparticles known as non-Abelian anyons. A type of non-Abelian anyon that supports universal quantum gates has now been simulated using superconducting qubits.

Experiments show that the shape of a biofilm, not just its cell doubling time, significantly impacts its expansion rate. This insight could guide new strategies for controlling biofilm growth.

Research Briefings

Rhombohedral graphene is an emerging material with a rich correlated-electron phenomenology, including superconductivity. The magnetism of symmetry-broken trilayer graphene has now been explored, revealing important details of the physics and providing a roadmap for broader explorations of rhombohedral graphene.
Research Briefing 08 Jul 2024

An improved optimization algorithm enables the training of large-scale neural quantum states in which the enormous number of neuron connections capture the intricate complexity of quantum many-body wavefunctions. This advance leads to unprecedented accuracy in paradigmatic quantum models, opening up new avenues for simulating and understanding complex quantum phenomena.
Research Briefing 09 Jul 2024 Top of page ⤴

Perspectives

Optical near-field microscopy has facilitated our understanding of nanophotonics. This Perspective explores the opportunities that near-field studies of terahertz fields provide for ultrafast phase transitions in condensed matter systems.

Articles

The complexity of a many-body quantum state grows exponentially with system size, hindering numerical studies. A unitary flow-based method now enables accurate estimates of long-term properties of one- and two-dimensional quantum systems.

As the energy consumption of neural networks continues to grow, different approaches to deep learning are needed. A neuromorphic method offering nonlinear computation based on linear wave scattering can be implemented using integrated photonics.

Mechanical modes promise applications in continuous-variable quantum information processing, but only if the final two elements—squeezing and nonlinearity—are achieved. Experiments with an oscillator coupled to a transmon qubit now achieve this.

The growth of a biofilm—a bacterial colony attached to a surface—is governed by a trade-off between horizontal and vertical expansion. Now, it is shown that this process significantly depends on the contact angle at the biofilm’s edge.

Measure for Measure

The Fisher information imposes a fundamental limit on the precision with which an unknown parameter can be estimated from noisy data, as Dorian Bouchet explains.