Nonreciprocity-the ability to transmit signals in one direction while blocking them in the reverse-has become a powerful resource in quantum technologies, enabling directional amplification, routing of quantum information, and topologically protected quantum states. Recent experimental advances have demonstrated nonreciprocal behavior in low-loss, fully integrated devices operating with weak or no magnetic bias, enabled by synthetic gauge fields, optomechanical interactions, and chiral light-matter coupling. These achievements overcome the limitations of more traditional approaches, making nonreciprocity compatible with superconducting circuits and scalable quantum photonic architectures as well as an integral part of the next generation of modular quantum computers, distributed quantum networks, and precision metrology. Here we highlight the key concepts for engineering nonreciprocity in quantum systems and describe how this functionality can be employed for high-fidelity qubit readout, robust quantum state transfer, and boosting the sensitivity of quantum sensors.

Wave transport devices, such as amplifiers, frequency converters, and nonreciprocal devices, are essential for modern communication, signal processing, and sensing applications. Of particular interest are traveling wave setups, which offer excellent gain and bandwidth properties. So far, the conceptual design of those devices has relied on human ingenuity. This makes it difficult and time-consuming to explore the full design space under a variety of constraints and target functionalities. In our work, we present a method which automates this challenge. By optimizing the discrete and continuous parameters of periodic coupled-mode lattices, our approach identifies the simplest lattices that achieve the target transport functionality, and we apply it to discover new schemes for directional amplifiers, isolators, and frequency demultiplexers. Leveraging automated symbolic regression tools, we find closed analytical expressions that facilitate the discovery of generalizable construction rules. Moreover, we utilize important conceptual connections between the device transport properties and non-Hermitian topology. The resulting structures can be implemented on a variety of platforms, including microwave, optical, and optomechanical systems. Our approach opens the door to extensions like the artificial discovery of lattice models with desired properties in higher dimensions or with nonlinear interactions.

In optics and photonics, a small number of building blocks—like resonators, waveguides, arbitrary couplings, and parametric interactions—allow the design of a broad variety of devices and functionalities, distinguished by their scattering properties. These devices include transducers, amplifiers, and nonreciprocal devices, like isolators or circulators. Usually, the design of such a system is handcrafted by an experienced scientist in a time-consuming process, where it remains uncertain whether the simplest possibility has indeed been found. In our work, we develop the discovery algorithm autoscatter, which automates this challenge. By optimizing the continuous and discrete system properties, our automated search identifies the minimal resources required to realize the requested scattering behavior. In the spirit of artificial scientific discovery, it produces a complete list of interpretable solutions and leads to generalizable insights, as we illustrate in several examples. autoscatter opens the door towards the automated discovery of scattering setups for photonics, microwaves, and optomechanics, with possible future extensions to periodic structures, sensing, and electronic devices.

We introduce a general method to engineer arbitrary Hamiltonians in the Floquet phase space of a periodically driven oscillator, based on the noncommutative Fourier transformation technique. We establish the relationship between an arbitrary target Floquet Hamiltonian in phase space and the periodic driving potential in real space. We obtain analytical expressions for the driving potentials in real space that can generate novel Hamiltonians in phase space, e.g., rotational lattices and sharp-boundary wells. Our protocol can be realized in a range of experimental platforms for nonclassical state generation and bosonic quantum computation.

Mechanical vibrations are being harnessed for a variety of purposes and at many length scales, from the macroscopic world down to the nanoscale. The considerable design freedom in mechanical structures allows one to engineer new functionalities. In recent years, this has been exploited to generate setups that offer topologically protected transport of vibrational waves (topological phonon transport), both in the solid state and in fluids. Borrowing concepts from electronic physics and being cross fertilized by concurrent studies for cold atoms and electromagnetic waves, this field of topological transport in engineered mechanical systems offers a rich variety of phenomena and platforms. In this Colloquium, a unifying overview of the various ideas employed in this area is provided, different approaches and experimental implementations are summarized, and the challenges as well as the prospects are commented upon.

In quantum optics a small number of building blocks, like resonators, waveguides, driving-induced coupling, and parametric interactions allow the design of a broad variety of devices and functionalities, distinguished by their scattering properties. These include transducers, amplifiers, and nonreciprocal devices, like isolators or circulators. Usually, the design of such systems is handcrafted by an experienced scientist. In our work, we develop a discovery algorithm that automates this process. By optimizing the continuous and discrete system properties our automated search identifies the minimal resources required to realize the requested scattering behavior. The discovered architectures represent classes of solutions and are not bound to special numerical values or platforms. Our approach is applicable to optical, microwave, mechanical, electrical, and hybrid circuits.

We study the use of deep neural networks towards the prediction of the optical properties of two-dimensional photonic crystals, as well as their inverse design. We incorporate a rigorous tight-binding model as a known operator in the machine learning algorithm. This physics-informed approach allows the prediction of meaningful model parameters rather than the high-dimensional full response, allowing for an efficient method as well as potential insight in the physical workings of specific designs. We demonstrate a four-order-of-magnitude speedup of prediction of bandstructures and field symmetries over full-field calculations, and proof-of-concept inverse design of photonic crystals with large gaps, flat bands, and Dirac-point degeneracies.

The Valley Hall effect provides a popular route to engineer robust waveguides for bosonic excitations such a photons and phonons. The almost complete absence of backscattering in many experiments has its theoretical underpinning in a smooth-envelope approximation that neglects large momentum transfer and is accurate only for small bulk band gaps and/or smooth domain walls. For larger bulk band gaps and hard domain walls backscattering is expected to become significant. Here, we show that in this experimentally relevant regime, the reflection of a wave at a sharp corner becomes highly sensitive on the orientation of the outgoing waveguide relative to the underlying lattice. Enhanced backscattering can be understood as being triggered by resonant tunneling transitions in quasimomentum space. Tracking the resonant tunneling energies as a function of the waveguide orientation reveals a self-repeating fractal pattern that is also imprinted in the density of states and the backscattering rate at a sharp corner.

Efficient approaches to quantum control and feedback are essential for quantum technologies, from sensing to quantum computation. Open-loop control tasks have been successfully solved using optimization techniques, including methods like gradient-ascent pulse engineering (GRAPE), relying on a differentiable model of the quantum dynamics. For feedback tasks, such methods are not directly applicable, since the aim is to discover strategies conditioned on measurement outcomes. In this work, we introduce feedback-GRAPE, which borrows some concepts from model-free reinforcement learning to incorporate the response to strong stochastic (discrete or continuous) measurements, while still performing direct gradient ascent through the quantum dynamics. We illustrate its power considering various scenarios based on cavity QED setups. Our method yields interpretable feedback strategies for state preparation and stabilization in the presence of noise. Our approach could be employed for discovering strategies in a wide range of feedback tasks, from calibration of multi-qubit devices to linear-optics quantum computation strategies, quantum-enhanced sensing with adaptive measurements, and quantum error correction.

It was recently discovered that atoms subject to a time-periodic drive can give rise to a crystal structure in phase space. In this work, we point out the atom-atom interactions give rise to collective phonon excitations of phase-space crystal via a pairing interaction with intrinsically complex phases that can lead to a phonon Chern insulator, accompanied by topologically robust chiral transport along the edge of the phase-space crystal. This topological phase is realized even in scenarios where the time-reversal transformation is a symmetry, which is surprising because the breaking of time-reversal symmetry is a strict precondition for topological chiral transport in the standard setting of real-space crystals. Our work has also important implications for the dynamics of 2D charged particles in a strong magnetic field.

Light is a powerful tool for controlling mechanical motion, as shown by numerous applications in the field of cavity optomechanics. Recently, small scale optomechanical circuits, connecting a few optical and mechanical modes, have been demonstrated in an ongoing push towards multi-mode on-chip optomechanical systems. An ambitious goal driving this trend is to produce topologically protected phonon transport. Once realized, this will unlock the full toolbox of optomechanics for investigations of topological phononics. Here, we report the realization of topological phonon transport in an optomechanical device. Our experiment is based on an innovative multiscale optomechanical crystal design and allows for site-resolved measurements in an array of more than 800 cavities. The sensitivity inherent in our optomechanical read-out allowed us to detect thermal fluctuations traveling along topological edge channels. This represents a major step forward in an ongoing effort to downscale mechanical topological systems.

Mechanical vibrations are being harnessed for a variety of purposes and at many length scales, from the macroscopic world down to the nanoscale. The considerable design freedom in mechanical structures allows to engineer new functionalities. In recent years, this has been exploited to generate setups that offer topologically protected transport of vibrational waves, both in the solid state and in fluids. Borrowing concepts from electronic physics and being cross-fertilized by concurrent studies for cold atoms and electromagnetic waves, this field of topological transport in engineered mechanical systems offers a rich variety of phenomena and platforms. In this review, we provide a unifying overview of the various ideas employed in this area, summarize the different approaches and experimental implementations, and comment on the challenges as well as the prospects.

Light propagating in the topological edge channel of an array of ring resonators is predicted to generate nested frequency combs: like a Matryoshka doll containing a set of smaller dolls, each 'tooth' of the comb comprises another frequency comb.

We report the observation of topological phonon transport within a multiscale optomechanical crystal structure consisting of an array of over 800 cavity-optomechanical elements. Using sensitive, spatially resolved optical read-out we detect thermal phonons in a 0.325 − 0.34 GHz band traveling along a topological edge channel, with substantial reduction in backscattering. This represents an important step from the pioneering macroscopic mechanical systems work towards topological phononic systems at the nanoscale.

A large set of recent experiments has been exploring topological transport in bosonic systems, e.g. of photons or phonons. In the vast majority, time-reversal symmetry is preserved, and band structures are engineered by a suitable choice of geometry, to produce topologically nontrivial bandgaps in the vicinity of high-symmetry points. However, this leaves open the possibility of large-quasimomentum backscattering, destroying the topological protection. Up to now, it has been unclear what precisely are the conditions where this effect can be sufficiently suppressed. In the present work, we introduce a comprehensive semiclassical theory of tunneling transitions in momentum space, describing backscattering for one of the most important system classes, based on the valley Hall effect. We predict that even for a smooth domain wall effective scattering centres develop at locations determined by both the local slope of the wall and the energy. Moreover, our theory provides a quantitative analysis of the exponential suppression of the overall reflection amplitude with increasing domain wall smoothness.

The design of periodic nanostructures allows to tailor the transport of photons, phonons, and matter waves for specific applications. Recent years have seen a further expansion of this field by engineering topological properties. However, what is missing currently are efficient ways to rapidly explore and optimize band structures and to classify their topological characteristics for arbitrary unit-cell geometries. In this work, we show how deep learning can address this challenge. We introduce an approach where a neural network first maps the geometry to a tight-binding model. The tight-binding model encodes not only the band structure but also the symmetry properties of the Bloch waves. This allows us to rapidly categorize a large set of geometries in terms of their band representations, identifying designs for fragile topologies. We demonstrate that our method is also suitable to calculate strong topological invariants, even when (like the Chern number) they are not symmetry indicated. Engineering of domain walls and optimization are accelerated by orders of magnitude. Our method directly applies to any passive linear material, irrespective of the symmetry class and space group. It is general enough to be extended to active and nonlinear metamaterials.

Synchronization of weakly coupled nonlinear oscillators is a ubiquitous phenomenon that has been observed across the natural sciences. We study the dynamics of optomechanical arrays-networks of mechanically compliant structures that interact with the radiation pressure force-which are driven to self-oscillation. These systems offer a convenient platform to study synchronization phenomena and have potential technological applications. We demonstrate that this system supports the existence of long-lived chimera states, where parts of the array synchronize while others do not. Through a combined numerical and analytical analysis we show that these chimera states can only emerge in the presence of mechanical frequency heterogeneity.

We propose a platform for robust and tunable nonreciprocal phonon transport based on arrays of optomechanical microtoroids. In our approach, time-reversal symmetry is broken by the interplay of photonic spin-orbit coupling, engineered using a state-of-the-art geometrical approach, and the optomechanical interaction. We demonstrate the topologically protected nature of this system by investigating its robustness to imperfections. This type of system could find application in phonon-based information storage and signal-processing devices.