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Governments around the world have become prolific issuers of soft law regulation in the form of corporate governance codes. However, the strategies that governments pursue to ensure the diffusion of the codes have remained unexplored in the literature. Drawing from institutional and sociopolitical perspectives, I hypothesize that governments pursue a combination of different intervention strategies to bring the corporate governance arrangements of firms in line with the issued code. These strategies focus on the mobilization of material resources, the dissemination of rationales and legitimating accounts for corporate governance change, interventions in social structure and the establishment of new social relations. I test my hypotheses in the context of the issuance of the national corporate governance code in Germany and find general support for my hypotheses.

Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein. Such an oracle would revolutionize science and technology as we know them. However, as scientists, we would not be satisfied with the oracle itself. We want more. We want to comprehend how the oracle conceived these predictions. This feat, denoted as scientific understanding, has frequently been recognized as the essential aim of science. Now, the ever-growing power of computers and artificial intelligence poses one ultimate question: How can advanced artificial systems contribute to scientific understanding or achieve it autonomously? We are convinced that this is not a mere technical question but lies at the core of science. Therefore, here we set out to answer where we are and where we can go from here. We first seek advice from the philosophy of science to understand scientific understanding. Then we review the current state of the art, both from literature and by collecting dozens of anecdotes from scientists about how they acquired new conceptual understanding with the help of computers. Those combined insights help us to define three dimensions of android-assisted scientific understanding: The android as a I) computational microscope, II) resource of inspiration and the ultimate, not yet existent III) agent of understanding. For each dimension, we explain new avenues to push beyond the status quo and unleash the full power of artificial intelligence's contribution to the central aim of science. We hope our perspective inspires and focuses research towards androids that get new scientific understanding and ultimately bring us closer to true artificial scientists., Comment: 13 pages, 3 figures, comments welcome!

Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are core properties of numerous future quantum technologies. The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand. We present a deep generative model of quantum optics experiments where a variational autoencoder is trained on a dataset of quantum optics experimental setups. In a series of computational experiments, we investigate the learned representation of our Quantum Optics Variational Auto Encoder (QOVAE) and its internal understanding of the quantum optics world. We demonstrate that the QOVAE learns an interpretable representation of quantum optics experiments and the relationship between experiment structure and entanglement. We show the QOVAE is able to generate novel experiments for highly entangled quantum states with specific distributions that match its training data. The QOVAE can learn to generate specific entangled states and efficiently search the space of experiments that produce highly entangled quantum states. Importantly, we are able to interpret how the QOVAE structures its latent space, finding curious patterns that we can explain in terms of quantum physics. The results demonstrate how we can use and understand the internal representations of deep generative models in a complex scientific domain. The QOVAE and the insights from our investigations can be immediately applied to other physical systems., Comment: Published in Nature Machine Intelligence https://doi.org/10.1038/s42256-022-00493-5

This study attempts to disentangle firm and country level effects on firm performance and firm ownership using hierarchical linear modeling. I argue that distinguishing country and firm level effects is complex in that firm level characteristics and strategic decision making are not independent of the national context but are embedded in both the national context and firm level stakeholder pressures. The results suggest greater country level effects for ownership than for performance. This pattern of results is consistent with stronger national level influences on ownership structure than on performance outcomes. This finding may reflect trends toward homogeneity due to the globalization of financial markets. Although I found little evidence that the identity of the largest equity owner influenced performance outcomes, several ownership classes were significant for the equation predicting the size of the largest shareholding.

Two processes of photon-pair creation can be arranged such that the paths of the emitted photons are identical. The path information is thereby not erased but rather never born in the first place due to this path identity. In addition to its implications for fundamental physics, this concept has recently led to a series of impactful discoveries in the fields of imaging, spectroscopy, and quantum information science. Here the idea of path identity is presented and a comprehensive review of recent developments is provided. Specifically, the concept of path identity is introduced based on three defining experimental ideas from the early 1990s. The three experiments have in common that they contain two photon-pair sources. The paths of one or both photons from the different sources overlap such that no measurement can recognize from which source they originate. A wide range of noteworthy quantum interference effects (at the single- or two-photon level), such as induced coherence, destructive interference of photon pairs, and entanglement generation, are subsequently described. Progress in the exploration of these ideas has stagnated and has gained momentum again only in the last few years. The focus of the review is the new development in the last few years that modified and generalized the ideas from the early 1990s. These developments are overviewed and explained under the same conceptual umbrella, which will help the community develop new applications and realize the foundational implications of this sleeping beauty.

Inverse design allows the generation of molecules with desirable physical quantities using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. First, we achieve non-trivial performance on typical benchmarks for generative models without any training. Additionally, we demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. Overall, we anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wider adoption., Interpolation and exploration within the chemical space for inverse design.

Significance Quantum entanglement amounts to an extremely strong link between two distant particles, a link so strong that it eludes any classical description and so unsettling that Albert Einstein described it as “spooky action at a distance.” Today, entanglement is not only a subject of fundamental research, but also a workhorse of emerging quantum technologies. In our current work we experimentally demonstrate a completely different method of entanglement generation. Unlike many traditional methods, where entanglement arises due to conservation of a physical quantity, such as momentum, in our method it is rather a consequence of indistinguishability of several particle-generating processes. This approach, where each process effectively adds one dimension to the entangled state, allows for a high degree of customizability., We present an experimental demonstration of a general entanglement-generation framework, where the form of the entangled state is independent of the physical process used to produce the particles. It is the indistinguishability of multiple generation processes and the geometry of the setup that give rise to the entanglement. Such a framework, termed entanglement by path identity, exhibits a high degree of customizability. We employ one class of such geometries to build a modular source of photon pairs that are high-dimensionally entangled in their orbital angular momentum. We demonstrate the creation of three-dimensionally entangled states and show how to incrementally increase the dimensionality of entanglement. The generated states retain their quality even in higher dimensions. In addition, the design of our source allows for its generalization to various degrees of freedom and even for the implementation in integrated compact devices. The concept of entanglement by path identity itself is a general scheme and allows for construction of sources producing also customized states of multiple photons. We therefore expect that future quantum technologies and fundamental tests of nature in higher dimensions will benefit from this approach.

The design of new devices and experiments in science and engineering has historically relied on the intuitions of human experts. This credo, however, has changed. In many disciplines, computer-inspired design processes, also known as inverse-design, have augmented the capability of scientists. Here we visit different fields of physics in which computer-inspired designs are applied. We will meet vastly diverse computational approaches based on topological optimization, evolutionary strategies, deep learning, reinforcement learning or automated reasoning. Then we draw our attention specifically on quantum physics. In the quest for designing new quantum experiments, we face two challenges: First, quantum phenomena are unintuitive. Second, the number of possible configurations of quantum experiments explodes combinatorially. To overcome these challenges, physicists began to use algorithms for computer-designed quantum experiments. We focus on the most mature and \textit{practical} approaches that scientists used to find new complex quantum experiments, which experimentalists subsequently have realized in the laboratories. The underlying idea is a highly-efficient topological search, which allows for scientific interpretability. In that way, some of the computer-designs have led to the discovery of new scientific concepts and ideas -- demonstrating how computer algorithm can genuinely contribute to science by providing unexpected inspirations. We discuss several extensions and alternatives based on optimization and machine learning techniques, with the potential of accelerating the discovery of practical computer-inspired experiments or concepts in the future. Finally, we discuss what we can learn from the different approaches in the fields of physics, and raise several fascinating possibilities for future research., Comment: Comments and suggestions for additional references are welcome!

The vast and growing number of publications in all disciplines of science cannot be comprehended by a single human researcher. As a consequence, researchers have to specialize in narrow sub-disciplines, which makes it challenging to uncover scientific connections beyond the own field of research. Thus access to structured knowledge from a large corpus of publications could help pushing the frontiers of science. Here we demonstrate a method to build a semantic network from published scientific literature, which we call SemNet. We use SemNet to predict future trends in research and to inspire new, personalized and surprising seeds of ideas in science. We apply it in the discipline of quantum physics, which has seen an unprecedented growth of activity in recent years. In SemNet, scientific knowledge is represented as an evolving network using the content of 750,000 scientific papers published since 1919. The nodes of the network correspond to physical concepts, and links between two nodes are drawn when two physical concepts are concurrently studied in research articles. We identify influential and prize-winning research topics from the past inside SemNet thus confirm that it stores useful semantic knowledge. We train a deep neural network using states of SemNet of the past, to predict future developments in quantum physics research, and confirm high quality predictions using historic data. With the neural network and theoretical network tools we are able to suggest new, personalized, out-of-the-box ideas, by identifying pairs of concepts which have unique and extremal semantic network properties. Finally, we consider possible future developments and implications of our findings., 9+6 pages, 6 figures

Multipartite entanglement is one of the core concepts in quantum information science with broad applications that span from condensed matter physics to quantum physics foundations tests. Although its most studied and tested forms encompass two-dimensional systems, current quantum platforms technically allow the manipulation of additional quantum levels. We report the experimental demonstration and certification of a high-dimensional multipartite entangled state in a superconducting quantum processor. We generate the three-qutrit Greenberger-Horne-Zeilinger state by designing the necessary pulses to perform high-dimensional quantum operations. We obtain the fidelity of $76\pm 1\%$, proving the generation of a genuine three-partite and three-dimensional entangled state. To this date, only photonic devices have been able to create and certify the entanglement of these high-dimensional states. Our work demonstrates that another platform, superconducting systems, is ready to exploit genuine high-dimensional entanglement and that a programmable quantum device accessed on the cloud can be used to design and execute experiments beyond binary quantum computation., Comment: 6 pages + 7 supplementary information, 3 + 3 figures, 1 + 3 tables

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science., Comment: 34 pages, 15 figures, comments and suggestions for additional references are welcome!

The interference of quanta lies at the heart of quantum physics. The multipartite generalization of single-quanta interference creates entanglement, the coherent superposition of states shared by several quanta. Entanglement allows non-local correlations between many quanta and hence is a key resource for quantum information technology. Entanglement is typically considered to be essential for creating non-local quantum interference. Here, we show that this is not the case and demonstrate multiphoton non-local quantum interference that does not require entanglement of any intrinsic properties of the photons. We harness the superposition of the physical origin of a four-photon product state, which leads to constructive and destructive interference with the photons’ mere existence. With the intrinsic indistinguishability in the generation process of photons, we realize four-photon frustrated quantum interference. This allows us to observe the following noteworthy difference to quantum entanglement: We control the non-local multipartite quantum interference with a photon that we never detect, which does not require quantum entanglement. These non-local properties pave the way for the studies of foundations of quantum physics and potential applications in quantum technologies.

We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search but is also an essential step towards automated design of multiparticle high-dimensional quantum experiments using generative machine learning models., Comment: 9 pages

One crucial question within artificial intelligence research is how this technology can be used to discover new scientific concepts and ideas. We present Theseus, an explainable AI algorithm that can contribute to science at a conceptual level. This work entails four significant contributions. (i) We introduce an interpretable representation of quantum optical experiments amenable to algorithmic use. (ii) We develop an inverse-design approach for new quantum experiments, which is orders of magnitudes faster than the best previous methods. (iii) We solve several crucial open questions in quantum optics, which is expected to advance photonic technology. Finally, and most importantly, (iv) the interpretable representation and drastic speedup produce solutions that a human scientist can interpret outright to discover new scientific concepts. We anticipate that Theseus will become an essential tool in quantum optics and photonic hardware, with potential applicability to other quantum physical disciplines., 9+5 pages, 5+7 figures; comments welcome

The parameters of a quantum system grow exponentially with the number of involved quantum particles. Hence, the associated memory requirement to store or manipulate the underlying wavefunction goes well beyond the limit of the best classical computers for quantum systems composed of a few dozen particles, leading to serious challenges in their numerical simulation. This implies that the verification and design of new quantum devices and experiments are fundamentally limited to small system size. It is not clear how the full potential of large quantum systems can be exploited. Here, we present the concept of quantum computer designed quantum hardware and apply it to the field of quantum optics. Specifically, we map complex experimental hardware for high-dimensional, many-body entangled photons into a gate-based quantum circuit. We show explicitly how digital quantum simulation of Boson sampling experiments can be realized. We then illustrate how to design quantum-optical setups for complex entangled photonic systems, such as high-dimensional Greenberger–Horne–Zeilinger states and their derivatives. Since photonic hardware is already on the edge of quantum supremacy and the development of gate-based quantum computers is rapidly advancing, our approach promises to be a useful tool for the future of quantum device design.

Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human-interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science.

Path identiy induces a broad interest in recent years due to the foundation for numerous novel quantum information applications. Here, we experimentally demonstrate quantum coherent superposition of two different origins of a four-photon state, where multi-photon frustrated interference emerges from the quantum indistinguishability by path identity. The quantum state is created in four probabilistic photon-pair sources on one integrated silicon photonic chip, two combinations of which can create photon quadruplets. Coherent elimination and revival of distributed four-photons are fully controlled by tuning phases. The experiment gives rise to peculiar quantum interference of two possible ways to create photon quadruplets rather than interference of different intrinsic properties of photons. Besides many known potential applications, this new kind of multi-photon nonlinear interference enables the possibility for various fundamental studies such as nonlocality with multiple spatially separated locations., 6 pages, 4 figures, article

Conspectus The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

Logic Artificial Intelligence (AI) is a subfield of AI where variables can take two defined arguments, True or False, and are arranged in clauses that follow the rules of formal logic. Several problems that span from physical systems to mathematical conjectures can be encoded into these clauses and solved by checking their satisfiability (SAT). In contrast to machine learning approaches where the results can be approximations or local minima, Logic AI delivers formal and mathematically exact solutions to those problems. In this work, we propose the use of logic AI for the design of optical quantum experiments. We show how to map into a SAT problem the experimental preparation of an arbitrary quantum state and propose a logic-based algorithm, called Klaus, to find an interpretable representation of the photonic setup that generates it. We compare the performance of Klaus with the state-of-the-art algorithm for this purpose based on continuous optimization. We also combine both logic and numeric strategies to find that the use of logic AI significantly improves the resolution of this problem, paving the path to developing more formal-based approaches in the context of quantum physics experiments., Comment: 10 pages + appendices, 4 figures

An open question in quantum optics is how to manipulate and control complex quantum states in an experimentally feasible way. Here we present concepts for transformations of high-dimensional multi-photonic quantum systems. The proposals rely on two new ideas: (I) a novel high-dimensional quantum non-demolition measurement, (II) the encoding and decoding of the entire quantum transformation in an ancillary state for sharing the necessary quantum information between the involved parties. Many solutions can readily be performed in laboratories around the world, and identify important pathways for experimental research in the near future. The concept has been found using the computer algorithm Melvin for designing computer-inspired quantum experiments. This demonstrates that computer algorithms can inspire new ideas in science, which is a widely unexplored potential., Comment: 6+2 pages, 3+2 figures

Computer-aided design of molecules has the potential to disrupt the field of drug and material discovery. Machine learning, and deep learning, in particular, have been topics where the field has been developing at a rapid pace. Reinforcement learning is a particularly promising approach since it allows for molecular design without prior knowledge. However, the search space is vast and efficient exploration is desirable when using reinforcement learning agents. In this study, we propose an algorithm to aid efficient exploration. The algorithm is inspired by a concept known in the literature as curiosity. We show on three benchmarks that a curious agent finds better performing molecules. This indicates an exciting new research direction for reinforcement learning agents that can explore the chemical space out of their own motivation. This has the potential to eventually lead to unexpected new molecules that no human has thought about so far., Comment: 9 pages, 2 figures; comments welcome

This paper is a celebration of the frontiers of science. Goodenough, the maestro who transformed energy usage and technology through the invention of the lithium ion battery, opens the programme, reflecting on the ultimate limits of battery technology. This applied theme continues through the subsequent pieces on energy related topics (the sodium ion battery and artificial fuels, by Mansson) and the ultimate challenge for 3 dimensional printing the eventual production of life, by Atala. A passage by Alexander follows, reflecting on a related issue: How might an artificially produced human being behave? Next comes a consideration of consiousness and free will by Allen and Lidstrom. Further voices and new instruments enter as Bowen, Mauranyapin and Madsen discuss whether dynamical processes of single molecules might be observed in their native state. The exploitation of chaos in science and technology, applications of Bose Einstein condensates and a consideration of the significance of entropy follow in pieces by Reichl, Rasel and Allen, respectively. Katsnelson and Koonin then discuss the potential generalisation of thermodynamic concepts in the context of biological evolution. Entering with the music of the cosmos, Yasskin discusses whether we might be able to observe torsion in the geometry of the universe. The crescendo comes with the crisis of singularities, their nature and whether they can be resolved through quantum effects, in the composition of Coley. The climax is Krenn, Melvin and Zeilinger consideration of how computer code can be autonomously surprising and creative. In a harmonious counterpoint, Yampolskiy concludes that such code is not yet able to take responsibility for coauthoring a paper., Please send comments and questions to Suzy Lidstrom

Quantum entanglement is important for emerging quantum technologies such as quantum computation and secure quantum networks. To boost these technologies, a race is currently ongoing to increase the number of particles in multiparticle entangled states, such as Greenberger–Horne–Zeilinger (GHZ) states. An alternative route is to increase the number of entangled quantum levels. Here, we overcome present experimental and technological challenges to create a three-particle GHZ state entangled in three levels for every particle. The resulting qutrit-entangled states are able to carry more information than entangled states of qubits. Our method, inspired by the computer algorithm Melvin, relies on a new multi-port that coherently manipulates several photons simultaneously in higher dimensions. The realization required us to develop a new high-brightness four-photon source entangled in orbital angular momentum. Our results allow qualitatively new refutations of local-realistic world views. We also expect that they will open up pathways for a further boost to quantum technologies. A three-dimensionally entangled Greenberger–Horne–Zeilinger state, where all three photons reside in a qutrit space, is generated by developing a new multi-port in combination with a novel four-photon source entangled in orbital angular momentum.

Despite the global boom in skyscraper construction, the drivers of skyscraper height are still poorly understood. While extant research primarily focuses on economic determinants of skyscraper heig...

A limitation in the downsizing literature is its lack of attention on how firms’ institutional context interacts with firm’s internal drivers of employee downsizing. This study examines the firm performance - employee downsizing relationship in 1,747 firms across 35 countries over three years and demonstrates that while this relationship is similar among firms across countries, its magnitude varies across countries, and that the cultural dimensions of in-group collectivism, power distance, uncertainty avoidance help explain this variance. Implications from these findings and future directions for employee downsizing research and practice are discussed.

PurposeThis study aims to examine the effects of competing influences emanating from firms’ social structural context (i.e. sent and received board of director interlocks and industry peers) on the adoption of an institutionally contested corporate governance code provision.Design/methodology/approachThe corporate governance code provision of interest in this research recommends that German firms listed on German stock exchanges should disclose the individual remuneration arrangements for their board members. This paper uses 945 firm year observations from 2002 to 2006, the time period during which the adoption of this provision was voluntary for firms, to examine the role of firms’ social structural context in the legitimization process of this provision.FindingsThe results show that sent board interlocks to firms that defy pressures to adopt this practice have an equally pronounced but opposing effect on its institutionalization process. Received interlocks are inconsequential in this process. The results also provide evidence for the existence of competing influences emanating from firms’ industry peers. In contrast to the effects associated with sent board interlocks, at the industry level, peer acquiescence has a more pronounced effect than peer defiance. Furthermore, the practice’s legitimacy among firms’ peers moderates the effects of sent board interlocks.Originality/valueThe results of this paper suggest that a balanced approach to studying institutional change in corporate governance needs to acknowledge the co-existence of conflicting signals regarding the spread of new institutional models. The findings suggest that firms’ social structural context plays a central role in processes of contested institutional change. Board interlocks and industry peers carry the potential to facilitate institutional change and facilitate institutional continuity and resistance to change. However, not all board interlocks are of equal importance, and industry peers constitute a source of legitimacy to which directors forming the interlocks attend.

Precise measurement or perfect cloning of unknown quantum states is forbidden by the laws of quantum mechanics. Yet, quantum teleportation in principle allows for a faithful and disembodied transmission of unknown quantum states between distant quantum systems using entanglement. There have been numerous experiments on teleportation of quantum states of single photons, atoms, trapped ions, defects in solid states, and superconducting circuits. However, all demonstrations to date were limited to a two-dimensional subspace$-$so-called qubit$-$of the quantized multiple levels of the quantum systems. In general, a quantum particle can naturally possess not only multiple degrees of freedom, but also, many degrees of freedom can have high quantum number beyond the simplified two-level subspace. Here, making use of multiport beam-splitters and ancillary single photons, we propose a resource-efficient and extendable scheme for teleportation of arbitrarily high-dimensional photonic quantum states. We report the first experimental teleportation of a qutrit, which is equivalent to a spin-1 system. Measurements over a complete set of 12 states in mutually unbiased bases yield a teleportation fidelity of 0.75(1), well above the optimal single-copy qutrit-state-estimation limit of 1/2. The fidelity also exceeds the limit of 2/3, the maximum possible for explanation through qubits only. Thus, we strictly prove a genuine three-dimensional, universal, and highly non-classical quantum teleportation. Combining previous methods of teleportation of two-particle composite states and multiple degrees of freedom, our work provides a complete toolbox for teleporting a quantum particle intact. We expect that our results will pave the way for quantum technology applications in high dimensions, since teleportation plays a central role in quantum repeaters and quantum networks., 23 pages, 12 figures, the version accepted by Physical Review Letters on 22nd July. The previous version was first submitted to a journal on 19th May

Quantum entanglement plays an important role in quantum information processes, such as quantum computation and quantum communication. Experiments in laboratories are unquestionably crucial to increase our understanding of quantum systems and inspire new insights into future applications. However, there are no general recipes for the creation of arbitrary quantum states with many particles entangled in high dimensions. Here, we exploit a recent connection between quantum experiments and graph theory and answer this question for a plethora of classes of entangled states. We find experimental setups for Greenberger-Horne-Zeilinger states, W states, general Dicke states, and asymmetrically high-dimensional multipartite entangled states. This result sheds light on the producibility of arbitrary quantum states using photonic technology with probabilistic pair sources and allows us to understand the underlying technological and fundamental properties of entanglement., Comment: 7 pages, 7 figures; Appendix 3 pages, 5 figures

We state a number of related questions on the structure of perfect matchings. Those questions are inspired by and directly connected to Quantum Physics. In particular, they concern the constructability of general quantum states using modern photonic technology. For that we introduce a new concept, denoted as inherited vertex coloring. It is a vertex coloring for every perfect matching. The colors are inherited from the color of the incident edge for each perfect matching. First, we formulate the concepts and questions in pure graph-theoretical language, and finally we explain the physical context of every mathematical object that we use. Importantly, every progress towards answering these questions can directly be translated into new understanding in quantum physics., Comment: 10 pages, 4 figures, 6 questions (added suggestions from peer-review)

Starting with the first experimental generation of a Greenberger-Horne-Zeilinger entangled state in three dimensions we show how to describe photonic quantum experiments using graph theory. This novel link promises exciting new applications ranging from multi-photon high-dimensionally entangled states to special purpose quantum simulation.

The study of light propagation has been a cornerstone of progress in physics and technology. Recently, advances in control and shaping of light have created significant interest in the propagation of complex structures of light -- particularly under realistic terrestrial conditions. While theoretical understanding of this research question has significantly grown over the last two decades, outdoor-experiments with complex light structures are rare, and comparisons with theory have been nearly lacking. Such situations show a significant gap between theoretical models of atmospheric light behaviour and current experimental effort. Here, in an attempt to reduce this gap, we describe an interesting result of atmospheric models which are feasible for empirical observation. We analyze in detail light propagation in different spatial bases and present results of the theory that the influence of atmospheric turbulence is basis-dependent. Concretely, light propagating as eigenstate in one complete basis is stronger influenced by atmosphere than light propagating in a different, complete basis. We obtain these results by exploiting a family of the continuously adjustable, complete basis of spatial modes -- the Ince-Gauss modes. Our concrete numerical results will hopefully inspire experimental efforts and bring the theoretical and empirical study of complex light patterns in realistic scenarios closer together., Comment: 9 pages and 5 figures; supplementary: 2 pages and 6 tables

The discovery of novel materials and functional molecules can help to solve some of society's most urgent challenges, ranging from efficient energy harvesting and storage to uncovering novel pharmaceutical drug candidates. Traditionally matter engineering -- generally denoted as inverse design -- was based massively on human intuition and high-throughput virtual screening. The last few years have seen the emergence of significant interest in computer-inspired designs based on evolutionary or deep learning methods. The major challenge here is that the standard strings molecular representation SMILES shows substantial weaknesses in that task because large fractions of strings do not correspond to valid molecules. Here, we solve this problem at a fundamental level and introduce SELFIES (SELF-referencIng Embedded Strings), a string-based representation of molecules which is 100\% robust. Every SELFIES string corresponds to a valid molecule, and SELFIES can represent every molecule. SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid. In our experiments, the model's internal memory stores two orders of magnitude more diverse molecules than a similar test with SMILES. Furthermore, as all molecules are valid, it allows for explanation and interpretation of the internal working of the generative models., Comment: 6+3 pages, 6+1 figures

We experimentally realize the first high-dimensional quantum teleportation experiment, and proposed a general method for teleporting n-dimensional photon. We developed an optimal SPDC entangled photon-pair source, and implement 12-photon entanglement and scattershot boson sampling.