Physics Colloquium

Superconductivity is a fascinating state of matter that transforms metals at very low temperature into perfect conductors and perfect diamagnets. This enables numerous technical applications for magnetic levitation, electric current transport without loss and for quantum information technology. A desired but rare type of unconventional superconductivity with possible uses in topological quantum computing is one where the superconducting condensate is odd under inversion symmetry, so-called odd-parity superconductivity. Only a handful of uranium-based materials have this property and it is usually explained by the presence of ferromagnetism enforcing a parallel alignment of the electrons forming the Cooper pair.

In the colloquium talk I will present our astonishing discovery that superconductivity in the material CeRh2As2 with a critical temperature of only 0.4 kelvin switches its state in a magnetic field and is then stable up to the extreme magnetic field of 16 tesla. The switching is understood as a unique phase transition from even-parity to odd-parity superconductivity that likely relies on a special crystallographic feature of the underlying material, CeRh2As2, and not on ferromagnetic interactions. I will show our experimental investigations into the question what stabilises such a transition that we address by tuning superconductivity and other coexisting orders with temperature, magnetic field and hydrostatic pressure. The resulting knowledge paves the way for the design of other odd-parity superconductors with higher transition temperatures useful for applications.

Recent rapid progress in applications of machine learning has also illustrated that there is an exponential growth of required resources, especially for advanced applications like large-language models. This makes it all the more urgent to explore possible alternatives to current digital artificial neural networks. The field of neuromorphic computing sets itself the goal to identify suitable physical architectures that enable us to perform machine learning tasks in a highly parallel and much more energy-efficient manner. In this talk, I will present two examples from our research in this domain. One important goal is physics-based training. I will introduce the idea of Hamiltonian Echo Backpropagation, which allows to perform both a physics-based version of backpropagation and parameter updates purely via physical dynamics, making it unique among proposed physical learning techniques. In the second part, I will present our recent idea on implementing fully nonlinear neuromorphic computing based on any purely linear wave scattering platform.

Self-Learning Machines Based on Hamiltonian Echo Backpropagation, Víctor López-Pastor and Florian Marquardt, Phys. Rev. X 13, 031020 (2023) https://journals.aps.org/prx/abstract/10.1103/PhysRevX.13.031020

Fully Non-Linear Neuromorphic Computing with Linear Wave Scattering, Clara C. Wanjura, Florian Marquardt, arXiv:2308.16181 https://arxiv.org/abs/2308.16181

The Standard Model of particle physics is incredibly successful but glaringly incomplete. Among the questions left open is the striking imbalance of matter and antimatter in our universe, which inspires experiments to compare the fundamental properties of matter/antimatter conjugates with high precision. The BASE collaboration at the antiproton decelerator of CERN is performing such high-precision comparisons with protons and antiprotons. Using advanced cryogenic Penning traps, we have performed the most precise comparison of the proton-to-antiproton charge-to-mass ratio with a fractional uncertainty of 16 parts in a trillion [1]. In another measurement, we have invented a novel spectroscopy technique, that allowed for the first direct measurement of the antiproton magnetic moment with a fractional precision of 1.5 parts in a billion [2]. Together with our last measurement of the proton magnetic moment [3] this improves the precision of previous magnetic moment based tests of the fundamental CPT invariance by more than a factor of 3000. A time series analysis of the sampled magnetic moment resonance furthermore enabled us to set first direct constraints on the interaction of antiprotons with axion-like particles (ALPs) [4], and most recently, we have used our ultra-sensitive single particle detection systems to derive constraints on the conversion of ALPs into photons [5]. In parallel we are working on the implementation of new measurement technology to sympathetically cool antiprotons [6] and to apply quantum logic inspired spectroscopy techniques [7]. In addition to that, we are currently developing the transportable antiproton-trap BASE-STEP, to relocate antiproton spectroscopy experiments from accelerator environment to dedicated precision laboratory space at Heinrich Heine University Düsseldorf. I will give a general introduction to the topic, will review the recent results produced by BASE, with particular focus on recent developments towards an at least 10-fold improved measurement of the antiproton magnetic moment.

[1] M. J. Borchert et al., Nature 601, 35 (2022).
[2] C. Smorra et al., Nature 550, 371 (2017).
[3] G. Schneider et al., Science 358, 1081 (2017).
[4] C. Smorra et al., Nature 575, 310 (2019).
[5] J. A. Devlin et al., Phys. Rev. Lett. 126, 041321 (2021).
[6] M. A. Bohman et al. Nature 596, 514 (2021)
[7] J. M Conrejo et al., New J. Phys. 23 073045

Rechargeable batteries have been an integral part of the portable electronics revolution and are now playing an increasingly important role in transport and grid applications, but the introduction of these devices comes with different sets of challenges. New technologies are being investigated, such as those involving using sodium and magnesium ions instead of lithium, or involving the flow of materials in an out of the electrochemical cell (in redox flow batteries). Importantly, fundamental science is key to producing non-incremental advances and to develop new strategies for energy storage and conversion.

This talk will describe starting with NMR studies of local structure and dynamics. In particular, the analysis of hyperfine interactions between the Li nuclei and paramagnetic ions will be described, focusing on the cathode material NMC-811 (Li[Ni_0.8Co_0.1Mn_0.1]O₂). Via an analysis of the hyperfine shifts, information can be obtained concerning the electronic structures of the materials as a function of state of charge, which can be used to understand how these materials function. Li NMR spectroscopy can also be used to quantify dynamics in these materials as a function of vacancy concentration and structural changes. We shall show how these dynamics then affect the mechanisms by which Li are removed from and reinserted into the structure. This work involves the use of new optical methods to study phase transitions at the particle level operando, i.e., while the batteries are being cycled. Finally, new results on extremely high-rate batteries will be outlined, making use of a variety of techniques including operando NMR spectroscopy.

Deep neural networks (DNNs) are revolutionizing the field of artificial intelligence and are key drivers of innovation in device technology and computer architecture. While there has been significant progress in the development of specialized hardware for DNN inference, many of the existing architectures physically split the memory and processing units. This means that DNN models are typically stored in a separate memory location, and that computational tasks require constant shuffling of data between the memory and processing units – a process that slows down computation and limits the maximum achievable energy efficiency. Analog in-memory computing (AIMC) is a promising approach that addressing this challenge by borrowing two key features of how biological neural networks are realized. Synaptic weights are physically localized in nanoscale memory elements and the associated computational operations are performed in the analog/mixed-signal domain. In the first part of the lecture, I will introduce AIMC based on non-volatile memory technology. The focus will be on the key concepts and the associated terminology. Subsequently, a multi-tile mixed-signal AIMC chip for deep learning inference will be presented. This chip fabricated in 14nm CMOS technology comprises 64 AIMC cores/tiles based on phase-change memory technology. It will serve as the basis to delve deeper into the device, circuits, architectural and algorithmic aspects of AIMC. Of particular focus will be achieving floating point-equivalent classification accuracy while performing the bulk of computations in the analog domain with relatively less precision. In the final part, I will present some of the ongoing research towards the next generation of AIMC chips and provide an outlook for the future.

Dr. Abu Sebastian is a Distinguished Scientist and technical manager at IBM Research – Zurich. He is one of the technical leaders of IBM’s research efforts towards next generation AI Hardware and manages the in-memory computing group at IBM Research - Zurich. He is the author of over 200 publications in peer-reviewed journals/conference proceedings and holds over 90 US patents. In 2015 he was awarded the European Research Council (ERC) consolidator grant and in 2020, he was awarded an ERC Proof-of-concept grant. He was an IBM Master Inventor and was named Principal and Distinguished Research Staff Member in 2018 and 2020, respectively. In 2019, he received the Ovshinsky Lectureship Award for his contributions to "Phase-change materials for cognitive computing". In 2023, he was conferred the title of Visiting Professor in Materials by University of Oxford. He is a distinguished lecturer and fellow of the IEEE.

Deep neural networks (DNNs) are revolutionizing the field of artificial intelligence and are key drivers of innovation in device technology and computer architecture. While there has been significant progress in the development of specialized hardware for DNN inference, many of the existing architectures physically split the memory and processing units. This means that DNN models are typically stored in a separate memory location, and that computational tasks require constant shuffling of data between the memory and processing units – a process that slows down computation and limits the maximum achievable energy efficiency. Analog in-memory computing (AIMC) is a promising approach that addressing this challenge by borrowing two key features of how biological neural networks are realized. Synaptic weights are physically localized in nanoscale memory elements and the associated computational operations are performed in the analog/mixed-signal domain. In the first part of the lecture, I will introduce AIMC based on non-volatile memory technology. The focus will be on the key concepts and the associated terminology. Subsequently, a multi-tile mixed-signal AIMC chip for deep learning inference will be presented. This chip fabricated in 14nm CMOS technology comprises 64 AIMC cores/tiles based on phase-change memory technology. It will serve as the basis to delve deeper into the device, circuits, architectural and algorithmic aspects of AIMC. Of particular focus will be achieving floating point-equivalent classification accuracy while performing the bulk of computations in the analog domain with relatively less precision. In the final part, I will present some of the ongoing research towards the next generation of AIMC chips and provide an outlook for the future.

Dr. Abu Sebastian is a Distinguished Scientist and technical manager at IBM Research – Zurich. He is one of the technical leaders of IBM’s research efforts towards next generation AI Hardware and manages the in-memory computing group at IBM Research - Zurich. He is the author of over 200 publications in peer-reviewed journals/conference proceedings and holds over 90 US patents. In 2015 he was awarded the European Research Council (ERC) consolidator grant and in 2020, he was awarded an ERC Proof-of-concept grant. He was an IBM Master Inventor and was named Principal and Distinguished Research Staff Member in 2018 and 2020, respectively. In 2019, he received the Ovshinsky Lectureship Award for his contributions to "Phase-change materials for cognitive computing". In 2023, he was conferred the title of Visiting Professor in Materials by University of Oxford. He is a distinguished lecturer and fellow of the IEEE.

The James Webb Space Telescope was launched on Dec. 25, 2021, and commissioning was completed in early July 2022. With its 6.5 m golden eye, and cameras and spectrometers covering 0.6 to 28 µm, Webb is already producing magnificent images of galaxies, active galactic nuclei, star-forming regions, and planets. Scientists are hunting for some of the first objects that formed after the Big Bang, the first black holes (primordial or formed in galaxies), and beginning to observe the growth of galaxies, the formation of stars and planetary systems, individual exoplanets through coronography and transit spectroscopy, and all objects in the Solar System from Mars on out. Plasma processes control the growth of stars, planets, and black holes, and the release of rotational energy through jets from protostars and active galactic nuclei (accreting black holes). I will show how we built the Webb and what we have learned so far. The greatest surprise, still not explained, is that the first galaxies grew faster, hotter, larger, brighter, and more massive than we had predicted. After Webb, we have ambitions for even more powerful telescopes. Webb is a joint project of NASA with the European and Canadian space agencies.

Bio

Dr. John C. Mather is a Senior Astrophysicist and is the Senior Project Scientist for the James Webb Space Telescope (JWST) at NASA’s Goddard Space Flight Center. Since the project start in 1995 until 2023, he has led the JWST science teams. As a postdoctoral fellow at NASA’s Goddard Institute for Space Studies he led the proposal efforts for the Cosmic Background Explorer (74-76), and came to GSFC to be the Study Scientist (76-88), Project Scientist (88-98), and the Principal Investigator for the Far IR Absolute Spectrophotometer (FIRAS) on COBE. With the COBE team, he showed that the cosmic microwave background radiation has a blackbody spectrum within 50 parts per million, confirming the expanding universe model to extraordinary accuracy. The COBE team also made the first map of the hot and cold spots in the background radiation (anisotropy). Dr. Mather received the Nobel Prize in Physics (2006) with George Smoot, for the COBE work.

The search for new quantum materials with novel properties is often focused on materials containing transition-metal, rare-earth and/or actinide elements. The presence of the atomic-like d or f orbitals provides a fruitful playground to generate novel phenomena. The intricate interplay of band formation with the local electron correlation and atomic multiplet effects leads to phases that are nearly iso-energetic, making materials’ properties highly tunable by doping, temperature, pressure or magnetic field. Understanding the behavior of the d and f electrons is essential for designing and controlling novel quantum materials. Therefore, identifying the d or f orbitals that actively participate in the formation of the ground state is crucial. So far, these orbitals have mostly been deduced from optical, X- ray and neutron spectroscopies in which spectra must be analyzed using theory or modelling. This, however, is also a challenge in and of itself, since ab-initio calculations hit their limits due to the many-body nature of the problem.

Here we developed a new experimental method that circumvents the need for involved analysis and instead provides the information as measured. With this technique, we can make a direct image of the active orbital and determine what the atomic-like object looks like in a real solid. The method, X-Ray Raman spectroscopy or non-resonant inelastic X-ray scattering using an s-core level (s-NIXS), relies on high momentum transfer in the inelastic scattering process, which is necessary for dipole-forbidden terms to gain spectral weight. To demonstrate the strength of the technique, we imaged the text-book example,ground-state x2-y2/3x2-r2 hole orbital of the Ni2+ ion in NiO single crystal (see Figure). We will present the basic principles of s-NIXS and details of its experimental implementation. We will show how we can apply this technique to unveil the active orbitals in a wide range of single crystalline materials. We will also lay out what instrumental improvements are needed to advance this method further.

[1] H. Yava?, M. Sundermann, K. Chen, A. Amorese, A. Severing, H. Gretarsson, M.W. Haverkort, L.H. Tjeng, Nature Physics 15, 559 (2019)
[2] B. Leedahl, M. Sundermann, A. Amorese, A. Severing, H. Gretarsson, L. Zhang, A.C. Komarek, A. Maignan, M.W. Haverkort, and L.H. Tjeng, Nature Commun. 10, 5447 (2019).
[3] A. Amorese, B. Leedahl, M. Sundermann, H. Gretarsson, Z. Hu, H.-J. Lin, C.T. Chen, M. Schmidt, H. Borrmann, Yu. Grin, A. Severing, M.W. Haverkort, and L.H. Tjeng, Phys. Rev. X 11, 011002 (2021).

Digitale Computer, die Informationen in Form von Bits darstellen, haben sich über viele Jahre hinweg in beispielloser Weise weiterentwickelt und sind in unserem Leben allgegenwärtig geworden. Heute stoßen diese klassischen, auf Miniaturisierung basierenden Technologien an ihre Grenzen, und es werden neue Rechenparadigmen gesucht, um den Stromverbrauch zu senken und die Rechenleistung zu erhöhen. Zwei neue Ansätze haben in den letzten Jahren enorme Fortschritte gemacht. Zum einen analoge Prozessoren als Acceleratoren für Probleme der Künstlichen Intelligenz und zum zweiten, Quanten Computer für komplexe Rechenprobleme, die für klassische Computer, auch Hochleistungsrechner, unlösbar sind. Quanten Computer basieren auf den Gesetzen der Quantenmechanik und werden von Grund auf neu entwickelt und aufgebaut. Kürzlich wurden bedeutende Fortschritte erzielt, und es gibt mittlerweile Quantenprozessoren mit 433 Qubits.

In diesem Vortrag wird ein Überblick über den Stand der Forschung der neuen Computer Paradigmen im Bereich der Rechner für Künstliche Intelligenz und der Quanten Computer gegeben.

Digitale Computer, die Informationen in Form von Bits darstellen, haben sich über viele Jahre hinweg in beispielloser Weise weiterentwickelt und sind in unserem Leben allgegenwärtig geworden. Heute stoßen diese klassischen, auf Miniaturisierung basierenden Technologien an ihre Grenzen, und es werden neue Rechenparadigmen gesucht, um den Stromverbrauch zu senken und die Rechenleistung zu erhöhen. Zwei neue Ansätze haben in den letzten Jahren enorme Fortschritte gemacht. Zum einen analoge Prozessoren als Acceleratoren für Probleme der Künstlichen Intelligenz und zum zweiten, Quanten Computer für komplexe Rechenprobleme, die für klassische Computer, auch Hochleistungsrechner, unlösbar sind. Quanten Computer basieren auf den Gesetzen der Quantenmechanik und werden von Grund auf neu entwickelt und aufgebaut. Kürzlich wurden bedeutende Fortschritte erzielt, und es gibt mittlerweile Quantenprozessoren mit 433 Qubits.

In diesem Vortrag wird ein Überblick über den Stand der Forschung der neuen Computer Paradigmen im Bereich der Rechner für Künstliche Intelligenz und der Quanten Computer gegeben.

The performance of electronic chips is to a large extent limited by the electrical resistance, which sets the maximum operation frequency and the minimum power consumption. It is expected that by replacing part of the electronic circuit by photonics, these limitations could be alleviated. For this goal, an integrated light source is required.

Silicon and germanium, unfortunately, cannot emit light efficiently due to their indirect bandgap, hampering the development of Si-based photonics. However, alloys of SiGe in the hexagonal phase are predicted to have a direct band gap [1]. In this work, we exploit the unique feature of the nanowire growth mechanism to control the crystal structure by tuning the contact angle of the catalyst particle and demonstrate the optical properties [2]. We show efficient light emission from hexagonal SiGe, up to room temperature, accompanied by a short radiative life time of around a nanosecond, the hallmarks of a direct band gap material. The band gap energy is tunable in the range of 0.35 till 0.7eV opening a plethora of new applications.

The next challenge is to demonstrate lasing from this new material. For this we have fabricated an external cavity. Above a certain excitation threshold, we observe a strong reduction of the radiative lifetime and a superlinear increase of the emission intensity, first indications of amplified spontaneous emission (ASE).

[1] Silvana Botti et al. , Phys. Rev. Mat. 3, 034602 (2019)
[2] E.M.T. Fadaly et al., Nature 580, 205 (2020).

Soft exoskeleton or exosuits have been introduced in the last decade as possible candidates to overcome the limitations and acceptability of wearable technology. Although the Exosuits initially promised tangible improvements, yet their soft wearable architecture presents strong drawbacks, placing this technology more in a complementary position rather than on a higher step of the podium respect to their predecessors.

During my speech, I will introduce the progress from our research on soft wearable exosuits at the Assistive Robotics and Interactive Exosuits Lab (ARIES), by presenting novel solutions on mechanical design, novel implementation of control strategies based on machine learning to master the non-linear behaviours. I will discuss in detail how using biosignals by means of realtime techniques based on musculoskeletal dynamics provide a symbiotic interface between the exosuit and the user and also introduce our latest results in clinical applications.

Folding the World: Infinite growth on a finite planet Prof. Dr. Anders Levermann Institut für Klimafolgenforschung, Potsdam We are at the end of an age – the age of expansion – and we need a new narrative for the next step. The limitations of our physical Earth collide with the necessity of rapid societal development. Accepting the harsh reality of both, we face a dilemma. The desperate call for renunciation and recession is understandable but counterproductive, because it does not resolve the dilemma. Where economists struggle, physicists know the answer: there is infinite opportunity in finite space. Therefore the mathematical concept of folding could provide a solution, because it allows for infinite motion in a finite world – through growth into diversity. Not growth into more, but into different – and not theoretically or esoterically but in a practical, applicable manner.

Thus solving the climate problem yields an opportunity for a new social narrative that is based on the theory of the dynamics of complex systems. There will be equations and there will be physics. Nothing new, but perhaps a new way of looking at the known.

Neutron stars are among the most compact objects in the Universe and the detection of gravitational waves and electromagnetic signals from the merger of two neutron stars in 2017 has been a revolution. This breakthrough observation enabled studies about the history of our cosmos, the formation of heavy elements, and the physics on subatomic scales. Since then, another binary neutron star merger was seen in April 2019, and in January 2020, the detection of two black hole ? neutron star mergers completed the picture.

Essential for the interpretation of all these observations are reliable models describing the merger dynamics. We show how these models can be used to derive new constraints on the equation of state of supranuclear-dense matter and the Hubble constant. For this purpose, we analyze the gravitational waves and electromagnetic signals emitted from the binary neutron star merger GW170817 together with other gravitational-wave measurements, as well as X-ray and radio observation of single neutron stars.

Finally, we compare the constraints derived from our multi-messenger analysis with heavy-ion collisions of gold nuclei, showing a remarkable consistency between macroscopic collisions of compact binaries and microscopic collisions in particle colliders.

The field of Asteroseismology — the study of the internal structures of stars through their global intrinsic oscillations, has been revolutionised over the past decade. This revolution was possible thanks to the high-precision high-cadence photometric data from space missions CoRoT, Kepler, K2 and TESS. These missions provided long timeseries data for hundreds of thousand stars. In many cases the brightness variations in these timeseries data reveal the intrinsic eigenmodes of the stars. These eigenmodes are defined by the internal structure of the star and hence, the stellar structure can be derived from the frequencies of the eigenmodes.

In this talk I will focus on stars cooler than ~ 6700 K, which exhibit oscillations similar to the oscillations in the Sun. I will present recent results and prospects of asteroseismic inferences of the stellar structure of these cool stars.

Biological systems avoid equilibrium by taking chemical energy from their surroundings and using it to do work. Cells organise intra-cellular components into the structures that allow them to grow, reproduce and move. Tissues, collections of cells, differentiate locally as they develop to perform the complex functions of different organs.

Active systems, also exist out of thermodynamic equilibrium. Dense active matter shows complex collective behaviour and mesoscale turbulence, the emergence of chaotic flow structures characterised by high vorticity and self-propelled topological defects. I shall describe the physics of dense active matter and discuss possible links to motility and morphogenesis in biological contexts.

Mankind has underestimated the lever it has in changing the biogeochemical state of the planet earth by emitting greenhouse gasses. We are in a position where the state of the planet is about to change into a condition that may be detrimental for mankind. It is thus extremely urgent to minimize the emission of greenhouse gasses in a global dimension. This can be achieved if the existing world market of fossil energy is replaced by a world market of renewable energies. Even so it is a challenging task and requires rebuilding the largest industry on earth within one generation.

The primary source of renewable energy is electricity that can locally be used with better efficiency than fossil energy carriers. But it cannot be stored and transported (traded) in grid dimensions. Future energy systems need a dual approach using green electrons and green molecules as energy carriers. Hydrogen is the primary molecular source. It needs transformation into transportable derivatives that may also be used as fuels. The central challenge of renewable primary electricity -its intermittency- can thus be overcome by using electrical or chemical batteries[1] allowing short-term and long-term storage respectively.

Catalysis as electrocatalysis and as interfaces for gas phase transformations are consequently the enabling processes operated at the scale of the global energy system. Although all essential process for chemical energy conversion (CEC) do exist as mature technologies they still show substantial deficiencies. One is the sector-coupled systemic interconnection (dynamical operation, feed gas purification), others are production processes for the infrastructure (electrolysers) and it is unclear what the fundamental limitations are driving the processes at thermodynamic limits under optimized conditions. Catalyst design educated by functional understanding and modernized digital tools based upon clean experimental data is a critical scientific contribution to energy science. Likewise, cutting-edge chemical engineering bridged to catalyst material science through computational catalysis science is another critical element.

The presentation links a systemic view[2] on the dimension of energy transformation to examples of where we stand in the functional understanding of important catalytic systems namely MeOH synthesis and ammonia synthesis/ reforming. Research should clearly discriminate between novel approaches that are needed for future generations of CEC processes and contributions to establish the first generation of CEC that is highly time-critical and thus needs focussed efforts translating verified fundamental knowledge into technology relevant transfers.

1. Schlogl, R., Chemical Batteries with CO2. Angewandte Chemie-International Edition, 2022. 61(7).
2. Schlögl, R., Put the Sun in the Tank: Future Developments in Sustainable Energy Systems. Angew. Chem. Int. Ed., 2019. 58(1): p. 343-348.

Nuclear halos are a fascinating manifestation of quantum physics. They belong to a subset of low-density clustering for which most of the probability to find the halo nucleon extends to a region of space that is classically forbidden. Their properties show universal aspects of few-body systems such as scaling laws. Advances in the production of radioactive isotope beams give access to loosely-bound neutron-rich systems at the nuclear driplines, where halos are found.

The Radioactive Isotope Beam Facility (RIBF) of RIKEN, Japan, provides unique opportunities to explore the neutron drip line in light nuclei. Recent highlights based on quasifree scattering experiments in inverse-kinematics will be presented.

Low-energy antiprotons offer a very unique sensitivity to the neutron and proton densities in the tail of the nuclear density. Such studies with short-lived nuclei at ISOLDE, CERN, are the motivation of the recently-accepted experiment PUMA (antiProton Unstable Matter Annihilation). The concept and status of the experiment will be briefly introduced.

Halos are expected to extend in the strangeness sector. The hypertriton, composed of a Lambda hyperon, a neutron and a proton, is a halo candidate. A FAIR-0 program at the R3B experiment aims at determining the size of the hypertriton by revisiting the historical experiment at the origin of the discovery of halos. The challenges and sensitivity of such a measurement will be discussed.

In the scientific reports, political debates, and to a large degree also in the media, the measure of global climate change around the world is global mean temperature rise used as the metric to determine how humans are changing the climate by burning fossil fuels. It is, however, not the abstract measure of global mean temperature that cause loss and damage from climate change, instead the impacts of climate change primarily manifest through rising sea levels and the changing risks of extreme weather events. For a long time it has not been possible to make the - arguably for the day to day life of most people crucial link – from anthropogenic climate change and global warming to individual weather and climate-related events with confidence but this has changed in recent years. Quantifying and establishing the link between individual weather events, that often lead to large damages, has been the focus of the emerging science of extreme event attribution. Attribution studies enhance climate science in two ways. Firstly, that even if a comprehensive inventory of the impacts of climate change today does not exists, nor is it discussed, attribution allows us to understand what climate change means. Today, to every one of us. Attribution brings climate change from an abstract threat in the future, to a concrete reality today. Secondly, disentangling predictable drivers of an extreme event like climate change, from natural variability and changes in vulnerability and exposure will allow a better understanding of where risks are coming from and in turn how they can be addressed. Extreme events open a window to address the problem of exposure and vulnerability. Scientific evidence of the importance of different drivers is essential to avoid playing blame games and allows instead for a well-informed debate about addressing risk.

By systematically recording key details of high-impact events on a national scale, combined with recent advances in event attribution and thus modifying the standard risk framework it would be possible to create inventories of climate change impacts. Not only would this id disaster preparedness and adaptation at local and national scales, such inventories also provide a comprehensive source of evidence for global stocktakes on adaptation and loss and damage such as mandated by the Paris Climate Agreement and would finally put adaptation on an equal footing with mitigation.

In the scientific reports, political debates, and to a large degree also in the media, the measure of global climate change around the world is global mean temperature rise used as the metric to determine how humans are changing the climate by burning fossil fuels. It is, however, not the abstract measure of global mean temperature that cause loss and damage from climate change, instead the impacts of climate change primarily manifest through rising sea levels and the changing risks of extreme weather events. For a long time it has not been possible to make the - arguably for the day to day life of most people crucial link – from anthropogenic climate change and global warming to individual weather and climate-related events with confidence but this has changed in recent years. Quantifying and establishing the link between individual weather events, that often lead to large damages, has been the focus of the emerging science of extreme event attribution. Attribution studies enhance climate science in two ways. Firstly, that even if a comprehensive inventory of the impacts of climate change today does not exists, nor is it discussed, attribution allows us to understand what climate change means. Today, to every one of us. Attribution brings climate change from an abstract threat in the future, to a concrete reality today. Secondly, disentangling predictable drivers of an extreme event like climate change, from natural variability and changes in vulnerability and exposure will allow a better understanding of where risks are coming from and in turn how they can be addressed. Extreme events open a window to address the problem of exposure and vulnerability. Scientific evidence of the importance of different drivers is essential to avoid playing blame games and allows instead for a well-informed debate about addressing risk.

By systematically recording key details of high-impact events on a national scale, combined with recent advances in event attribution and thus modifying the standard risk framework it would be possible to create inventories of climate change impacts. Not only would this id disaster preparedness and adaptation at local and national scales, such inventories also provide a comprehensive source of evidence for global stocktakes on adaptation and loss and damage such as mandated by the Paris Climate Agreement and would finally put adaptation on an equal footing with mitigation.

Since the inception of agriculture by mankind about ten millennia ago, the basis of the food supply for the human population has been the farming of field crops. However, our conventional, biogenic agriculture (CBA) has failed to provide a reliable concept to feed a growing population in a sustainable way. In particular CBA suffers from severe environmental externalities - such as the massive use of land area, water for irrigation, fertiliser, pesticides, herbicides, and fossil fuel.

Here we suggest the artificial synthesis of carbohydrates from (atmospheric) carbon dioxide (CO₂), water, and renewable energy. This approach would allow not only a highly reliable production without those externalities, but would also open the possibility to increase the agricultural capacity of our planet by several orders of magnitude. Our study shows that saccharose could be produced from CO₂, water and electrical energy with an efficiency exceeding 30%, or about 15 kWh per kg of sugar. Factoring in the efficiency of photovoltaic electricity generation we derive a „sun to sugar“ efficiency exceeding 6%, which is about 10-times the efficiency of CBA sugar beets or sugar cane.

All required technology is either commercially available or at least developed on a lab- scale. No directed research has, however, yet been conducted towards an industry- scale carbohydrate synthesis because the CBA carbohydrate production was thought to be cheaper. In fact we estimate the production costs of artificial sugar at about 1 €/kg while today’s spot market price for conventional sugar is about 0.3 €/kg. However, considering the environmental and socioeconomic externalities of the conventional sugar production, its total costs (including external costs) is estimated at 1 to 2 €/kg. Accordingly, artificial sugar appears already today to be the less expensive way of production.

Artificial sugar production allows also subsequent synthesis of other carbohydrates such as starch. Artificial carbohydrate production (vegan+) could provide an affordable and secure food supply for today's world population, drastically lower the ecological externalities of the food system, and free valuable land for alternative use like nature reservations or afforestation.

Of course, replacing part of our agricultural system by industrial production of food can have profound societal implications the discussion of which should be started now.

[1] S. Paschen and Q. Si, Quantum phases driven by strong correlations, Nat. Rev. Phys. 3, 9 (2021); The many faces (phases) of strong correlations, Europhys. News 52/4, 30 (2021).

We study the force-force correlator for disordered elastic systems. We show that each of the relevant universality classes has its own function. The nicest experiments are for DNA unzipping and Barkhausen noise. For the latter we observe two distinct universality classes, depending on the range of spin interactions. In all cases force- force correlations grow linearly at small distances, while they are bounded at large distances. As a consequence, avalanches are anti-correlated, i.e. reduced in size, at short distances.

Our theory is based on the functional renormalization group, which we compare to an alternative approach, namely mean-field theory with replica-symmetry breaking. The latter is one of the key achievements cited in this year’s Nobel prize for Giorgio Parisi. Another involves super mathematics, which we use to map charge-density waves to loop-erased random walks.

Festkolloquium zum 90. Geburtstag von Prof. Dr. Gisbert zu Putlitz

Highly charged ions (HCI) have many favorable properties for tests of fundamental physics and as potential next-generation optical atomic frequency standards [1]. For example, narrow optical fine-structure transitions have smaller polarizabilities and electric quadrupole moments, but much stronger relativistic, QED and nuclear size contributions to their binding energy compared to their (near) neutral counterparts. Therefore, HCI have been found to be among the most sensitive atomic species to probe for a possible variation of the fine-structure constant or dark matter coupling. HCI can readily be produced and stored in an electron beam ion trap (EBIT). There, the most accurate laser spectroscopy on any HCI was performed on the 17 Hz wide fine-structure transition in Ar13+ with 400 MHz resolution, lagging almost twelve orders of magnitude behind state-of-the-art optical clocks. This was primarily limited by Doppler broadening of the megakelvin hot ion plasma in the EBIT[2]. The lack of a suitable optical transition for laser cooling and detection can be overcome through sympathetic cooling with a co-trapped Be+ ion [3]. Techniques developed for quantum information processing with trapped ions can be used to perform quantum logic spectroscopy [4]: A series of laser pulses transfers the internal state information of the Ar¹³⁺ ion after spectroscopy onto the Be⁺ion for efficient readout.

We present the first coherent laser spectroscopy of an HCI. Ar¹³⁺ are extracted from a compact EBIT [5], charge-to-mass selected and injected into a cryogenic Paul trap containing a crystal of laser-cooled Be⁺ ions [6]. By removing excess Be+ ions, a crystal composed of a Be⁺/Ar¹³⁺ ion pair isobtained. Results on sympathetic ground state cooling and quantum logic spectroscopy of the Ar¹³⁺P_1/2-P_3/2 fine-structure transition at 441 nm will be presented, improving the precision of the observed line center by more than eight orders of magnitude. Furthermore, excited state lifetimes and the first high-accuracy measurement of excited state g-factor demonstrate the versatility of the technique to access all relevant atomic parameters [7]. Finally, we have started to perform frequency measurements of this transition, including first estimates of systematic uncertainties.

[1] M. G. Kozlov et al., Rev. Mod. Phys. 90, 045005 (2018).
[2] I. Dragani? et al., Phys. Rev. Lett. 91, (2003).
[3] L. Schmögeret al., Science 347, 1233–1236 (2015).
[4] P. O. Schmidtet al., Science 309, 749–752 (2005).
[5] P. Mickeet al., Rev. Sci. Instrum.89, 063109 (2018).
[6] T. Leopoldet al., Rev. Sci. Instrum. 90, 073201 (2019).
[7] P. Micke et al., Nature578 60-65 (2020).

[1] K. J. Satzinger et al., “Quantum control of surface acoustic wave phonons”, Nature 563, 661–665 (2018).
[2] A. Bienfait et al., “Phonon-mediated quantum state transfer and remote qubit entanglement”, Science 364, 368-371(2019).

“Why is water the unique fluid in which life occurs? What is the role of water in the myriad of processes – from catalysis to molecular recognition- that make up metabolism in the cell?” has been named as one of the top future challenges in chemistry.

Terahertz (THz) spectroscopy is a new tool to probe subtle changes in the hydration dynamics. THz spectroscopy gives a direct access to the collective, (sub-)psec hydrogen bond dynamics, the fingerprint of the water dynamics. [1,2]

Molecular understanding of biological recognition processes is surely a major prerequisite for future drug design. Protein-ligand binding is favorable when the change in free energy ΔG=ΔH-TΔS is negative. Up to now, calorimetric measurements are all based on heat transfer and are thus inherently slow (with relaxation times of typically 1 - 100s, depending on the system and method applied) and restricted to equilibrium conditions, so only either the bound or the unbound state – or stationary mixtures of both (determined by the equilibrium constant K) are characterized in terms of ΔG and ΔH. We propose that the THz spectrum of hydration water around solutes can be correlated with changes in entropy ΔS(t). In a proof of principle experiment on solvated alcohols we could show that this proposed correlation indeed holds [4]. THz calorimetry maps the solvent reorganization and will allow to record calorimetric properties far beyond equilibrium conditions.

Traditional bulk calorimetry yields enthalpy and entropy values as ensemble averages that include the contributions of the solute and solvent, as well as coupled solute-solvent interactions. Our scientific vision is to introduce ultrafast “THz-Calorimetry” , i.e. to correlate the THz spectrum with entropy changes of the solvent under non-equilibrium conditions with envisioned time resolutions of nano seconds. I will present linear and non-linear THz experiments to probe the change in hydrogen network dynamics in real time with ns or even fs time resolution [3]. Applications included enzymatic reactions, protein folding or charge transfer in electrochemical cells.

[1] V. Conti Nibali, M. Havenith, JACS2014, 136(37), 12800–12807
[2] J. Dielmann-Geßner et al.,PNAS2014, 111 (50), 17857–17862
[3] H. Wirtz, S. Schäfer, C. M. Hoberg, K.M. Reid, D. Leitner, M. Havenith,. Biochemistry 2018, 57 (26), 3650–3657
[4] F. Böhm, G. Schwaab, M. Havenith, Angew. Chem. Int. Ed. 2017, 56, 9981–9985

“Why is water the unique fluid in which life occurs? What is the role of water in the myriad of processes – from catalysis to molecular recognition- that make up metabolism in the cell?” has been named as one of the top future challenges in chemistry.

Terahertz (THz) spectroscopy is a new tool to probe subtle changes in the hydration dynamics. THz spectroscopy gives a direct access to the collective, (sub-)psec hydrogen bond dynamics, the fingerprint of the water dynamics. [1,2]

Molecular understanding of biological recognition processes is surely a major prerequisite for future drug design. Protein-ligand binding is favorable when the change in free energy ΔG=ΔH-TΔS is negative. Up to now, calorimetric measurements are all based on heat transfer and are thus inherently slow (with relaxation times of typically 1 - 100s, depending on the system and method applied) and restricted to equilibrium conditions, so only either the bound or the unbound state – or stationary mixtures of both (determined by the equilibrium constant K) are characterized in terms of ΔG and ΔH. We propose that the THz spectrum of hydration water around solutes can be correlated with changes in entropy ΔS(t). In a proof of principle experiment on solvated alcohols we could show that this proposed correlation indeed holds [4]. THz calorimetry maps the solvent reorganization and will allow to record calorimetric properties far beyond equilibrium conditions.

Traditional bulk calorimetry yields enthalpy and entropy values as ensemble averages that include the contributions of the solute and solvent, as well as coupled solute-solvent interactions. Our scientific vision is to introduce ultrafast “THz-Calorimetry” , i.e. to correlate the THz spectrum with entropy changes of the solvent under non-equilibrium conditions with envisioned time resolutions of nano seconds. I will present linear and non-linear THz experiments to probe the change in hydrogen network dynamics in real time with ns or even fs time resolution [3]. Applications included enzymatic reactions, protein folding or charge transfer in electrochemical cells.

[1] V. Conti Nibali, M. Havenith, JACS2014, 136(37), 12800–12807
[2] J. Dielmann-Geßner et al.,PNAS2014, 111 (50), 17857–17862
[3] H. Wirtz, S. Schäfer, C. M. Hoberg, K.M. Reid, D. Leitner, M. Havenith,. Biochemistry 2018, 57 (26), 3650–3657
[4] F. Böhm, G. Schwaab, M. Havenith, Angew. Chem. Int. Ed. 2017, 56, 9981–9985

In 1999 the presenter had his first encounter with an active volcano, and he made 2 important conclusions:

1. Volcanoes are amazing
2. Here is work to do

He contacted his friend and colleague Prof. Ulrich Platt, and together they launched an EU-project DORSIVA, aiming at Development of Optical Remote Sensing Instruments for Volcanic Applications. The rest is history....

Today DOAS instruments are running on more than 40 active volcanoes, providing real-time data on volcanic sulfur dioxide emission rates, used for geophysical and geochemical research and risk assessment. This talk will present successes and failures from 18 years work on active volcanoes, with a lot of pictures.

In 1999 the presenter had his first encounter with an active volcano, and he made 2 important conclusions:

1. Volcanoes are amazing
2. Here is work to do

He contacted his friend and colleague Prof. Ulrich Platt, and together they launched an EU-project DORSIVA, aiming at Development of Optical Remote Sensing Instruments for Volcanic Applications. The rest is history....

Today DOAS instruments are running on more than 40 active volcanoes, providing real-time data on volcanic sulfur dioxide emission rates, used for geophysical and geochemical research and risk assessment. This talk will present successes and failures from 18 years work on active volcanoes, with a lot of pictures.

References
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What is known as the “many-body problem” is the simple fact that our theoretical toolbox is generally incapable of handling systems containing many mutually interacting quantum particles. In one dimension, there exist however special theories for which exact solutions of the Schrödinger equation can be found. Long viewed as exceptional artifacts or irrelevant curiosities, these “integrable systems" have now come to play a major role in our understanding of quantum statistical mechanics and strongly-correlated systems.

This colloquium will motivate the importance and relevance of these theories, by showcasing how they relate to experimental realizations in magnetic and cold atomic systems, highlighting what they teach us about fundamental concepts such as equilibration, thermalization and universality, and hinting at what the future of (out-of-equilibrium) many-body quantum physics might hold in reserve.

Why are we brushing our teeth? To remove the formed biofilm and to prevent tooth decay. But why is the biofilm so sticky, which forces are involved? To answer theseand other questions, we teamed up with colleagues in microbiology and clinical dentistry to identify suitable model systems for the investigation and quantification of the forces involved. Our main experimental tool is force spectroscopy using an atomic force microscope (AFM). By mounting a single, living bacterium on the AFM cantilever, we can record force-distance-curves and study the adhesive force on a variety of surfaces under different conditions [1] . Force-distance-curves not only provide information about adhesive force and energy, but also about the length and stiffness of the proteins involved. Monte Carlo simulations guide the interpretation of the experimental force-distance curves and allow further insight in the type of involved forces [1,2].

The biofilm on our teeth not only consists of bacteria, but also of proteins. Further experiments probe the intermolecular forces between proteins as well as between proteins and surfaces. Certain properties of amphiphilic proteins lead to strong cohesion forces, allowing for the building of membranes and even vesicles [3,4]. How far can we go towards a pure-proteinaceous cell membrane? Can ion channels be inserted?

[1] N. Thewes et al, Stochastic binding of Staphylococcus aureus to hydrophobic surfaces, Soft Matter 11 (2015) 8913
[2] C. Spengler et al., Determination of the nano-scaled contact area of Staphylococcal cells, Nanoscale 9 (2017) 10084
[3] H. Hähl et al., Pure Protein Bilayers and Vesicles from Native Fungal Hydrophobins, Adv. Mat.29 (2017) 1602888
[4] H. Hähl et al, Adhesion Properties of Freestanding Hydrophobin Bilayers, Langmuir 34(2018) 8542

Heliophysics encompasses research of phenomena as diverse as dynamo processes in the solar interior, solar eruptions, the interaction of solar particles and field with the Earth’s magnetic field, and the aurora. These processes and interactions are largely of plasma physical nature. Heliophysics systems range from cold, collisional plasmas, to hot, collisionless, and even relativistic scenarios, the dynamics of many of which are still not or only poorly understood. We do know, however, that this chain of plasma physical processes does create beauty in form of the fascinating evolution of the solar corona to the intricate forms of the aurora. In addition to these captivating evolutions – and tantalizing science problems, Heliophysics can also affect society in a more sinister fashion. Eruptions and their impact on Earth can, for example, accelerate charged particles to energies large enough to damage satellites or harm humans in space. They can impact our ability to communicate with space infrastructure and affect GPS accuracy or access. Finally, large current systems, generated as part of violent dynamical processes in Earth’s magnetic field, can induce damaging currents in extended power grids.

This presentation will provide a primarily phenomenological overview of both sides of the Heliophysics coin: The scientific chain of processes and scientific puzzles, and their potential effects on our infrastructure.

Ultra-relativistic heavy-ion collisions create extreme conditions in temperature and energy density, such that a plasma of quarks and gluons, no longer confined in color-neutral hadrons, is produced. This is the state of matter, which existed a few microseconds after the Big Bang. Nowadays we investigate the properties of the hot, strongly-interacting plasma at accelerators such as the LHC at CERN, with fascinating instruments such as the ALICE experiment. I will guide you through this special wonderland and discuss the physics harvest of the last years of heavy-ion program at the LHC.

Furthermore, exciting plans lay ahead for the heavy-ion community: the high-luminosity era of the LHC for lead-ion collisions will start already in 2021, with interaction rates up to 50 kHz. The high statistics of precision data will open new frontiers in the field. At the same time, future opportunities at higher rates or/and energies are under consideration.

We investigated different kind of π–conjugated molecules in a combined scanning tunnelling (STM) and atomic force microscope (AFM). Whereas both measurement channel s show features with sub-molecular resolution, the information they can provide is truly complementary. For example, STM allows the direct imaging of the unperturbed molecular orbitals [1], whereas the AFM channel directly reveals the molecular geometry [2, 3]. When applied to STM-based single-molecule synthesis and on-surface chemistry, the combination of these techniques enables a direct quantification of the interplay of geometry and electronic coupling in real space [3, 4]. In particular, in many cases only the AFM channel enables discriminating different binding sites inside a single molecule, which is a prerequisite to obtain a full atomistic description of regioselectivity in on-surface chemistry [4]. Similarly, in the case of hydrogen-bonded molecular assembly the AFM provides direct insight into the bond rearrangement upon crystallization in two dimensions [5], which is elusive for STM.

The possibility of tailoring optical waveforms has allowed scientists to steer ultrafast electronic motion directly via the oscillating carrier wave of light – a principle dubbed “lightwave electronics” [6]. Terahertz (THz) scanning tunnelling microscopy [7] (THz-STM) has introduced a new paradigm by combining STM with lightwave electronics. In THz-STM, the electric field of a phase-stable single-cycle THz waveform acts as a transient bias voltage across an STM junction. In analogy to the all-electronic pump-probe scheme introduced recently in STM [8] these voltage transients may result in a net current that can be detected by time-integrating electronics.

By means of a low-noise low-temperature lightwave-STM we entered an unprecedented tunnelling regime, where the peak of a terahertz electric-field waveform opens an otherwise forbidden tunnelling channel through a single molecular orbital. In this way, the terahertz peak removes a single electron from an individual pentacene molecule’s highest occupied molecular orbital within a time win dow of ~100 fs – faster than an oscillation cycle of the terahertz wave. This quantum process allowed us to capture a microscopic real-space snapshot of the molecular orbital on a sub-cycle time scale. By correlating two successive state-selective tunnelling events, we directly tracked coherent THz vibrations of a single molecule in the time domain [9].

[1] J. Repp et al., Phys. Rev. Lett. 94, 026803 (2005)
[2] L. Gross et al., Science 325, 1110 (2009)
[3] F. Albrecht et al., JACS 137, 7424 (2015)
[4] N. Kocić et al., JACS 138, 5585 (2016).
[5] L. Patera et al., Angew. Chem. Int. Ed. 56, 10786 (2017)
[6] E. Goulielmakis et al., Science 317, 769 (2007)
[7] T. L. Cocker et al., Nature Photon. 7, 620 (2013)
[8] S. Loth et al., Science 329, 1628 (2010)
[9] T. L. Cocker et al., Nature 539, 263 (2016)

Local realism is the principle of locality (no instantaneous action-at-a-distance) joined to realism (the world and its properties exist whether or not we observe them). While it is typical for philosophers to question realism, in physics (as in common sense), realism was implicitly assumed until Niels Bohr and Wehner Heisenberg introduced the anti-realist Copenhagen Interpretation in 1927. Einstein's 1927-1935 arguments in support of local realism showed how Bohr's position describes a radically different understanding of physics and our relation to the physical world. In 1964 John Bell gave local realism a mathematical form, and showed that it was in principle testable. The first such Bell test was reported in 1970, and by now several generations of physicists have grappled with the problem of how to rigorously test a claim at the boundary of physics and philosophy.

After illust rating the nature of Bell tests with historical examples, I will turn to the problem of “loopholes” in Bell tests, a current obsession for the field. I will describe the so-called “loophole-free” tests, which combined high- efficiency detection methods, rigorous statistical analysis, purpose- built physical random number generators, and space-like separation, to simultaneously close the detection efficiency loophole, the locality loophole, the memory loophole, and other named loopholes. A quantum technology spin-off of these tests is the pulsed laser phase diffusion random number generator, which we built and analysed for the three “loophole-free” experiments of 2015. I will then turn my attention to recent tests that address in new ways the “freedom-of-choice” loophole, including steps toward a “cosmic" Bell test using extremely distant sources as randomness generators, and our recent “BIG Bell test" using 13 globally -distributed experiments and 100,000 human beings as randomness generators.

This talk is devoted to versatile studies, whose common point is the fact that beta or gamma decay from polarized radioactive nuclei is anisotropic in space.

Our experimental setup devoted to laser polarization of short-lived nuclei is located at the CERN-ISOLDE facility. We have already used it to polarize 35Ar beam with the aim to determine more precisely the Vud matrix element of the CKM quark mixing matrix. Soon, we plan to perform nuclear structure studies by measuring angular beta-gamma coincidences in order to assign spins and parities of nuclear excited states around 30Na, where observations are especially challenging for nuclear theory.

The main part of our present activities concerns beta-detected NMR, which is up to 10 orders of magnitude more sensitive than conventional NMR, due to a much higher degree of spin polarization and a much more efficient resonance detection via beta-decay asymmetry. We aim at using it for the studies of the interaction of proteins and DNA with metal ions, such as Na, Cu, Zn, which are crucial in many biological processes, including Alzheimer’s and Parkinson’s diseases. A further development concerns gamma-detected MRI, which can combine the strengths of the high sensitivity of PET and SPECT techniques with high spatial resolution of MRI by using polarized beams of longer-lived gamma-decaying nuclei.

In this talk I will introduce asymmetry of beta and gamma decay, will mention principles of laser polarization and the experimental setup, and will concentrate on selected aspects of the versatile research avenues mentioned above.

Which features make the brain such a powerful and energy-efficient computing machine? Can we reproduce them in the solid state, and if so, what type of computing paradigm would we obtain? I will show that a machine that uses memory to both process and store information, like our brain, and is endowed with intrinsic parallelism and information overhead - namely takes advantage, via its collective state, of the network topology related to the problem - has a computational power far beyond our standard digital computers [1]. We have named this novel computing paradigm “memcomputing” [2, 3, 4]. As examples, I will show the polynomial-time solution of prime factorization, the search version of the subset-sum problem [5], and approximations to the Max- SAT beyond the inapproximability gap [6] using polynomial resources and self-organizing logic gates, namely gates that self-organize to satisfy their logical proposition [5]. I will also show that these machines are described by a topological field theory, and they compute via an instantonic phase, implying that they are robust against noise and disorder [7]. The digital memcomputing machines we propose can be efficiently simulated, are scalable and can be easily realized with available nanotechnology components. Work supported in part by MemComputing, Inc. (http://memcpu.com/).

[1] F. L. Traversa and M. Di Ventra, Universal Memcomputing Machines, IEEE Transactions on Neural Networks and Learning Systems 26, 2702 (2015).
[2] M. Di Ventra and Y.V. Pershin, Computing: the Parallel Approach, Nature Physics 9, 200 (2013).
[3] M. Di Ventra and Y.V. Pershin, Just add memory, Scientific American 312, 56 (2015).
[4] M. Di Ventra and F.L. Traversa, Memcomputing: leveraging memory and physics to compute efficiently, J. Appl. Phys. (in press), arXiv:1802.06928.
[5] F. L. Traversa and M. Di Ventra, Polynomial-time solution of prime factorization and NP-complete problems with digital memcomputing machines, Chaos: An Interdisciplinary Journal of Nonlinear Science 27, 023107 (2017).
[6] F. L. Traversa, P. Cicotti, F. Sheldon, and M. Di Ventra, Evidence of an exponential speed-up in the solution of hard optimization problems, arXiv:1710.09278.
[7] M. Di Ventra, F. L. Traversa and I.V. Ovchinnikov, Topological field theory and computing with instantons, Annalen der Physik 1700123 (2017).