Sunday, 18 March

19:00 - Welcome reception

Monday, 19 March

8:30 - Welcome address

Welcome remarks from Prof. Peter Comba, Director of IWH, Prof. Karlheinz Meier, the Structures initiative, Prof. Carlo Ewerz, Scientific Coordinator of EMMI, and the organizers.

9:00 - Karlheinz Meier, "Physical Models of Brain Circuits - A non-Turing Approach to Computation"

The brain is a complex network of 10^11 nodes and 10^15 synaptic connections. It evolves in continuous interaction with the environment on timescales from milliseconds to years. Numerical simulations of this system provide some insights but are severely constrained by energy consumption and simulation times.

In 1982 Feynman postulated a method, in which the number of computer elements required to simulate a large physical system is proportional to the space-time volume of the physical system. Similar to todays quantum emulators neuromorphic systems follow this path by building physical models of brain circuits under user control rather than solving differential equations numerically.

Like the biological archetype physical model neuromorphic systems exhibit attractive features like energy efficiency, fault tolerance and the ability to learn.

The talk will introduce this approach and present some recent results.

9:45 - John Martinis, "Quantum Computing at Google"

A key step in the roadmap to build a useful quantum computer will be to demonstrate its exponentially growing computing power. I will explain how a 7 by 7 array of superconducting xmon qubits with nearest-neighbor coupling, and with programmable single- and two-qubit gate with errors of about 0.2%, can execute a modest depth quantum computation that fully entangles the 49 qubits. Sampling of the resulting output can be checked against a classical simulation to demonstrate proper operation of the quantum computer and compare its system error rate with predictions. With a computation space of 2^49 = 5 x 10^14 states, the quantum computation can only be checked using the biggest supercomputers. I will show experimental data towards this demonstration from a 9 qubit adjustable-coupler “gmon” device, which implements the basic sampling algorithm of quantum supremacy for a computational (Hilbert) space of about 500. We have begun testing of the quantum supremacy chip.

10:30 - Coffee break
11:00 - Giuseppe Carleo, "Machine Learning for Quantum Many-Body Physics"

In this talk I will present recent applications of machine-learning-based approaches to quantum physics. First, I will discuss how a systematic machine learning of the many-body wave-function can be realized. This goal has been achieved in [1], introducing a variational representation of quantum states based on artificial neural networks. In conjunction with Monte Carlo schemes, this representation can be used to study both ground-state and unitary dynamics, with controlled accuracy. Moreover, I will show how a similar representation can be used to perform efficient Quantum State Tomography on highly-entangled states [2], previously inaccessible to state-of-the art tomographic approaches.

[1] Carleo, and Troyer – Science 355, 602 (2017).

[2] Torlai, Mazzola, Carrasquilla, Troyer, Melko, and Carleo – Nature Physics (in press, 2018) arXiv:1703.05334.

11:45 - Mihai Petrovici, "Spiking Neuron Ensembles and Probabilistic Inference"

The ability to perform probabilistic (Bayesian) inference is a hallmark of mammalian cognition and a coveted feature for embedded AI. Recent developments in machine learning have tried to capture this kind of computation with so-called “deep” architectures, but the analogy to biology remains superficial. I will discuss a framework for cognitive computation with spiking neurons that narrows the gap between biological and artificial deep networks, while employing well-documented aspects of cortical dynamics such as spike-based communication, operation in a high-conductance state, short-term plasticity and background-driven stochasticity. By design, these models lend themselves to neuromorphic implementation, which allows them to profit from the advantages offered by these new technologies.

Spiking Neuron Ensembles and Probabilistic Inference

12:30 - Lunch
14:00 - Martin Gärttner, "Neural Network Representation of a Near-critical Quantum Ising System out of Equilibrium"

A new method to describe the unitary evolution of interacting quantum many-body systems has been introduced recently which is based on the representation of quantum states in terms of an artificial neural network (ANN). Focusing on the spin 1/2 quantum Ising model with transverse and longitudinal field after a quench near criticality, we study the prospects and limitations of this method. We compare our results to those obtained with exact analytical results, with a semi-classical discrete Truncated-Wigner approach, and with tDMRG simulations. We find that the dTWA gives good results only at short times or near zero transverse field. The ANN approach works well in a much wider range of parameters. Only in regimes where long-range spin correlations build up the long-time dynamics becomes unstable and deviates from the exact results. The ANN approach yields qualitatively correct results in regimes where the entanglement entropy in the long-time limit is extensive.

Presentation Slide: BDC2018_Martin_Gaerttner

14:45 - Xiaopeng Li, "Machine Learning Approaches to Entangled Quantum States"

Artificial neural networks play a prominent role in the rapidly growing field of machine learning and are recently introduced to quantum many-body systems. This talk will focus on using a machine-learning model, the restricted Boltzmann machine (RBM) to describe entangled quantum states. Both short- and long-range coupled RBM will be discussed. For a short-range RBM, the associated quantum state satisfies an entanglement area law, regardless of spatial dimensions. I will present our recently constructed exact RBM models for nontrivial topological phases, including a 1d cluster state and a 2d toric code. For a long-range RBM, the captured entanglement entropy scales linearly with the number of variational parameters in the RBM model, in sharp contrast to the log-scaling in matrix product state representation.

15:30 - Coffee break
16:00 - Simon Trebst, "Machine Learning Quantum Phases of Matter"

Machine learning techniques have become ubiquitous, but often hidden helpmates in our daily life. This includes pattern recognition technologies that have long filtered data in electronic mailboxes and have more recently become powerful enough to identify users by the touch of a button or the scan of a face.

In my talk, I will briefly review the algorithmic foundations of machine learning approaches and then turn to their application in the context of statistical physics problems. I will demonstrate that machine learning techniques are capable to discriminate phases of matter by extracting essential features in the many-body wavefunction or the ensemble of correlators sampled for instance in Monte Carlo simulations. Of particular interest are quantum many-fermion problems that have long resisted a thorough numerical understanding — will we be able to guide our understanding of the emergence of superconductivity or topological order in such systems using machine learning?

Machine Learning Quantum Phases of Matter

16:45 - Christof Wunderlich, " Speeding-up the Decision Making of a Learning Agent Using an Ion Trap Quantum Processor"

We report a proof-of-principle experimental demonstration of the quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions [1]. The decision-making process of a quantum learning agent within the projective simulation paradigm for machine learning is implemented in a system of two qubits. The latter are realized using hyperfine states of two frequency-addressed atomic ions exposed to a static magnetic field gradient. We show that the deliberation time of this quantum learning agent is quadratically improved with respect to comparable classical learning agents. The performance of this quantum-enhanced learning agent highlights the potential of scalable quantum processors taking advantage of machine learning.

[1] Th. Sriarunothai et al., arXiv: 1709.01366 (2017).

19:00 - Symposium's dinner

Tuesday, 20 March

9:00 - Stephen Furber, "The SpiNNaker Project"

The SpiNNaker (Spiking Neural Network Architecture) project aims to produce a massively-parallel computer capable of modelling large-scale neural networks in biological real time. The machine has been 18 years in conception and ten years in construction, and has so far delivered a 500,000-core machine in six 19-inch racks, and now being expanded towards the million-core full system. Although primarily intended as a platform to support research into information processing in the brain, SpiNNaker has also proved useful for Deep Networks and similar applied Big Data applications. In this talk I will present an overview of the machine and the design principles that went into its development, and I will indicate the sort of applications for which it is proving useful.

Presentation Slide: BDC2018_Steve_Furber

9:45 - Rainer Blatt, "Quantum Computations and Quantum Simulations with Trapped Ions"

The quantum toolbox of the Innsbruck ion-trap quantum computer is applied to simulate the dynamics and to investigate the propagation of entanglement in a quantum many-body system represented by long chains of trapped-ion qubits [1]. With strings of up to 10 ions, a dynamical phase transition was recently observed [2] and an efficient procedure for the characterization of a quantum many-body system of up to 14 entangled ions has been implemented [3].

Moreover, using the quantum toolbox operations, universal (digital) quantum simulation was realized with a string of trapped ions [4]. Here we report the experimental demonstration of a digital quantum simulation of a lattice gauge theory, by realizing (1 + 1)-dimensional quantum electrodynamics (the Schwinger model) on a few-qubit trapped-ion quantum computer [3]. We are interested in the real-time evolution of the Schwinger mechanism, describing the instability of the bare vacuum due to quantum fluctuations, which manifests itself in the spontaneous creation of electron–positron pairs. To make efficient use of our quantum resources, we map the original problem to a spin model by eliminating the gauge fields in favor of exotic long-range

interactions, which can be directly and efficiently implemented on an ion trap architecture.

[1] P. Jurcevic et al., Nature 511, 202 (2014)

[2] P. Jurcevic et al., Phys. Rev. Lett. 111, 080501 (2017)

[3] B. P. Lanyon et al., Nature Physics 13, 1158 (2017)

[4] E. A. Martinez et al., Nature 534, 516 (2016)

Presentation Slides: BDC2018_Rainer_Blatt

10:30 - Coffee break
11:00 - Antonio Acin, "Detecting Entanglement and Non-Local Correlations of Many-Body Quantum States"

Quantum correlations are fundamental for quantum information protocols and for our understanding of many-body quantum physics. The detection of these correlations in these systems is challenging because it requires the estimation and processing of an exponentially growing amount of parameters. We present methods to alleviate these problem and discuss their application to physically relevant quantum states.

Presentation Slides: BDC2018_Antonio_Acin

11:45 - Sebastian Huber, "Automated Phase and Low Energy Detection"

Classifying phases of matter is key to our understanding of many problems in physics. For quantum-mechanical systems in particular, the task can be daunting due to the exponentially large Hilbert space. With modern computing power and access to ever-larger data sets, classification problems are now routinely solved using machine-learning techniques. Here, we propose a neural-network approach to finding phase transitions, based on the performance of a neural network after it is trained with data that are deliberately labelled incorrectly. We demonstrate the success of this method on the topological phase transition in the Kitaev chain, the thermal phase transition in the classical Ising model, and the many-body-localization transition in a disordered quantum spin chain. Our method does not depend on order parameters, knowledge of the topological content of the phases, or any other specifics of the transition at hand.

Presentation Slides: BDC2018_Sebastian_Huber

12:30 - Lunch
14:00 - Martin Plenio, "Diamond Quantum Simulator Architectures"

In this talk I will present some ideas for quantum simualators that may be operated at room temperature.

Presentation slides: BDC2018_Martin_Plenio

14:45 - Iris Schwenk, "Quantum simulation without quantum error correction - on the way to applications in chemical and pharmaceutical industry" (Hot Topic)

Quantum simulation is a tool to investigate problems, e.g., in chemistry or condensed matter physics, that are not solvable analytically or on classical computers. However, the inevitability of perturbations constitutes a major roadblock to useful quantum simulations. Since we cannot predict the result of the simulation, it is difficult to estimate the effect of perturbations.

We show that in specific systems a measurement of additional correlators can be used to verify the reliability of the quantum simulation. The procedure only requires additional measurements on the quantum simulator itself. Besides, we present a method which, in certain circumstances, allows for the reconstruction of the ideal result from measurements on a perturbed quantum simulator.

To exploit near term applications for quantum simulation we have founded a company to develop quantum algorithms to predict material properties for chemical and pharmaceutical companies. We will discuss the various steps that are necessary to implement quantum chemical problems on a quantum computer and the challenges involved in solving concrete consumer problems. We intend for our software to be hardware agnostic and to work on conventional and state of the art quantum computers.

15:15 - Nikolaj Zinner, "Quantum Spin Transistors in Superconducting Circuits" (Hot Topic)

Starting from ideas developed in the realm of strongly interacting cold atoms, I will show how to realize quantum spin transistors in small spin networks. Then I will outline how these could be realized in current superconducting platforms using transmons or flux qubits. Other neat applications of these principles for modular quantum computation includes small quantum routers and quantum spin diodes.

15:45 - Christof Weitenberg, "Topology and Dynamics in Driven Hexagonal Lattices" (Hot Topic)

Ultracold atoms are a versatile system to study the fascinating phenomena of gauge fields and topological band structures. By Floquet driving of optical lattices, the topology of the Bloch bands can be engineered. This poster presents experimental schemes for momentum-resolved Bloch state tomography, which allow mapping out the Berry curvature and obtaining the Chern number. Furthermore, it discusses the dynamics of the wave function after a quench into the Floquet system. We observe the appearance of dynamical vortices, which trace out a closed contour, the topology of which can be directly mapped to the Chern number. Our measurements provide a new perspective on topology and dynamics and a unique starting point for studying interacting topological phases.

16:15 - Bus transfer to the Neuenheim Feld Campus
16:45 - Coffee and Lab tours
18:00 - Poster session
19:00 - BBQ Dinner
21:30 - Bus transfer to the old town

Wednesday, 21 March

9:00 - Lincoln Carr, "Complex Networks on Quantum States: from Quantum Phase Transitions to Emergent Dynamics of Quantum Cellular Automata"

Complex networks defined on quantum states via quantum mutual information turn out to give a surprising level of new insight on physical problems ranging from quantum critical phenomena to far-from-equilibrium quantum dynamics. Measures on such networks serve to rapidly and efficiently identify quantum critical points for workhorse many-body models studied in present quantum simulator experiments. A small modification of such models allows one to produce entangled quantum cellular automata. Complex network-based averages and dynamics serve as key quantifiers for emergent complexity along with localized robust dynamical structures and entropy fluctuations. They show that a new class of highly entangled yet also highly structured quantum states arise out of dynamics just a short step away from present experimental protocols. They also identify a set of simple criteria, called Goldilocks Rules, which consistently produce complexity independent of the details of the protocol.

9:45 - Jacob Biamonte, "Towards Optimality Results in the Alternating Operator Ansatz"

We present some recent and ongoing results related optimal solutions to the alternating operator ansatz (QAOA). This is the backbone behind gate-model based quantum deep learning networks based on generative Boltzmann machine models. Time permitting, we will present results about those as well.

10:30 - Coffee break
11:00 - Bettina Heim, "Leveraging the Power of Quantum for Machine Learning"

Quantum machine learning is an exciting new field emerging at the intersection between quantum computing and machine learning (ML). On one hand, advances in quantum algorithms provide new approaches for widely used classical routines that could improve both training of and sampling from ML models by increasing accuracy and/or efficiency, or allowing for richer model classes. On the other hand quantum computers are a natural fit for applying machines learning techniques to study quantum systems, where the ability to more easily process and model quantum states could give a significant edge over classical approximations.

The development of quantum computing devices with an increasingly large number of qubits encourages to investigate the practicality of these algorithms and what progress one could hope for as quantum technology matures. In my talk I will give an overview over existing quantum machine learning algorithms, their potential, and their caveats. 

11:45 - Christine Muschik, "Real-Time Dynamics of Lattice Gauge Theories with a Few-Qubit Quantum Computer"

Lattice gauge theories describe fundamental phenomena in nature, but calculating their real-time dynamics on classical computers is notoriously difficult. In a recent publication [Nature 534, 516 (2016)], we proposed and experimentally demonstrated a digital quantum simulation of 1+1-dimensional quantum electrodynamics (Schwinger model) on a few-qubit trapped-ion quantum computer. We are interested in the real-time evolution of the Schwinger mechanism, describing the instability of the bare vacuum due to quantum fluctuations, which manifests itself in the spontaneous creation of electron-positron pairs. To make efficient use of our quantum resources, we map the original problem to a spin model by eliminating the gauge fields in favour of exotic long-range interactions, which have a direct and efficient implementation on an ion trap architecture. Our work represents a first step towards quantum simulating high-energy theories with atomic physics experiments.

12:30 - Lunch
14:00 - Antonio Mezzacapo, "Dealing with imperfect quantum machines"
Near-term applications of quantum computers on current quantum devices are hindered by both control and decoherence issues. While we research a path to fully fault-tolerant devices, understanding how to deal with noise and imperfect control is of outmost importance. In this talk we are going to address the most promising applications of early-stage quantum computers, and how they can benefit from short-depth algorithms and error mitigation of quantum observables measured on real devices, without error-correcting their underlying quantum state.
14:45 - Christian Gross, "Novel Detection Possibilities with Quantum Gas Microscopes: From Hidden Correlations to Incommensurate Magnetism"

The rich physics of the Fermi-Hubbard model arises due to the interplay of the charge and spin degrees of freedom. Here we highlight novel detection methods for these degrees of freedom using spin and charge resolved single atom detection in ultracold lattice systems. The detection of the full local and global counting statistics allows us to analyze non-local correlation functions and perform data-postselection, which reveal hidden spin correlations and incommensurate magnetism in 1D chains.

Presentation Slide: BDC2018_Christian_Gross

15:30 - Coffee break
16:00 - Jacob Sherson, "Remote Connected Science, Hybrid Human-Machine Learning in Quantum Physics and Beyond"

Despite enabling impressive advances, the big-data driven deep learning paradigm has been challenged by AI scholars for not holding the potential to reach human scale intelligence. Instead, they propose studies of human psychology as a basis for hybrid human-machine intelligence. An open question for the future of research is therefore how to design interfaces that allow for an optimal interaction between human intuition, complex machinery, and increasingly powerful ML.

In the project, we have developed gamified interfaces allowing so far 250,000 players to contribute to research by providing insightful seeds for quantum optimization algorithms and remote access to our ultra-cold atoms experiment for amateur scientists, students, and researchers. Finally, I will discuss our effort to provide efficient, game-based heuristics for NP-hard computational problems related to spin glasses and ongoing efforts to demonstrate quantum supremacy using quantum annealing.

Presentation Slide: BDC2018_Jacob_Sherson


Shahnawaz Ahmed, "Learning constraints: A walk through the Deep Learning zoo with Sudoku and ellipsoids"
Many Deep Neural Network architectures and tricks that are currently used for training are poorly understood beyond a heuristic level. We discuss the learning of various types of constraints using several Deep Learning architectures. Specifically, we study the learning of the rules of Sudoku and various polynomial function based constraints. We design the network weights from scratch to understand how constraints are learned. The necessity of techniques such as skip connections and drop out are clear from this approach. Limits on the number of neurons in the first hidden layer are established which was discussed in the context of learning the Ising model near criticality. The goal is learning symmetries, complex relations and constraints in data rather than just obtaining a correct prediction. In science, Deep Learning can only be beneficial if we are able to extract learned relationships and constraints in the data rather than just make predictions using a black box approach.
Andrey Bagrov, "Applications of the AdS/CFT-holography to many-body quantum systems"
Luca Bayha, "Anomalous breaking of scale invariance in a two dimensional Fermi gas"
System lacking an absolute scale in the Hamiltonian show the same behavior on all scales. An example of such a scale invariant system is the classical Fermi gas in two dimensions with contact interactions. When adding a harmonic potential, the breathing mode frequency is fixed by the scale invariance of the classical gas. On the quantum mechanical level, however, the scale invariance is broken by introducing the two dimensional scattering length as a regulator. This quantum anomaly leads to a shift of the frequency of the breathing mode of the cloud. Here I present our experimental study of this frequency shift for a two component Fermi gas in the strongly interacting regime. We observe a significant shift away from the scale invariant result depending on both interactions and temperature. A careful analysis of all the additional terms that may lead to explicit breaking of scale invariance is required to distinguish their effect from the effects caused by the anomaly.
Andreas Baumbach, "Computation with Spiking Neural Networks"
Biologically inspired networks of spiking neurons can be used to implement Boltzmann machines, that can be trained to perform a multitude of tasks, including pattern completion and classification. We will present the implementation and configuration of these networks and propose ideas how we can link these implementations to recent work on the Quantum Many Body problems.
Stefanie Czischek, "Artificial Neural Network Representation of Spin Systems in a Quantum Critical Regime"
We use the newly developed artificial-neural-network (ANN) representation of quantum spin-1/2 states based on restricted Boltzmann machines to study the dynamical build-up of correlations after sudden quenches in the transverse-field Ising model (TFIM). We calculate correlation lengths and study their time evolution after sudden quenches into the vicinity of the quantum critical point. By comparison with exact numerical solutions we show that in the close vicinity of the quantum critical point, in the regime of large correlations, large network sizes are necessary to capture the exact dynamics. On the other hand we show a high accuracy of the ANN representation in regimes with smaller correlations even for small network sizes. By looking at the TFIM in an additional longitudinal field, we find the same behavior of the ANN representation by comparison with DMRG caclulations for a not exactly solvable system, which suggests that the method be efficiently used for more complex systems.
Martin Gärttner, "Spatially distributed multipartite entanglement enables Einstein-Podolsky-Rosen steering of atomic clouds"
A key resource for distributed quantum-enhanced protocols is entanglement between spatially separated modes. Yet, the robust generation and detection of nonlocal entanglement between spatially separated regions of an ultracold atomic system remains a challenge. Here, we use spin mixing in a tightly con- fined Bose-Einstein condensate to generate an entangled state of indistinguish- able particles in a single spatial mode. We show experimentally that this lo- cal entanglement can be spatially distributed by self-similar expansion of the atomic cloud. Spatially resolved spin read-out is used to reveal a particularly strong form of quantum correlations known as Einstein-Podolsky-Rosen steering between distinct parts of the expanded cloud. Based on the strength of Einstein-Podolsky-Rosen steering we construct a witness, which testifies up to genuine five-partite entanglement.
Gabriel Andres Fonseca Guerra, "Testing Local Realism for Theories of the Brain"
The violation of local realism is perhaps a more striking feature of quantum mechanics than quantization itself. Bell experiments test whether such violations exist in a physical system and if so, rule out a theory of local hidden variables which could explain the observed phenomena, prevailing the quantum over the classical interpretation. On a rather different spatiotemporal scale, resides the brain as a theoretical challenge for a unified theory. Here we explore the (non-)locality of brain states in a search for bounds on the quantum-like requirements for a theory of the brain.
Stephan Helmrich, "Self-organised critical states in driven-dissipative atomic gases"
The competition between driving, interactions and dissipation in physical systems can lead to the emergence of out-of-equilibrium phases as well as the extreme case of universal regimes. We prepare such phases of matter in driven-dissipative samples of ultracold atomic systems excited to strongly interacting Rydberg states. For weak driving we find that the system remains in an inactive state, while for strong driving the system self-organises into a critical state through subsequent cycles of facilitated excitation and decay events. We probe the nature of this self-organised critical state by analysing its scaling behaviour, temporal dynamics and susceptibility.
Luca Innocenti, "Supervised learning of time-independent Hamiltonians for gate design"
The last years have seen a growing interest in the merging of the fields of classical machine learning and quantum physics. In particular, a number of machine learning techniques originally developed for big data analysis have been fruitfully adapted to problems in quantum information science and many-body theory (Carleo and Troyer 2017, Torlai et al. 2017). While in these works standard neural network architectures have been used, the same training techniques can be applied to substantially different architectures. This kind of neural-network-inspired optimisation has already been demonstrated in the context of quantum gate learning (Banchi et al. Nature npjqi 2016). Building on that work we generalise their method and using automatic differentiation we make it possible to explore networks of 8 and more qubits, optimising over hundreds of possible pairwise interactions. We use this method to train qubit networks finding sets of interactions implementing target quantum gates.
Miroslav Jezek, "Photonic simulation in quantum thermodynamics, quantum computing, and communication"
I briefly review our experimental work on conditional cooling of quantum channels [10.1038/srep16721] and qubit interaction enhancement [10.1103/PhysRevA.92.022341, 10.1038/srep32125]. Furthermore, I present recent results on quantum thermodynamics simulations employing photon statistics generation and manipulation [10.1038/s41598-017-13502-0, arXiv:1801.03063].
Selim Jochim, "The Heidelberg Quantum Architecture"
A novel quantum simulation experiment using ultracold atoms will be set up at Heidelberg University with parameters matched in such a way that its inputs and outputs can be interfaced with a neuromorphic hardware developed within the Human Brain project. Essential ideas of the approach being pursued will be presented.
Ralf Klemt, "Direct observation of correlations and entanglement in two-fermion quantum states"
Entanglement is a defining feature of quantum many-body states and the central resource for quantum communication and computing. However, in particular in itinerant systems, entanglement is notoriously difficult to characterize experimentally. A key role is therefore played by small but deterministically prepared systems which allow us to certify the emergence of correlations and entanglement in a direct and well-controlled fashion. In the work presented on this poster, we deterministically prepare strongly interacting quantum states of two fermionic atoms trapped in a double-well potential. We measure both the in-situ and momentum distribution with single atom resolution, enabling us to extract all relevant correlation functions. Strong correlations indicating high coherence in our system allow us to observe the emergence of entanglement in the mode as well as the particle degree of freedom, and to identify relevant witnesses, measures and protocols to characterize it.
Philipp Kunkel, "Spatially distributed multipartite entanglement enables Einstein-Podolsky-Rosen steering of atomic clouds"
A key resource for distributed quantum-enhanced protocols is entangled states between spatially separated modes. Here, we use spin mixing in a tightly confined BEC of 87Rb to generate a squeezed vacuum state in a single spatial mode. We show experimentally that the corresponding local entanglement can be spatially distributed by self-similar expansion of the atomic cloud in a waveguide potential. Spatially resolved spin read-out is used to reveal EPR steering between distinct parts of the expanded cloud. Building on the ability to partition the system arbitrarily, we show three-way steering. To quantify the connection between the strength of EPR steering and genuine multipartite entanglement we construct a witness, which reveals up to genuine five partite entanglement.
Daniel Linnemann, "Spatially distributed multipartite entanglement enables Einstein-Podolsky-Rosen steering of atomic clouds"
A key resource for distributed quantum-enhanced protocols is entangled states between spatially separated modes. Here, we use spin mixing in a tightly confined BEC of 87Rb to generate a squeezed vacuum state in a single spatial mode. We show experimentally that the corresponding local entanglement can be spatially distributed by self-similar expansion of the atomic cloud in a waveguide potential. Spatially resolved spin read-out is used to reveal EPR steering between distinct parts of the expanded cloud. Building on the ability to partition the system arbitrarily, we show three-way steering. To quantify the connection between the strength of EPR steering and genuine multipartite entanglement we construct a witness, which reveals up to genuine five partite entanglement.
Markus Mittnenzweig, "Hybrid quantum-classical modeling for the simulation of quantum dot devices"
We introduce a new hybrid quantum-classical modeling approach for the simulation of electrically driven quantum dot devices by coupling the semi-classical drift-diffusion system with a quantum master equation in Lindblad form. Our approach enables the calculation of quantum optical figures of merit and the spatially resolved simulation of the current flow in realistic device geometries in a unified way. We prove that the hybrid model is consistent with fundamental axioms of (non-)equilibrium thermodynamics, in particular it guarantees the second law. The approach is demonstrated by numerical simulations of an electrically driven single-photon source in the stationary and transient operation regime.
Katja Mombaur, "How can model-based optimization and neural networks be most efficiently combined for motion control and prediction?"
Optimal control based on realistic physical models is a powerful method for motion prediction and control for humans, robots and assistive robotic technology. While this approach allows to precisely describe all mechanical properties including kinematic and dynamic limitations, the dynamics of technical actuators and muscles as well as behavior rules, and important drawback are the high computation times. In previous research we have successfully combined optimal control and Bayesian machine learning methods by learning movement primitives based on optimal control solutions that served as training data, allowing us to generate variable walking motions for complex humanoid robots. In current research, we are interested to explore if the results could be even improved by combining neural networks and optimal control methods and how this could be most efficiently done taking software and hardware aspects into account. NN alone have so far not been able to solve such problems.
Puneet Murthy, "Exploring two-dimensional Fermi systems with ultracold atoms"
Johannes Otterbach, "Unsupervised Machine Learning on a Hybrid Quantum Computer"
Machine learning techniques have led to broad adoption of a statistical model of computing. The statistical distributions natively available on quantum processors are a superset of those available classically. Harnessing this attribute has the potential to accelerate or otherwise improve machine learning relative to classical performance. A key challenge toward that goal is learning to hybridize classical computing resources and traditional learning techniques with the emerging capabilities of general purpose quantum processors. Here, we demonstrate such hybridization by training a 19-qubit gate model processor to solve a clustering problem. We use the quantum approximate optimization algorithm in conjunction with a gradient-free Bayesian optimization to train the quantum machine. This quantum/classical hybrid algorithm shows robustness to realistic noise, and we find evidence that classical optimization can be used to train around both coherent and incoherent imperfections.
Davide Pastorello, "Two-way quantum key distribution based on tripartite entanglement"
Entanglement is a well-known resource in quantum information. In particular, it can be exploited for quantum key distribution (QKD). We propose a two-way QKD scheme employing GHZ-type states of three qubits obtaining an extension of the standard E91 protocol with a significant increase in the number of shared bits. Eavesdropping attacks can be detected measuring violation of the CHSH inequality and the secret key rate can be estimated in a device-independent scenario.
Cartik Sharma, "Source quantization of EEG studies using quantum machine learning"

We propose mathematical formulations for localizing source acitivity in EEG data sets for PTSD victims using quantum machine learning. We explore several techniques in source localization for global optimization and select the Ising annealing model to localize source activity instantaneously. Calculations are performed on a DWAVE 2000 for quantum computation and are rapid and efficient with high accuracy. We also adopt the use of restricted boltzmann machines based on adaptive filtering and parametric fitting to obtain optimal solutions. The data used for this exercise and proposal are eeg wave trains for 4D datasets in a spatio-temporal sense. We compare source quantization with conventional classical techniques and find that adaptive speed improvements lend itslef to clinical success and validity as concerns to diagnosing the PTSD problem. Several iterations of continuous calculation allow for expedited results and neural recovery.

Tom Tetzlaff, "Deterministic networks for probabilistic computing"
Neuronal-network models of high-level brain function often rely on the presence of stochasticity. The majority of these models assumes that each neuron is equipped with its own private source of randomness, often in the form of uncorrelated external noise. In biological neuronal networks, the origin of this noise remains unclear. In hardware implementations, the number of noise sources is limited due to space and bandwidth constraints. Hence, neurons in large networks have to share noise sources. We show that the resulting shared-noise correlations can significantly impair the computational performance of stochastic neuronal networks, but that this problem is naturally overcome by generating noise with deterministic recurrent neuronal networks. By virtue of the decorrelating effect of inhibitory feedback, a network of a few hundred neurons can serve as a natural source of uncorrelated noise for large ensembles of functional networks, each comprising thousands of units.
Tobias Thommes, "Design and Implementation of an EXTOLL network-interface for the communication-FPGA in the BrainScaleS neuromorphic computing system"

The Human Brain Project (HBP) aims to understand by means of Synthesis Biology how the inconceivably efficient system of the Human Brain works. The BrainScaleS system at the Kirchhoff-Institute for Physics in Heidelberg is part of the HBP and pursues this goal by developing a neuromorphic analog hardware system in combination with a conventional computing cluster. This poster summarizes the development of a new network interface for the FPGAs controlling the data communication between the neuromorphic hardware chips and the conventional digital system. The new interface will enable the BrainScaleS system to use the benefits of the Extoll network, a high-performance interconnection network, optimized for low latency and high message rates.

Yuuki Tokunaga, "Cavity/Circuit QED-based quantum computing"

Cavity/circuit QED is a promising system for realizing quantum information processing, because the deterministic atom-photon interaction can efficiently make single photon sources, and atom-photon quantum gates. One of the difficulty of realizing scalable quantum computing is the trade-off between the high-fidelity operation and the integration of many qubits. Cavity/circuit QED systems may solve the problem by deterministically connecting remote atomic systems through flying photons/microwave photons and by efficiently reproducing lossy photonic qubits. Here, I present two physical systems from my collaborative works. One is the nanofiber cavity QED systems, which is a promising candidate for obtaining the high cooperativity and cavity array systems. The other is a circuit QED system that can make entanglement between remote superconducting atoms by using a quantum gate between a superconducting atom and a propagating microwave photon [Phys. Rev. Applied, 7, 064006 (2017)].

Sabine Tornow, "A Quantum Information Course for Computer Science Students"
A quantum information course is presented for students of computer science at a university of applied sciences in Germany. The lecture is built on Python simulation exercises covering different toy models, quantum algorithms and quantum error correction.
Artem Volosniev, "Using cold atoms to engineer a spin chain with perfect state transfer"
"How a quantum state (or information about it) can be transmitted" is the question that arises when one thinks about quantum computing. We study this problem in a one-dimensional gas of strongly-interacting cold atoms [1,2]. It is shown that these systems give one the opportunity to simulate inhomogeneous Heisenberg Hamiltonians, where the spin-spin interactions are determined by the shape of the trapping potential. Therefore, they can be used to engineer spin chains that enjoy perfect state transfer. We illustrate our findings using a simple yet non-trivial four-body system. [1] A. G. Volosniev, D. Petrosyan, M. Valiente, D. V. Fedorov, A. S. Jensen, and N. T. Zinner Phys. Rev. A 91 023620 (2015) [2] O. V. Marchukov, A. G. Volosniev, M. Valiente, D. Petrosyan, and N. T. Zinner Nature Commun. 7 13070 (2016)
Yibo Wang, "Q-Walker: a fully-programmable quantum dynamics simulator with Rydberg-dressed atoms"
The transport of energy, charge and information is of fundamental importance in nature and technology ranging from (bio)physical processes to the operation of nano-electronic devices. Building on experimental advances with Rydberg atoms as controlled quantum many-body systems, we aim to establish a programmable quantum dynamics simulator “Q-walker”, which is suited to studying quantum transport on complex networks. The key components include fully configurable arrays of individual atoms, tunable long-range interactions for mediating couplings, local control over system-reservoir interactions, and time- space- and state-resolved readout. Q-Walker can be used to study quantum and classical transport in complex networks and to mimic the fundamental processes played in biological networks such as light-harvesting complexes. We hope to answer questions concerning the role of quantum coherence in photosynthesis and devise methods for classifying complex quantum networks.
Ralf Wessel, "Computing Vision with Neural Circuits Operating at Self-Organized Criticality"
The confusing thicket of malleable connections between billions of neurons renders the brain a complex adaptive system. The subjective experience of vision is thought to emerge from the impact of incoming spatiotemporal stimuli onto this pliable tangle of neuronal interactions. Yet, to date, a convincing computational framework for the processing of visual stimuli in neural circuits remains elusive. The construction of a solid understanding of cortical circuit dynamics is likely to provide a useful launch pad for ongoing and future investigations of cortical computation and sensory processing. I will present new insight into cortical circuit dynamics from the perspectives of recorded cortical population activity and membrane potential fluctuations, and from model investigations. Our results suggest that cortical circuits self-organize towards a balanced critical regime at which correlated variability is maintained at an intermediate level with advantages for neural computation.
Matthias Zimmermann, "Developing scientific applications with the Data Vortex network"

The Data Vortex (DV) network is used to communicate large amounts of data between different nodes of a parallel computer. We present benchmarks illustrating how DV excels at communicating random packets in situations where aggregation is difficult and for algorithms involving the fast Fourier transform. Next, we briefly describe a programming model for DV and draw comparisons with MPI. Finally, we summarize ongoing research projects: (i) inviscid incompressible fluid flow, (ii) the Schrödinger equation for few-body systems, and (iii) the simulation of ideal quantum computers capable of running general quantum circuits.

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