arXiv subjects

Daniel Braun

Publications and source records attributed to Daniel Braun.

At least 55 records · Page 3Linked to original sources

arXiv2023

Super-Resolution Imaging with Multiparameter Quantum Metrology in Passive Remote Sensing

We study super-resolution imaging theoretically using a distant n-mode interferometer in the microwave regime for passive remote sensing, used e.g., for satellites like the "soil moisture and ocean salinity (SMOS)" mission to observe the surface of the Earth. We give a complete quantum mechanical analysis of multiparameter estimation of the temperatures on the source plane. We find the optimal detection modes by combining incoming modes with an optimized unitary that enables the most informative measurement based on photon counting in the detection modes and saturates the quantum Cramér-Rao bound from the symmetric logarithmic derivative for the parameter set of temperatures. In our numerical analysis, we achieved a quantum-enhanced super-resolution by reconstructing an image using the maximum likelihood estimator with a pixel size of 3 km, which is ten times smaller than the spatial resolution of SMOS with comparable parameters. Further, we find the optimized unitary for uniform temperature distribution on the source plane, with the temperatures corresponding to the average temperatures of the image. Even though the corresponding unitary was not optimized for the specific image, it still gives a super-resolution compared to local measurement scenarios for the theoretically possible maximum number of measurements.

quant-ph↗

arXiv2022

Color Coding of Large Value Ranges Applied to Meteorological Data

This paper presents a novel color scheme designed to address the challenge of visualizing data series with large value ranges, where scale transformation provides limited support. We focus on meteorological data, where the presence of large value ranges is common. We apply our approach to meteorological scatterplots, as one of the most common plots used in this domain area. Our approach leverages the numerical representation of mantissa and exponent of the values to guide the design of novel "nested" color schemes, able to emphasize differences between magnitudes. Our user study evaluates the new designs, the state of the art color scales and representative color schemes used in the analysis of meteorological data: ColorCrafter, Viridis, and Rainbow. We assess accuracy, time and confidence in the context of discrimination (comparison) and interpretation (reading) tasks. Our proposed color scheme significantly outperforms the others in interpretation tasks, while showing comparable performances in discrimination tasks.

eess.IV↗

arXiv2022

Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics

In this paper, we consider the task of retrieving documents with predefined topics from an unlabeled document dataset using an unsupervised approach. The proposed unsupervised approach requires only a small number of keywords describing the respective topics and no labeled document. Existing approaches either heavily relied on a large amount of additionally encoded world knowledge or on term-document frequencies. Contrariwise, we introduce a method that learns jointly embedded document and word vectors solely from the unlabeled document dataset in order to find documents that are semantically similar to the topics described by the keywords. The proposed method requires almost no text preprocessing but is simultaneously effective at retrieving relevant documents with high probability. When successively retrieving documents on different predefined topics from publicly available and commonly used datasets, we achieved an average area under the receiver operating characteristic curve value of 0.95 on one dataset and 0.92 on another. Further, our method can be used for multiclass document classification, without the need to assign labels to the dataset in advance. Compared with an unsupervised classification baseline, we increased F1 scores from 76.6 to 82.7 and from 61.0 to 75.1 on the respective datasets. For easy replication of our approach, we make the developed Lbl2Vec code publicly available as a ready-to-use tool under the 3-Clause BSD license.

cs.CL↗

arXiv2022

Principles of quantum functional testing

With increasing commercial availability of quantum information processing devices the need for testing them efficiently for their specified functionality will arise. Complete quantum channel characterization is out of the question for anything more than the simplest quantum channels for one or two qubits. Quantum functional testing leads to a decision problem with outcomes corresponding to rejection or acceptance of the claim of the producer that the device parameters are within certain specifications. In this context, we introduce and analyse three ingredients that can speed up this decision problem: iteration of the channel, efficient decision criteria, and non-greedy adaptive experimental design.

quant-ph↗

arXiv2022

Quantum metrology with ultracold chemical reactions

Chemical chain reactions are known to enable extremely sensitive detection schemes in chemical, biological, and medical analysis, and have even been used in the search for dark matter. Here we show that coherent, ultracold chemical reactions harbor great potential for quantum metrology: In an atom-molecule Bose-Einstein condensate (BEC), a weak external perturbation can modify the reaction dynamics and lead to the coherent creation of molecules in an atom-dominant regime which can be selectively detected with modern spectroscopic techniques. This promises to substantially improve the viability of previously proposed BEC-based sensors for acceleration, gravitational waves, and other physical quantities, including the detection of dark matter, that so far relied on the detection of the tiny density modulations caused by the creation of single phonons.

cond-mat.quant-gas↗

arXiv2022

Benchmarking the performance of portfolio optimization with QAOA

We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA). For a given list of assets, the portfolio optimization problem is formulated as quadratic binary optimization constrained on the number of assets contained in the portfolio. QAOA has been suggested as a possible candidate for solving this problem (and similar combinatorial optimization problems) more efficiently than classical computers in the case of a sufficiently large number of assets. However, the practical implementation of this algorithm requires a careful consideration of several technical issues, not all of which are discussed in the present literature. The present article intends to fill this gap and thereby provide the reader with a useful guide for applying QAOA to the portfolio optimization problem (and similar problems). In particular, we will discuss several possible choices of the variational form and of different classical algorithms for finding the corresponding optimized parameters. Viewing at the application of QAOA on error-prone NISQ hardware, we also analyze the influence of statistical sampling errors (due to a finite number of shots) and gate and readout errors (due to imperfect quantum hardware). Finally, we define a criterion for distinguishing between "easy" and "hard" instances of the portfolio optimization problem

quant-ph↗

arXiv2022

Perspectives of measuring gravitational effects of laser light and particle beams

We study possibilities of creation and detection of oscillating gravitational fields from lab-scale high energy, relativistic sources. The sources considered are high energy laser beams in an optical cavity and the ultra-relativistic proton bunches circulating in the beam of the Large Hadron Collider (LHC) at CERN. These sources allow for signal frequencies much higher and far narrower in bandwidth than what most celestial sources produce. In addition, by modulating the beams, one can adjust the source frequency over a very broad range, from Hz to GHz. The gravitational field of these sources and responses of a variety of detectors are analyzed. We optimize a mechanical oscillator such as a pendulum or torsion balance as detector and find parameter regimes such that -- combined with the planned high-luminosity upgrade of the LHC as a source -- a signal-to-noise ratio substantially larger than 1 should be achievable at least in principle, neglecting all sources of technical noise. This opens new perspectives of studying general relativistic effects and possibly quantum-gravitational effects with ultra-relativistic, well-controlled terrestrial sources.

gr-qc↗

arXiv2022

Construction and Elicitation of a Black Box Model in the Game of Bridge

We address the problem of building a decision model for a specific bidding situation in the game of Bridge. We propose the following multi-step methodology i) Build a set of examples for the decision problem and use simulations to associate a decision to each example ii) Use supervised relational learning to build an accurate and readable model iii) Perform a joint analysis between domain experts and data scientists to improve the learning language, including the production by experts of a handmade model iv) Build a better, more readable and accurate model.

cs.AI↗

arXiv2022

Quantifying Causal Influence in Quantum Mechanics

We extend Pearl's definition of causal influence to the quantum domain, where two quantum systems $A$, $B$ with finite-dimensional Hilbert space are embedded in a common environment $C$ and propagated with a joint unitary $U$. For finite dimensional Hilbert space of $C$, we find the necessary and sufficient condition on $U$ for a causal influence of $A$ on $B$ and vice versa. We introduce an easily computable measure of the causal influence and use it to study the causal influence of different quantum gates, its mutuality, and quantum superpositions of different causal orders. For two two-level atoms dipole-interacting with a thermal bath of electromagnetic waves, the space-time dependence of causal influence almost perfectly reproduces the one of reservoir-induced entanglement.

quant-ph↗

arXiv2022

N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks

Monocular depth estimation has been a popular area of research for several years, especially since self-supervised networks have shown increasingly good results in bridging the gap with supervised and stereo methods. However, these approaches focus their interest on dense 3D reconstruction and sometimes on tiny details that are superfluous for autonomous navigation. In this paper, we propose to address this issue by estimating the navigation map under a quadtree representation. The objective is to create an adaptive depth map prediction that only extract details that are essential for the obstacle avoidance. Other 3D space which leaves large room for navigation will be provided with approximate distance. Experiment on KITTI dataset shows that our method can significantly reduce the number of output information without major loss of accuracy.

cs.CV↗

arXiv2022

Influence of cosmological expansion in local experiments

Whether the cosmological expansion can influence the local dynamics, below the galaxy clusters scale, has been the subject of intense investigations in the past three decades. In this work, we consider McVittie and Kottler spacetimes, embedding a spherical object in a FLRW spacetime. We calculate the influence of the cosmological expansion on the frequency shift of a resonator and estimate its effect on the exchange of light signals between local observers. In passing, we also clarify some of the statements made in the literature.

gr-qc↗

arXiv2022

Quantum metrology with a non-linear kicked Mach-Zehnder interferometer

We study the sensitivity of a Mach-Zehnder interferometer that contains in addition to the phase shifter a non-linear element. By including both elements in a cavity or a loop that the light transverses many times, a non-linear kicked version of the interferometer arises. We study its sensitivity as function of the phase shift, the kicking strength, the maximally reached average number of photons, and damping due to photon loss for an initial coherent state. We find that for vanishing damping Heisenberg-limited scaling of the sensitivity arises if squeezing dominates the total photon number. For small to moderate damping rates the non-linear kicks can considerably increase the sensitivity as measured by the quantum Fisher information per unit time.

quant-ph↗

arXiv2022

Quantum-router: Storing and redirecting light at the photon level

We propose a method for spatially re-routing single photons or light in a coherent state with small average photon number by purely electronic means, i.e. without using mechanical devices such as micro-mirror arrays. The method is based on mapping the quantum state of the incoming light onto a spin-wave in an atomic ensemble as is done in quantum memories of light. Then the wavevector of the spin-wave is modified in a controlled way by an applied magnetic field gradient. Finally, by re-applying the same control beam as for storing, the signal pulse is released in a new direction that depends on the deflected wavevector of the spin-wave. We show by numerical simulation that efficiencies can be achieved for arbitrary deflection angles in the plane that are comparable with simple photon storage and re-emission in forward direction, and propose a new method for eliminating the stored momentum as source of decoherence in the quantum memory. In a reasonable parameter regime, the re-routing should be achievable on a time-scale on the order of few to $\sim100$ microseconds, depending on the deflection angle. The shifts in the wavevector that can be achieved using the Zeeman-effect, with otherwise minimal changes to the spin-wave, can also be used to complement existing ac-Stark spin-wave manipulation methods.

quant-ph↗

arXiv2021

Quantum-enhanced passive remote sensing

We investigate theoretically the ultimate resolution that can be achieved with passive remote sensing in the microwave regime used e.g.~on board of satellites observing Earth, such as the Soil Moisture and Ocean Salinity (SMOS) mission. We give a fully quantum mechanical analysis of the problem, starting from thermal distributions of microscopic currents on the surface to be imaged that lead to a mixture of coherent states of the electromagnetic field which are then measured with an array of receivers. We derive the optimal detection modes and measurement schemes that allow one to saturate the quantum Cramér-Rao bound for the chosen parameters that determine the distribution of the microscopic currents. For parameters comparable to those of SMOS, a quantum enhancement of the spatial resolution by more than a factor of 20 should be possible with a single measurement and a single detector, and a resolution down to the order of 1 meter and less than a 1/10 Kelvin for the theoretically possible maximum number of measurements.

quant-ph↗

arXiv2021

Stochastic emulation of quantum algorithms

Quantum algorithms profit from the interference of quantum states in an exponentially large Hilbert space and the fact that unitary transformations on that Hilbert space can be broken down to universal gates that act only on one or two qubits at the same time. The former aspect renders the direct classical simulation of quantum algorithms difficult. Here we introduce higher-order partial derivatives of a probability distribution of particle positions as a new object that shares these basic properties of quantum mechanical states needed for a quantum algorithm. Discretization of the positions allows one to represent the quantum mechanical state of $n_\text{bit}$ qubits by $2(n_\text{bit}+1)$ classical stochastic bits. Based on this, we demonstrate many-particle interference and representation of pure entangled quantum states via derivatives of probability distributions and find the universal set of stochastic maps that correspond to the quantum gates in a universal gate set. We prove that the propagation via the stochastic map built from those universal stochastic maps reproduces up to a prefactor exactly the evolution of the quantum mechanical state with the corresponding quantum algorithm, leading to an automated translation of a quantum algorithm to a stochastic classical algorithm. We implement several well-known quantum algorithms, analyse the scaling of the needed number of realizations with the number of qubits, and highlight the role of destructive interference for the cost of the emulation. Foundational questions raised by the new representation of a quantum state are discussed.

quant-ph↗

arXiv2021

Perfect quantum-state synchronization

We investigate the most general mechanisms that lead to perfect synchronization of the quantum states of all subsystems of an open quantum system starting from an arbitrary initial state. We provide a necessary and sufficient condition for such "quantum-state synchronization", prove tight lower bounds on the dimension of the environment's Hilbert space in two main classes of quantum-state synchronizers, and give an analytical solution for their construction. The functioning of the found quantum-state synchronizer of two qubits is demonstrated experimentally on an IBM quantum computer and we show that the remaining asynchronicity is a sensitive measure of the quantum computer's imperfection.

quant-ph↗

arXiv2021

Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation

Quantum metrology promises unprecedented measurement precision but suffers in practice from the limited availability of resources such as the number of probes, their coherence time, or non-classical quantum states. The adaptive Bayesian approach to parameter estimation allows for an efficient use of resources thanks to adaptive experiment design. For its practical success fast numerical solutions for the Bayesian update and the adaptive experiment design are crucial. Here we show that neural networks can be trained to become fast and strong experiment-design heuristics using a combination of an evolutionary strategy and reinforcement learning. Neural-network heuristics are shown to outperform established heuristics for the technologically important example of frequency estimation of a qubit that suffers from dephasing. Our method of creating neural-network heuristics is very general and complements the well-studied sequential Monte-Carlo method for Bayesian updates to form a complete framework for adaptive Bayesian quantum estimation.

quant-ph↗

arXiv2021

Optimal estimation of time-dependent gravitational fields with quantum optomechanical systems

We study the fundamental sensitivity that can be achieved with an ideal optomechanical system in the nonlinear regime for measurements of time-dependent gravitational fields. Using recently developed methods to solve the dynamics of a nonlinear optomechanical system with a time-dependent Hamiltonian, we compute the quantum Fisher information for linear displacements of the mechanical element due to gravity. We demonstrate that the sensitivity can not only be further enhanced by injecting squeezed states of the cavity field, but also by modulating the light--matter coupling of the optomechanical system. We specifically apply our results to the measurement of gravitational fields from small oscillating masses, where we show that, in principle, the gravitational field of an oscillating nano-gram mass can be detected based on experimental parameters that will likely be accessible in the near-term future. Finally, we identify the experimental parameter regime necessary for gravitational wave detection with a quantum optomechanical sensor.

quant-ph↗