A5002277966- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Quantum Mechanics and Applications
- Nonlinear Photonic Systems
- Quantum and electron transport phenomena
- Quantum-Dot Cellular Automata
- Low-power high-performance VLSI design
- Parallel Computing and Optimization Techniques
- Advanced Fiber Laser Technologies
- Neural Networks and Reservoir Computing
- Nonlinear Dynamics and Pattern Formation
- Numerical methods for differential equations
- Advanced Mathematical Physics Problems
- Computability, Logic, AI Algorithms
- Stochastic Gradient Optimization Techniques
- Blind Source Separation Techniques
- Photonic Crystals and Applications
- Nonlinear Waves and Solitons
- Advancements in Semiconductor Devices and Circuit Design
University of Stuttgart
2007-2011
PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides unified architecture near-term computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature the ability to compute gradients variational circuits in way that compatible with classical techniques such as backpropagation. thus extends automatic differentiation algorithms common optimization machine learning include hybrid computations. A...
Abstract Kernel methods have a wide spectrum of applications in machine learning. Recently, link between quantum computing and kernel theory has been formally established, opening up opportunities for techniques to enhance various existing machine-learning methods. We present distance-based classifier whose is based on the state fidelity training test data. The can be tailored systematically with circuit raise an arbitrary power assign weights each Given specific input state, our protocol...
Ubiquitous in quantum computing is the step to encode data into a state. This process called state preparation, and its complexity for non-structured exponential on number of qubits. Several works address this problem, instance, by using variational methods that train fixed depth circuit with manageable complexity. These have their limitations, as lack back-propagation technique barren plateaus. work proposes an algorithm reduce preparation offloading computational classical computer. The...
Abstract Discrete stochastic processes (DSP) are instrumental for modeling the dynamics of probabilistic systems and have a wide spectrum applications in science engineering. DSPs usually analyzed via Monte-Carlo methods since number realizations increases exponentially with time steps, importance sampling is often required to reduce variance. We propose quantum algorithm calculating characteristic function DSP, which completely defines its probability distribution, using circuit elements...
Abstract Quantum computing opens exciting opportunities for kernel-based machine learning methods, which have broad applications in data analysis. Recent works show that quantum computers can efficiently construct a model of classifier by engineering the interference effect to carry out kernel evaluation parallel. For practical these an important issue is minimize size circuits. We present simplest circuit constructing binary classifier. This achieved generalizing encode labels relative...
Abstract Quantum circuit algorithms often require architectural design choices analogous to those made in constructing neural and tensor networks. These tend be hierarchical, modular exhibit repeating patterns. Neural Architecture Search (NAS) attempts automate network through learning architecture achieves state-of-the-art performance. We propose a framework for representing quantum architectures using techniques from NAS, which enables search space search. use this justify the importance...
In this paper we investigate the connection between quantum information theory and machine learning. particular, show how state discrimination can represent a useful tool to address standard classification problem in Previous studies have shown that optimal measurement developed context of communication inspire new binary algorithm achieve higher inference accuracy for various datasets. Here propose model arbitrary multiclass inspired by discrimination, which is enabled encoding data space...
Standing modulating pulse solutions consist of a standing pulse-like envelope an underlying spatially and temporarily oscillating carrier wave.Using spatial dynamics, invariant manifold theory normal form for periodic systems we construct such on large domains in time space nonlinear wave equation with coefficients.Such play important role theoretical scenarios where photonic crystals are used as optical storage.7 Estimates the center-stable 22 8 The reversible reflection 25 A An example B...
To witness quantum advantages in practical settings, substantial efforts are required not only at the hardware level but also on theoretical research to reduce computational cost of a given protocol. Quantum computation has potential significantly enhance existing classical machine learning methods, and several algorithms for binary classification based kernel method have been proposed. These rely estimating an expectation value, which turn requires expensive data encoding procedure be...
The Helstrom measurement (HM) is known to be the optimal strategy for distinguishing non-orthogonal quantum states with minimum error. Previously, a binary classifier based on classical simulation of HM has been proposed. It was observed that using multiple copies sample data reduced classification Nevertheless, exponential growth in runtime hindered comprehensive investigation relationship between number and performance. We present an efficient method arbitrary by utilizing state fidelity....
Quadratic Unconstrained Binary Optimization (QUBO) sits at the heart of many industries and academic fields such as logistics, supply chain, finance, pharmaceutical science, chemistry, IT, energy sectors, among others. These problems typically involve optimizing a large number binary variables, which makes finding exact solutions exponentially more difficult. Consequently, most QUBO are classified NP-hard. To address this challenge, we developed powerful feedforward neural network (FNN)...
Kernel methods have a wide spectrum of applications in machine learning. Recently, link between quantum computing and kernel theory has been formally established, opening up opportunities for techniques to enhance various existing learning methods. We present distance-based classifier whose is based on the state fidelity training test data. The can be tailored systematically with circuit raise an arbitrary power assign weights each Given specific input state, our protocol calculates weighted...
Discrete stochastic processes (DSP) are instrumental for modelling the dynamics of probabilistic systems and have a wide spectrum applications in science engineering. DSPs usually analyzed via Monte Carlo methods since number realizations increases exponentially with time steps, importance sampling is often required to reduce variance. We propose quantum algorithm calculating characteristic function DSP, which completely defines its probability distribution, using circuit elements that grows...
Ubiquitous in quantum computing is the step to encode data into a state. This process called state preparation, and its complexity for non-structured exponential on number of qubits. Several works address this problem, instance, by using variational methods that train fixed depth circuit with manageable complexity. These have their limitations, as lack back-propagation technique barren plateaus. work proposes an algorithm reduce preparation offloading computational classical computer. The...
Motiviert durch die Existenz sog. Breather-Losungen in der Sine-Gordon Gleichung betrachten wir kubisch-nicht-nichtlineare Klein-Gordon und setzen uns zum Ziel raumlich periodische Koeffizienten zu finden, mit denen Breather-Losungen, lokalisierte, zeitlich periodiche Losungen nachgewiesen werden konnen. Dazu benutzen Floquettheorie, Invariante Mannigfaltigkeitstheorie Storungstheorie fur DGLn um einer nachzuweisen.