Daniëlle Schuman
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View article: From Classical Data to Quantum Advantage – Quantum Policy Evaluation on Quantum Hardware
From Classical Data to Quantum Advantage – Quantum Policy Evaluation on Quantum Hardware Open
Quantum policy evaluation (QPE) is a reinforcement learning (RL) algorithm which is quadratically more efficient than an analogous classical Monte Carlo estimation. It makes use of a direct quantum mechanical realization of a finite Markov…
View article: Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification
Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification Open
Exploiting the fact that samples drawn from a quantum annealer inherently follow a Boltzmann-like distribution, annealing-based Quantum Boltzmann Machines (QBMs) have gained increasing popularity in the quantum research community. While th…
View article: Reducing QUBO Density by Factoring Out Semi-Symmetries
Reducing QUBO Density by Factoring Out Semi-Symmetries Open
Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing are prominent approaches for solving combinatorial optimization problems, such as those formulated as Quadratic Unconstrained Binary Optimization (QUBO). These algorit…
View article: Reducing QAOA Circuit Depth by Factoring out Semi-Symmetries
Reducing QAOA Circuit Depth by Factoring out Semi-Symmetries Open
QAOA is a quantum algorithm for solving combinatorial optimization problems. It is capable of searching for the minimizing solution vector $x$ of a QUBO problem $x^TQx$. The number of two-qubit CNOT gates in the QAOA circuit scales linearl…
View article: Towards Less Greedy Quantum Coalition Structure Generation in Induced Subgraph Games
Towards Less Greedy Quantum Coalition Structure Generation in Induced Subgraph Games Open
The transition to 100% renewable energy requires new techniques for managing energy networks, such as dividing them into sensible subsets of prosumers called micro-grids. Doing so in an optimal manner is a difficult optimization problem, a…
View article: Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling Open
Quantum one-class support vector machines leverage the advantage of quantum kernel methods for semi-supervised anomaly detection. However, their quadratic time complexity with respect to data size poses challenges when dealing with large d…
View article: Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection
Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection Open
Anomaly detection in Endpoint Detection and Response (EDR) is a critical task in cybersecurity programs of large companies.With rapidly growing amounts of data and the omnipresence of zero-day attacks, manual and rule-based detection techn…
View article: Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
Benchmarking Quantum Surrogate Models on Scarce and Noisy Data Open
Surrogate models are ubiquitously used in industry and academia to efficiently approximate black box functions. As state-of-the-art methods from classical machine learning frequently struggle to solve this problem accurately for the often …
View article: Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements
Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements Open
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View article: Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines Open
Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural Networks with the p…
View article: Sampling problems on a Quantum Computer
Sampling problems on a Quantum Computer Open
Due to the advances in the manufacturing of quantum hardware in the recent years, significant research efforts have been directed towards employing quantum methods to solving problems in various areas of interest. Thus a plethora of novel …
View article: Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
Benchmarking Quantum Surrogate Models on Scarce and Noisy Data Open
Surrogate models are ubiquitously used in industry and academia to efficiently approximate given black box functions. As state-of-the-art methods from classical machine learning frequently struggle to solve this problem accurately for the …
View article: Solving Large Steiner Tree Problems in Graphs for Cost-efficient Fiber-To-The-Home Network Expansion
Solving Large Steiner Tree Problems in Graphs for Cost-efficient Fiber-To-The-Home Network Expansion Open
The expansion of Fiber-To-The-Home (FTTH) networks creates high costs due to expensive excavation procedures. Optimizing the planning process and minimizing the cost of the earth excavation work therefore lead to large savings. Mathematica…
View article: Solving Large Steiner Tree Problems in Graphs for Cost-Efficient Fiber-To-The-Home Network Expansion
Solving Large Steiner Tree Problems in Graphs for Cost-Efficient Fiber-To-The-Home Network Expansion Open
The expansion of Fiber-To-The-Home (FTTH) networks creates high costs due to expensive excavation procedures. Optimizing the planning process and minimizing the cost of the earth excavation work therefore lead to large savings. Mathematica…