arXiv subjects

Jeanette Miriam Lorenz

Publications and source records attributed to Jeanette Miriam Lorenz.

At least 37 records · Page 2Linked to original sources

arXiv2024

Identifying Bottlenecks of NISQ-friendly HHL algorithms

Quantum computing promises enabling solving large problem instances, e.g. large linear equation systems with HHL algorithm, once the hardware stack matures. For the foreseeable future quantum computing will remain in the so-called NISQ era, in which the algorithms need to account for the flaws of the hardware such as noise. In this work, we perform an empirical study to test scaling properties and directly related noise resilience of the the most resources-intense component of the HHL algorithm, namely QPE and its NISQ-adaptation Iterative QPE. We explore the effectiveness of noise mitigation techniques for these algorithms and investigate whether we can keep the gate number low by enforcing sparsity constraints on the input or using circuit optimization techniques provided by Qiskit package. Our results indicate that currently available noise mitigation techniques, such as Qiskit readout and Mthree readout packages, are insufficient for enabling results recovery even in the small instances tested here. Moreover, our results indicate that the scaling of these algorithms with increase in precision seems to be the most substantial obstacle. These insights allowed us to deduce an approximate bottleneck for algorithms that consider a similar time evolution as QPE. Such observations provide evidence of weaknesses of such algorithms on NISQ devices and help us formulate meaningful future research directions.

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arXiv2024

Discrete Randomized Smoothing Meets Quantum Computing

Breakthroughs in machine learning (ML) and advances in quantum computing (QC) drive the interdisciplinary field of quantum machine learning to new levels. However, due to the susceptibility of ML models to adversarial attacks, practical use raises safety-critical concerns. Existing Randomized Smoothing (RS) certification methods for classical machine learning models are computationally intensive. In this paper, we propose the combination of QC and the concept of discrete randomized smoothing to speed up the stochastic certification of ML models for discrete data. We show how to encode all the perturbations of the input binary data in superposition and use Quantum Amplitude Estimation (QAE) to obtain a quadratic reduction in the number of calls to the model that are required compared to traditional randomized smoothing techniques. In addition, we propose a new binary threat model to allow for an extensive evaluation of our approach on images, graphs, and text.

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arXiv2024

Predominant Aspects on Security for Quantum Machine Learning: Literature Review

Quantum Machine Learning (QML) has emerged as a promising intersection of quantum computing and classical machine learning, anticipated to drive breakthroughs in computational tasks. This paper discusses the question which security concerns and strengths are connected to QML by means of a systematic literature review. We categorize and review the security of QML models, their vulnerabilities inherent to quantum architectures, and the mitigation strategies proposed. The survey reveals that while QML possesses unique strengths, it also introduces novel attack vectors not seen in classical systems. We point out specific risks, such as cross-talk in superconducting systems and forced repeated shuttle operations in ion-trap systems, which threaten QML's reliability. However, approaches like adversarial training, quantum noise exploitation, and quantum differential privacy have shown potential in enhancing QML robustness. Our review discuss the need for continued and rigorous research to ensure the secure deployment of QML in real-world applications. This work serves as a foundational reference for researchers and practitioners aiming to navigate the security aspects of QML.

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arXiv2024

Efficient Encodings of the Travelling Salesperson Problem for Variational Quantum Algorithms

Routing problems are a common optimization problem in industrial applications, which occur on a large scale in supply chain planning. Due to classical limitations for solving NP-hard problems, quantum computing hopes to improve upon speed or solution quality. Several suggestions have been made for encodings of routing problems to solve them with variational quantum algorithms. However, for an end user it is hard to decide a priori which encoding will give the best solutions according to their needs. In this work, we investigate different encodings for the Travelling Salesperson Problem. We compare their scaling and performance when using the Quantum Approximate Optimization Algorithm and the Variational Quantum Eigensolver and provide a clear guide for users when to choose which encoding. For small instances, we find evidence that the permutation encoding can yield good results since it does not suffer from feasibility issues.

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arXiv2024

Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis

As quantum machine learning continues to develop at a rapid pace, the importance of ensuring the robustness and efficiency of quantum algorithms cannot be overstated. Our research presents an analysis of quantum randomized smoothing, how data encoding and perturbation modeling approaches can be matched to achieve meaningful robustness certificates. By utilizing an innovative approach integrating Grover's algorithm, a quadratic sampling advantage over classical randomized smoothing is achieved. This strategy necessitates a basis state encoding, thus restricting the space of meaningful perturbations. We show how constrained $k$-distant Hamming weight perturbations are a suitable noise distribution here, and elucidate how they can be constructed on a quantum computer. The efficacy of the proposed framework is demonstrated on a time series classification task employing a Bag-of-Words pre-processing solution. The advantage of quadratic sample reduction is recovered especially in the regime with large number of samples. This may allow quantum computers to efficiently scale randomized smoothing to more complex tasks beyond the reach of classical methods.

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arXiv2024

Building Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset

In this work, we are introducing a Quantum-Classical Bayesian Neural Network (QCBNN) that is capable to perform uncertainty-aware classification of classical medical dataset. This model is a symbiosis of a classical Convolutional NN that performs ultra-sound image processing and a quantum circuit that generates its stochastic weights, within a Bayesian learning framework. To test the utility of this idea for the possible future deployment in the medical sector we track multiple behavioral metrics that capture both predictive performance as well as model's uncertainty. It is our ambition to create a hybrid model that is capable to classify samples in a more uncertainty aware fashion, which will advance the trustworthiness of these models and thus bring us step closer to utilizing them in the industry. We test multiple setups for quantum circuit for this task, and our best architectures display bigger uncertainty gap between correctly and incorrectly identified samples than its classical benchmark at an expense of a slight drop in predictive performance. The innovation of this paper is two-fold: (1) combining of different approaches that allow the stochastic weights from the quantum circuit to be continues thus allowing the model to classify application-driven dataset; (2) studying architectural features of quantum circuit that make-or-break these models, which pave the way into further investigation of more informed architectural designs.

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arXiv2024

Hybrid Meta-Solving for Practical Quantum Computing

The advent of quantum algorithms has initiated a discourse on the potential for quantum speedups for optimization problems. However, several factors still hinder a practical realization of the potential benefits. These include the lack of advanced, error-free quantum hardware, the absence of accessible software stacks for seamless integration and interaction, and the lack of methods that allow us to leverage the theoretical advantages to real-world use cases. This paper works towards the creation of an accessible hybrid software stack for solving optimization problems, aiming to create a fundamental platform that can utilize quantum technologies to enhance the solving process. We introduce a novel approach that we call Hybrid Meta-Solving, which combines classical and quantum optimization techniques to create customizable and extensible hybrid solvers. We decompose mathematical problems into multiple sub-problems that can be solved by classical or quantum solvers, and propose techniques to semi-automatically build the best solver for a given problem. Implemented in our ProvideQ toolbox prototype, Meta-Solving provides interactive workflows for accessing quantum computing capabilities. Our evaluation demonstrates the applicability of Meta-Solving in industrial use cases. It shows that we can reuse state-of-the-art classical algorithms and extend them with quantum computing techniques. Our approach is designed to be at least as efficient as state-of-the-art classical techniques, while having the potential to outperform them if future advances in the quantum domain are made.

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arXiv2024

Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization

Advancements in Quantum Computing (QC) and Neural Combinatorial Optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variational Quantum Algorithms such as QAOA can be used to solve a wide range of combinatorial optimization problems. On the other hand, the same class of problems can be solved by NCO, a method that has shown promising results, particularly since the introduction of Graph Neural Networks. Given recent advances in both research areas, we introduce Hamiltonian-based Quantum Reinforcement Learning (QRL), an approach at the intersection of QC and NCO. We model our ansatzes directly on the combinatorial optimization problem's Hamiltonian formulation, which allows us to apply our approach to a broad class of problems. Our ansatzes show favourable trainability properties when compared to the hardware efficient ansatzes, while also not being limited to graph-based problems, unlike previous works. In this work, we evaluate the performance of Hamiltonian-based QRL on a diverse set of combinatorial optimization problems to demonstrate the broad applicability of our approach and compare it to QAOA.

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arXiv2024

Understanding the effects of data encoding on quantum-classical convolutional neural networks

Quantum machine learning was recently applied to various applications and leads to results that are comparable or, in certain instances, superior to classical methods, in particular when few training data is available. These results warrant a more in-depth examination of when and why improvements can be observed. A key component of quantum-enhanced methods is the data encoding strategy used to embed the classical data into quantum states. However, a clear consensus on the selection of a fitting encoding strategy given a specific use-case has not yet been reached. This work investigates how the data encoding impacts the performance of a quantum-classical convolutional neural network (QCCNN) on two medical imaging datasets. In the pursuit of understanding why one encoding method outperforms another, two directions are explored. Potential correlations between the performance of the quantum-classical architecture and various quantum metrics are first examined. Next, the Fourier series decomposition of the quantum circuits is analyzed, as variational quantum circuits generate Fourier-type sums. We find that while quantum metrics offer limited insights into this problem, the Fourier coefficients analysis appears to provide better clues to understand the effects of data encoding on QCCNNs.

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arXiv2024

Quantum Multi-Agent Reinforcement Learning for Aerial Ad-hoc Networks

Quantum machine learning (QML) as combination of quantum computing with machine learning (ML) is a promising direction to explore, in particular due to the advances in realizing quantum computers and the hoped-for quantum advantage. A field within QML that is only little approached is quantum multi-agent reinforcement learning (QMARL), despite having shown to be potentially attractive for addressing industrial applications such as factory management, cellular access and mobility cooperation. This paper presents an aerial communication use case and introduces a hybrid quantum-classical (HQC) ML algorithm to solve it. This use case intends to increase the connectivity of flying ad-hoc networks and is solved by an HQC multi-agent proximal policy optimization algorithm in which the core of the centralized critic is replaced with a data reuploading variational quantum circuit. Results show a slight increase in performance for the quantum-enhanced solution with respect to a comparable classical algorithm, earlier reaching convergence, as well as the scalability of such a solution: an increase in the size of the ansatz, and thus also in the number of trainable parameters, leading to better outcomes. These promising results show the potential of QMARL to industrially-relevant complex use cases.

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arXiv2024

Quantum Patch-Based Autoencoder for Anomaly Segmentation

Quantum Machine Learning investigates the possibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to identify irregularities at sample level and can be addressed with both supervised and unsupervised methods. Autoencoders are commonly used in unsupervised tasks, where models are trained to reconstruct normal instances efficiently, allowing anomaly identification through high reconstruction errors. While quantum autoencoders have been proposed in the literature, their application to anomaly segmentation tasks remains unexplored. In this paper, we introduce a patch-based quantum autoencoder (QPB-AE) for image anomaly segmentation, with a number of parameters scaling logarithmically with patch size. QPB-AE reconstructs the quantum state of the embedded input patches, computing an anomaly map directly from measurement through a SWAP test without reconstructing the input image. We evaluate its performance across multiple datasets and parameter configurations and compare it against a classical counterpart.

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arXiv2023

Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses

Leveraging the unique properties of quantum mechanics, Quantum Machine Learning (QML) promises computational breakthroughs and enriched perspectives where traditional systems reach their boundaries. However, similarly to classical machine learning, QML is not immune to adversarial attacks. Quantum adversarial machine learning has become instrumental in highlighting the weak points of QML models when faced with adversarial crafted feature vectors. Diving deep into this domain, our exploration shines light on the interplay between depolarization noise and adversarial robustness. While previous results enhanced robustness from adversarial threats through depolarization noise, our findings paint a different picture. Interestingly, adding depolarization noise discontinued the effect of providing further robustness for a multi-class classification scenario. Consolidating our findings, we conducted experiments with a multi-class classifier adversarially trained on gate-based quantum simulators, further elucidating this unexpected behavior.

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arXiv2023

Variational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments

Quantum Reinforcement Learning (QRL) emerged as a branch of reinforcement learning (RL) that uses quantum submodules in the architecture of the algorithm. One branch of QRL focuses on the replacement of neural networks (NN) by variational quantum circuits (VQC) as function approximators. Initial works have shown promising results on classical environments with discrete action spaces, but many of the proposed architectural design choices of the VQC lack a detailed investigation. Hence, in this work we investigate the impact of VQC design choices such as angle embedding, encoding block architecture and postprocessesing on the training capabilities of QRL agents. We show that VQC design greatly influences training performance and heuristically derive enhancements for the analyzed components. Additionally, we show how to design a QRL agent in order to solve classical environments with continuous action spaces and benchmark our agents against classical feed-forward NNs.

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arXiv2023

Efficient learning of Sparse Pauli Lindblad models for fully connected qubit topology

The challenge to achieve practical quantum computing considering current hardware size and gate fidelity is the sensitivity to errors and noise. Recent work has shown that by learning the underlying noise model capturing qubit cross-talk, error mitigation can push the boundary of practical quantum computing. This has been accomplished using Sparse Pauli-Lindblad models only on devices with a linear topology connectivity (i.e. superconducting qubit devices). In this work we extend the theoretical requirement for learning such noise models on hardware with full connectivity (i.e. ion trap devices).

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arXiv2023

Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness

Emerging quantum computing technologies, such as Noisy Intermediate-Scale Quantum (NISQ) devices, offer potential advancements in solving mathematical optimization problems. However, limitations in qubit availability, noise, and errors pose challenges for practical implementation. In this study, we examine two decomposition methods for Mixed-Integer Linear Programming (MILP) designed to reduce the original problem size and utilize available NISQ devices more efficiently. We concentrate on breaking down the original problem into smaller subproblems, which are then solved iteratively using a combined quantum-classical hardware approach. We conduct a detailed analysis for the decomposition of MILP with Benders and Dantzig-Wolfe methods. In our analysis, we show that the number of qubits required to solve Benders is exponentially large in the worst-case, while remains constant for Dantzig-Wolfe. Additionally, we leverage Dantzig-Wolfe decomposition on the use-case of certifying the robustness of ReLU networks. Our experimental results demonstrate that this approach can save up to 90\% of qubits compared to existing methods on quantum annealing and gate-based quantum computers.

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arXiv2023

Recommending Solution Paths for Solving Optimization Problems with Quantum Computing

Solving real-world optimization problems with quantum computing requires choosing between a large number of options concerning formulation, encoding, algorithm and hardware. Finding good solution paths is challenging for end users and researchers alike. We propose a framework designed to identify and recommend the best-suited solution paths. This introduces a novel abstraction layer that is required to make quantum-computing-assisted solution techniques accessible to end users without requiring a deeper knowledge of quantum technologies. State-of-the-art hybrid algorithms, encoding and decomposition techniques can be integrated in a modular manner and evaluated using problem-specific performance metrics. Equally, tools for the graphical analysis of variational quantum algorithms are developed. Classical, fault tolerant quantum and quantum-inspired methods can be included as well to ensure a fair comparison resulting in useful solution paths. We demonstrate and validate our approach on a selected set of options and illustrate its application on the capacitated vehicle routing problem (CVRP). We also identify crucial requirements and the major design challenges for the proposed automation layer within a quantum-assisted solution workflow for optimization problems.

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arXiv2023

Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection

As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated. A key concern in this regard is the ability to identify whether a given sample is from the training distribution, or is an "Out-Of-Distribution" (OOD) sample. In addition, adversaries can manipulate OOD samples in ways that lead a classifier to make a confident prediction. In this study, we present a novel approach for certifying the robustness of OOD detection within a $\ell_2$-norm around the input, regardless of network architecture and without the need for specific components or additional training. Further, we improve current techniques for detecting adversarial attacks on OOD samples, while providing high levels of certified and adversarial robustness on in-distribution samples. The average of all OOD detection metrics on CIFAR10/100 shows an increase of $\sim 13 \% / 5\%$ relative to previous approaches.

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arXiv2023

Benchmarking the Variational Quantum Eigensolver using different quantum hardware

The Variational Quantum Eigensolver (VQE) is a promising quantum algorithm for applications in chemistry within the Noisy Intermediate-Scale Quantum (NISQ) era. The ability for a quantum computer to simulate electronic structures with high accuracy would have a profound impact on material and biochemical science with potential applications e.g., to the development of new drugs. However, considering the variety of quantum hardware architectures, it is still uncertain which hardware concept is most suited to execute the VQE for e.g., the simulation of molecules. Aspects to consider here are the required connectivity of the quantum circuit used, the size and the depth and thus the susceptibility to noise effects. Besides theoretical considerations, empirical studies using available quantum hardware may help to clarify the question of which hardware technology might be better suited for a certain given application and algorithm. Going one step into this direction, within this work, we present results using the VQE for the simulation of the hydrogen molecule, comparing superconducting and ion trap quantum computers. The experiments are carried out with a standardized setup of ansatz and optimizer, selected to reduce the amount of iterations required. The findings are analyzed considering different quantum processor types, calibration data as well as the depth and gate counts of the circuits required for the different hardware concepts after transpilation.

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