arXiv subjects

Robert Wille

Publications and source records attributed to Robert Wille.

At least 109 records · Page 6Linked to original sources

arXiv2024

Multi-controlled Phase Gate Synthesis with ZX-calculus applied to Neutral Atom Hardware

Quantum circuit synthesis describes the process of converting arbitrary unitary operations into a gate sequence of a fixed universal gate set, usually defined by the operations native to a given hardware platform. Most current synthesis algorithms are designed to synthesize towards a set of single qubit rotations and an additional entangling two qubit gate, such as CX, CZ, or the Molmer Sorensen gate. However, with the emergence of neutral atom based hardware and their native support for gates with more than two qubits, synthesis approaches tailored to these new gate sets become necessary. In this work, we present an approach to synthesize multi controlled phase gates using ZX calculus. By representing quantum circuits as graph like ZX diagrams, one can utilize the distinct graph structure of diagonal gates to identify multi controlled phase gates inherently present in some quantum circuits even if none were explicitly defined in the original circuit. We evaluate the approach on a wide range of benchmark circuits and compare them to the standard Qiskit synthesis regarding its circuit execution time for neutral atom based hardware with native support of multi controlled gates. Our results show possible advantages for current state of the art hardware and represent the first exact synthesis algorithm supporting arbitrary sized multi controlled phase gates.

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arXiv2024

A Predictive Approach for Selecting the Best Quantum Solver for an Optimization Problem

Leveraging quantum computers for optimization problems holds promise across various application domains. Nevertheless, utilizing respective quantum computing solvers requires describing the optimization problem according to the Quadratic Unconstrained Binary Optimization (QUBO) formalism and selecting a proper solver for the application of interest with a reasonable setting. Both demand significant proficiency in quantum computing, QUBO formulation, and quantum solvers, a background that usually cannot be assumed by end users who are domain experts rather than quantum computing specialists. While tools aid in QUBO formulations, support for selecting the best-solving approach remains absent. This becomes even more challenging because selecting the best solver for a problem heavily depends on the problem itself. In this work, we are accepting this challenge and propose a predictive selection approach, which aids end users in this task. To this end, the solver selection task is first formulated as a classification task that is suitable to be solved by supervised machine learning. Based on that, we then propose strategies for adjusting solver parameters based on problem size and characteristics. Experimental evaluations, considering more than 500 different QUBO problems, confirm the benefits of the proposed solution. In fact, we show that in more than 70% of the cases, the best solver is selected, and in about 90% of the problems, a solver in the top two, i.e., the best or its closest suboptimum, is selected. This exploration proves the potential of machine learning in quantum solver selection and lays the foundations for its automation, broadening access to quantum optimization for a wider range of users.

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arXiv2024

MUFASA: Multi-View Fusion and Adaptation Network with Spatial Awareness for Radar Object Detection

In recent years, approaches based on radar object detection have made significant progress in autonomous driving systems due to their robustness under adverse weather compared to LiDAR. However, the sparsity of radar point clouds poses challenges in achieving precise object detection, highlighting the importance of effective and comprehensive feature extraction technologies. To address this challenge, this paper introduces a comprehensive feature extraction method for radar point clouds. This study first enhances the capability of detection networks by using a plug-and-play module, GeoSPA. It leverages the Lalonde features to explore local geometric patterns. Additionally, a distributed multi-view attention mechanism, DEMVA, is designed to integrate the shared information across the entire dataset with the global information of each individual frame. By employing the two modules, we present our method, MUFASA, which enhances object detection performance through improved feature extraction. The approach is evaluated on the VoD and TJ4DRaDSet datasets to demonstrate its effectiveness. In particular, we achieve state-of-the-art results among radar-based methods on the VoD dataset with the mAP of 50.24%.

cs.CV↗

arXiv2024

An Abstract Model and Efficient Routing for Logical Entangling Gates on Zoned Neutral Atom Architectures

Recent experimental achievements have demonstrated the potential of neutral atom architectures for fault-tolerant quantum computing. These architectures feature the dynamic rearrangement of atoms during computation, enabling nearly arbitrary two-dimensional rearrangements. Additionally, they employ a zoned layout with dedicated regions for entangling, storage, and readout. This architecture requires design automation software that efficiently compiles quantum circuits to this hardware and takes care that atoms are in the right place at the right time. In this paper, we initiate this line of work by providing, (1) an abstract model of the novel architecture and, (2) an efficient solution to the routing problem of entangling gates. By this, we aim to maximize the parallelism of entangling gates and minimize the overhead caused by the routing of atoms between zones. In addition to that, we keep the realm of fault-tolerant quantum computing in mind and consider logical qubit arrays, each of which encodes one logical qubit. We implemented the proposed idea as a tool called NALAC and demonstrated its effectiveness and efficiency by showing that it can significantly reduce the routing overhead of logical entangling gates compared to the naive approach. As part of the Munich Quantum Toolkit (MQT), NALAC is publicly available as open-source at https://github.com/cda-tum/mqt-qmap.

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arXiv2024

Comparing Lazy Constraint Selection Strategies in Train Routing with Moving Block Control

Railroad transportation plays a vital role in the future of sustainable mobility. Besides building new infrastructure, capacity can be improved by modern train control systems, e.g., based on moving blocks. At the same time, there is only limited work on how to optimally route trains using the potential gained by these systems. Recently, an initial approach for train routing with moving block control has been proposed to address this demand. However, detailed evaluations on so-called lazy constraints are missing, and no publicly available implementation exists. In this work, we close this gap by providing an extended approach as well as a flexible open-source implementation that can use different solving strategies. Using that, we experimentally evaluate what choices should be made when implementing a lazy constraint approach. The corresponding implementation and benchmarks are publicly available as part of the Munich Train Control Toolkit (MTCT) at https://github.com/cda-tum/mtct.

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arXiv2024

Towards an Automatic Framework for Solving Optimization Problems with Quantum Computers

Optimizing objective functions stands to benefit significantly from leveraging quantum computers, promising enhanced solution quality across various application domains in the future. However, harnessing the potential of quantum solvers necessitates formulating problems according to the Quadratic Unconstrained Binary Optimization (QUBO) model, demanding significant expertise in quantum computation and QUBO formulations. This expertise barrier limits access to quantum solutions. Fortunately, automating the conversion of conventional optimization problems into QUBO formulations presents a solution for promoting accessibility to quantum solvers. This article addresses the unmet need for a comprehensive automatic framework to assist users in utilizing quantum solvers for optimization tasks while preserving interfaces that closely resemble conventional optimization practices. The framework prompts users to specify variables, optimization criteria, as well as validity constraints and, afterwards, allows them to choose the desired solver. Subsequently, it automatically transforms the problem description into a format compatible with the chosen solver and provides the resulting solution. Additionally, the framework offers instruments for analyzing solution validity and quality. Comparative analysis against existing libraries and tools in the literature highlights the comprehensive nature of the proposed framework. Two use cases (the knapsack problem and linear regression) are considered to show the completeness and efficiency of the framework in real-world applications. Finally, the proposed framework represents a significant advancement towards automating quantum computing solutions and widening access to quantum optimization for a broader range of users.

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arXiv2024

Stripping Quantum Decision Diagrams of their Identity

Classical representations of quantum states and operations as vectors and matrices are plagued by an exponential growth in memory and runtime requirements for increasing system sizes. Based on their use in classical computing, an alternative data structure known as Decision Diagrams (DDs) has been proposed, which, in many cases, provides both a more compact representation and more efficient computation. In the classical realm, decades of research have been conducted on DDs and numerous variations tailored for specific applications exist. However, DDs for quantum computing are just in their infancy and there is still room for tailoring them to this new technology. In particular, existing representations of DDs require extending all operations in a quantum circuit to the full system size through extension by nodes representing identity matrices. In this work, we make an important step forward for quantum DDs by stripping these identity structures from quantum operations. This significantly reduces the number of nodes required to represent them as well as eases the pressure on key building blocks of their implementation. As a result, we obtain a structure that is more natural for quantum computing and significantly speeds up with computations-with a runtime improvement of up to 70x compared to the state-of-the-art.

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arXiv2024

Towards Application-Aware Quantum Circuit Compilation

Quantum computing has made tremendous improvements in both software and hardware that have sparked interest in academia and industry to realize quantum computing applications. To this end, several steps are necessary: The underlying problem must be encoded in a quantum circuit, a suitable device must be selected to execute it, and it must be compiled accordingly. This compilation step has a significant influence on the quality of the resulting solution. However, current state-of-the-art compilation tools treat the quantum circuit as a sequence of instructions without considering the actual application it realizes -- wasting a yet untapped potential to increase the solution quality. In this work, a different approach is explored that explicitly incorporates the application considered and aims to optimize its solution quality during compilation. Initial results show the benefits of this approach: For an industry-inspired application of a quantum generative model, the proposed approach outperformed Qiskit's most-optimized compilation scheme and led to better solution quality. Therefore, this work presents a first step towards application-aware compilation.

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arXiv2024

Analog information decoding of bosonic quantum LDPC codes

Quantum error correction is crucial for scalable quantum information processing applications. Traditional discrete-variable quantum codes that use multiple two-level systems to encode logical information can be hardware-intensive. An alternative approach is provided by bosonic codes, which use the infinite-dimensional Hilbert space of harmonic oscillators to encode quantum information. Two promising features of bosonic codes are that syndrome measurements are natively analog and that they can be concatenated with discrete-variable codes. In this work, we propose novel decoding methods that explicitly exploit the analog syndrome information obtained from the bosonic qubit readout in a concatenated architecture. Our methods are versatile and can be generally applied to any bosonic code concatenated with a quantum low-density parity-check (QLDPC) code. Furthermore, we introduce the concept of quasi-single-shot protocols as a novel approach that significantly reduces the number of repeated syndrome measurements required when decoding under phenomenological noise. To realize the protocol, we present a first implementation of time-domain decoding with the overlapping window method for general QLDPC codes, and a novel analog single-shot decoding method. Our results lay the foundation for general decoding algorithms using analog information and demonstrate promising results in the direction of fault-tolerant quantum computation with concatenated bosonic-QLDPC codes.

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arXiv2024

Mixed-Dimensional Qudit State Preparation Using Edge-Weighted Decision Diagrams

Quantum computers have the potential to solve important problems which are fundamentally intractable on a classical computer. The underlying physics of quantum computing platforms supports using multi-valued logic, which promises a boost in performance over the prevailing two-level logic. One key element to exploiting this potential is the capability to efficiently prepare quantum states for multi-valued, or qudit, systems. Due to the time sensitivity of quantum computers, the circuits to prepare the required states have to be as short as possible. In this paper, we investigate quantum state preparation with a focus on mixed-dimensional systems, where the individual qudits may have different dimensionalities. The proposed approach automatically realizes quantum circuits constructing a corresponding mixed-dimensional quantum state. To this end, decision diagrams are used as a compact representation of the quantum state to be realized. We further incorporate the ability to approximate the quantum state to enable a finely controlled trade-off between accuracy, memory complexity, and number of operations in the circuit. Empirical evaluations demonstrate the effectiveness of the proposed approach in facilitating fast and scalable quantum state preparation, with performance directly linked to the size of the decision diagram. The implementation is freely available as part of Munich Quantum Toolkit~(MQT), under the framework MQT Qudits at github.com/cda-tum/mqt-qudits.

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arXiv2024

The MQT Handbook: A Summary of Design Automation Tools and Software for Quantum Computing

Quantum computers are becoming a reality and numerous quantum computing applications with a near-term perspective (e.g., for finance, chemistry, machine learning, and optimization) and with a long-term perspective (e.g., for cryptography or unstructured search) are currently being investigated. However, designing and realizing potential applications for these devices in a scalable fashion requires automated, efficient, and user-friendly software tools that cater to the needs of end users, engineers, and physicists at every level of the entire quantum software stack. Many of the problems to be tackled in that regard are similar to design problems from the classical realm for which sophisticated design automation tools have been developed in the previous decades. The Munich Quantum Toolkit (MQT) is a collection of software tools for quantum computing developed by the Chair for Design Automation at the Technical University of Munich which explicitly utilizes this design automation expertise. Our overarching objective is to provide solutions for design tasks across the entire quantum software stack. This entails high-level support for end users in realizing their applications, efficient methods for the classical simulation, compilation, and verification of quantum circuits, tools for quantum error correction, support for physical design, and more. These methods are supported by corresponding data structures (such as decision diagrams) and core methods (such as SAT encodings/solvers). All of the developed tools are available as open-source implementations and are hosted on https://github.com/cda-tum.

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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

Forward and Backward Constrained Bisimulations for Quantum Circuits using Decision Diagrams

Efficient methods for the simulation of quantum circuits on classic computers are crucial for their analysis due to the exponential growth of the problem size with the number of qubits. Here we study lumping methods based on bisimulation, an established class of techniques that has been proven successful for (classic) stochastic and deterministic systems such as Markov chains and ordinary differential equations. Forward constrained bisimulation yields a lower-dimensional model which exactly preserves quantum measurements projected on a linear subspace of interest. Backward constrained bisimulation gives a reduction that is valid on a subspace containing the circuit input, from which the circuit result can be fully recovered. We provide an algorithm to compute the constraint bisimulations yielding coarsest reductions in both cases, using a duality result relating the two notions. As applications, we provide theoretical bounds on the size of the reduced state space for well-known quantum algorithms for search, optimization, and factorization. Using a prototype implementation, we report significant reductions on a set of benchmarks. In particular, we show that constrained bisimulation can boost decision-diagram-based quantum circuit simulation by several orders of magnitude, allowing thus for substantial synergy effects.

quant-ph↗

arXiv2024

A Framework for the Design and Realization of Alternative Superconducting Quantum Architectures

Superconducting quantum hardware architectures have been designed by considering the physical constraints of the underlying physics. These general-purpose architectures leave room for customization and optimization that can be exploited with alternative architectures specific to the quantum applications that will be executed on the quantum hardware. However, the corresponding design steps are hardly integrated yet and still rely heavily on manual labor. In this work, we provide a software framework that aims at providing a foundation to address this drawback. To this end, we first review the design of superconducting quantum hardware architectures and, afterwards, propose a cohesive framework encapsulating the design flow of an application-specific quantum hardware architecture. The resulting framework integrates high-level architecture generation optimized for a quantum application, the physical layout of the architecture, as well as optimization of the layout in a methodical manner. The framework with a reference implementation is available via https://github.com/cda-tum/dasqa under an open-source license.

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arXiv2024

HamilToniQ: An Open-Source Benchmark Toolkit for Quantum Computers

In this paper, we introduce HamilToniQ, an open-source, and application-oriented benchmarking toolkit for the comprehensive evaluation of Quantum Processing Units (QPUs). Designed to navigate the complexities of quantum computations, HamilToniQ incorporates a methodological framework assessing QPU types, topologies, and multi-QPU systems. The toolkit facilitates the evaluation of QPUs' performance through multiple steps including quantum circuit compilation and quantum error mitigation (QEM), integrating strategies that are unique to each stage. HamilToniQ's standardized score, H-Score, quantifies the fidelity and reliability of QPUs, providing a multidimensional perspective of QPU performance. With a focus on the Quantum Approximate Optimization Algorithm (QAOA), the toolkit enables direct, comparable analysis of QPUs, enhancing transparency and equity in benchmarking. Demonstrated in this paper, HamilToniQ has been validated on various IBM QPUs, affirming its effectiveness and robustness. Overall, HamilToniQ significantly contributes to the advancement of the quantum computing field by offering precise and equitable benchmarking metrics.

quant-ph↗

arXiv2024

A Framework to Formulate Pathfinding Problems for Quantum Computing

With the applications of quantum computing becoming more and more widespread, finding ways that allow end users without experience in the field to apply quantum computers to solve their individual problems is becoming a crucial task. However, current optimization algorithms require problem instances to be posed in complex formats that are challenging to formulate, even for experts. In particular, the Quadratic Unconstrained Binary Optimization (QUBO) formalism employed by many quantum optimization algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA), involves the mathematical rewriting of constraints under strict conditions. To facilitate this process, we propose a framework to automatically generate QUBO formulations for pathfinding problems. This framework allows users to translate their specific problem instances into formulations that can be passed directly to quantum algorithms for optimization without requiring any expertise in the field of quantum computing. It supports three different encoding schemes that can easily be compared without requiring manual reformulation efforts. The resulting QUBO formulations are robust and efficient, reducing the previously tedious and error-prone reformulation process to a task that can be completed in a matter of seconds. In addition to an open-source Python package available on https://github.com/cda-tum/mqt-qubomaker, we also provide a graphical user interface accessible through the web (https://cda-tum.github.io/mqt-qubomaker/), which can be used to operate the framework without requiring the end user to write any code.

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arXiv2024

Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications

Radar and camera fusion yields robustness in perception tasks by leveraging the strength of both sensors. The typical extracted radar point cloud is 2D without height information due to insufficient antennas along the elevation axis, which challenges the network performance. This work introduces a learning-based approach to infer the height of radar points associated with 3D objects. A novel robust regression loss is introduced to address the sparse target challenge. In addition, a multi-task training strategy is employed, emphasizing important features. The average radar absolute height error decreases from 1.69 to 0.25 meters compared to the state-of-the-art height extension method. The estimated target height values are used to preprocess and enrich radar data for downstream perception tasks. Integrating this refined radar information further enhances the performance of existing radar camera fusion models for object detection and depth estimation tasks.

cs.CV↗

arXiv2024

Computational Capabilities and Compiler Development for Neutral Atom Quantum Processors: Connecting Tool Developers and Hardware Experts

Neutral Atom Quantum Computing (NAQC) emerges as a promising hardware platform primarily due to its long coherence times and scalability. Additionally, NAQC offers computational advantages encompassing potential long-range connectivity, native multi-qubit gate support, and the ability to physically rearrange qubits with high fidelity. However, for the successful operation of a NAQC processor, one additionally requires new software tools to translate high-level algorithmic descriptions into a hardware executable representation, taking maximal advantage of the hardware capabilities. Realizing new software tools requires a close connection between tool developers and hardware experts to ensure that the corresponding software tools obey the corresponding physical constraints. This work aims to provide a basis to establish this connection by investigating the broad spectrum of capabilities intrinsic to the NAQC platform and its implications on the compilation process. To this end, we first review the physical background of NAQC and derive how it affects the overall compilation process by formulating suitable constraints and figures of merit. We then provide a summary of the compilation process and discuss currently available software tools in this overview. Finally, we present selected case studies and employ the discussed figures of merit to evaluate the different capabilities of NAQC and compare them between two hardware setups.

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