Reviewerly/exHarmony huggingface dataset:
Quantum Network Utility Maximization

Network Utility Maximization (NUM) is a mathe-matical framework that has endowed researchers with powerful methods for designing and analyzing classical communication protocols. NUM has also enabled the development of distributed algorithms for solving the resource allocation problem, while at the same time providing certain guarantees, e.g., that of fair treatment, to the users of a network. We extend here the notion of NUM to quantum networks, and propose three quantum utility functions - each incorporating a different entanglement measure. We aim both to gain an understanding of some of the ways in which quantum users may perceive utility, as well as to explore structured and theoretically-motivated methods of simultaneously servicing multiple users in distributed quantum systems. Using our quantum NUM constructions, we develop an optimization framework for networks that use the single-photon scheme for entanglement generation, which enables us to solve the resource allocation problem while exploring rate-fidelity tradeoffs within the network topologies that we consider. We learn that two of our utility functions, which are based on distillable entanglement and secret key fraction, are in close agreement with each other and produce similar solutions to the optimization problems we study. While these two utilities place a higher emphasis on end-to-end fidelity, our third utility- based on entanglement negativity - has more favorable mathematical properties, and tends to place a higher value on the rate at which users receive entangled resources. These contrasting behaviors thus provide ideas regarding the suitability of quantum network utility definitions to different quantum applications.
Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning

Reinforcement learning is a growing field in AI with a lot of potential. Intelligent behavior is learned automatically through trial and error in interaction with the environment. However, this learning process is often costly. Using variational quantum circuits as function approximators potentially can reduce this cost. In order to implement this, we propose the quantum natural policy gradient (QNPG) algorithm - a second-order gradient-based routine that takes advantage of an efficient approximation of the quantum Fisher information matrix. We experimentally demonstrate that QNPG outperforms first-order based training on different Contextual Bandits environments regarding convergence speed and stability and moreover reduces the sample complexity. Furthermore, we provide evidence for the practical feasibility of our approach by training on a 12-qubit hardware device.
Parallelizing Quantum-Classical Workloads: Profiling the Impact of Splitting Techniques

Quantum computers are the next evolution of computing hardware. Quantum devices are being exposed through the same familiar cloud platforms used for classical computers, and enabling seamless execution of hybrid applications that combine quantum and classical components. Quantum devices vary in features, e.g., number of qubits, quantum volume, CLOPS, noise profile, queuing delays and resource cost. So, it may be useful to split hybrid workloads with either large quantum circuits or a large number of quantum circuits, into smaller units. In this paper, we profile two workload splitting techniques on IBM's Quantum Cloud: (1) Circuit parallelization, to split one large circuit into multiple smaller ones, and (2) Data parallelization to split a large number of circuits that run on one device to smaller batches of circuits that run on concurrently on multiple devices. These can improve the utilization of heterogeneous quantum hardware, but involve trade-offs. We evaluate these techniques on two key algorithmic classes: Variational Quantum Eigensolver (VQE) and Quantum Support Vector Machine (QSVM), and measure the impact on quality of the result, circuit execution times, quantum resource usage, and processing overheads, relative to a baseline without such parallelization. The results are obtained on real hardware and complemented by simulations. We see that (1) VQE with circuit cutting is ~ 39% better in ground state estimation than the uncut version, and (2) QSVM that combines data parallelization with a reduced feature set yields upto 3 ×improvement in quantum workload execution time and reduces quantum resource use by 3 ×, while providing comparable accuracy. Error mitigation can improve the accuracy by ≈ 7% and reduce resource footprint by ≈4% compared to the best case among the considered scenarios.
Quantum Sensor Network Algorithms for Transmitter Localization

A quantum sensor (QS) is able to measure various physical phenomena with extreme sensitivity. QSs have been used in several applications such as atomic interferometers, but few applications of a quantum sensor network (QSN) have been proposed or developed. We look at a natural application of QSN-localization of an event (in particular, of a wireless signal transmitter). In this paper, we develop effective quantum-based techniques for the localization of a transmitter using a QSN. Our approaches pose the localization problem as a well-studied quantum state discrimination (QSD) problem and address the challenges in its application to the localization problem. In particular, a quantum state discrimination solution can suffer from a high probability of error, especially when the number of states (i.e., the number of potential transmitter locations in our case) can be high. We address this challenge by developing a two-level localization approach, which localizes the transmitter at a coarser granularity in the first level, and then, in a finer granularity in the second level. We address the additional challenge of the impracticality of general measurements by developing new schemes that replace the QSD's measurement operator with a trained parameterized hybrid quantum-classical circuit. Our evaluation results using a custom-built simulator show that our best scheme is able to achieve meter-level (1-5m) localization accuracy; in the case of discrete locations, it achieves near-nerfect (99-100%) classification accuracy.
Pitfalls in Experiments with DNN4SE: An Analysis of the State of the Practice

Software engineering (SE) techniques are increasingly relying on deep learning approaches to support many SE tasks, from bug triaging to code generation. To assess the efficacy of such techniques researchers typically perform controlled experiments. Conducting these experiments, however, is particularly challenging given the complexity of the space of variables involved, from specialized and intricate architectures and algorithms to a large number of training hyper-parameters and choices of evolving datasets, all compounded by how rapidly the machine learning technology is advancing, and the inherent sources of randomness in the training process. In this work we conduct a mapping study, examining 194 experiments with techniques that rely on deep neural networks (DNNs) appearing in 55 papers published in premier SE venues to provide a characterization of the state of the practice, pinpointing experiments’ common trends and pitfalls. Our study reveals that most of the experiments, including those that have received ACM artifact badges, have fundamental limitations that raise doubts about the reliability of their findings. More specifically, we find: 1) weak analyses to determine that there is a true relationship between independent and dependent variables (87% of the experiments), 2) limited control over the space of DNN relevant variables, which can render a relationship between dependent variables and treatments that may not be causal but rather correlational (100% of the experiments), and 3) lack of specificity in terms of what are the DNN variables and their values utilized in the experiments (86% of the experiments) to define the treatments being applied, which makes it unclear whether the techniques designed are the ones being assessed, or how the sources of extraneous variation are controlled. We provide some practical recommendations to address these limitations.
Multi-Agent Lifelong Implicit Neural Learning

Implicit neural representations (INRs) have emerged as powerful tools for the continuous representation of signals, finding applications in imaging, computer graphics, and signal compression. Additionally, decentralized multi-agent systems are crucial in various applications, frequently leading to enhanced reliability and efficiencies in computation and communication. In this paper, we explore using multi-agent Lifelong Learning (LL) systems for learning INRs. We propose a rigorous problem setup and evaluation plan to investigate the efficacy of such systems compared to single-agent and multi-task learning baselines. Our research, conducted across varied dimensions, demonstrates promising results, thereby contributing a novel perspective to the realm of continual learning.
Challenges of Depth Estimation for Transparent Objects

Transparent objects and surfaces are pervasive in man-made environments and need to be considered in any vision system. Accurate depth data is a key factor for such systems reliability, requiring transparency to be inferred, due to the sensing challenges. However, the current state-of-the-art methods to predict the depth of such objects are not reliable enough to ensure safe operation of robots in arbitrary complex scenes. In order to better understand and improve upon existing solutions, we evaluate the performance of a variety of depth estimation methods. Doing so, we disentangle the different factors impacting their performance. Among our findings, neural radiance fields offer the best accuracy, but are very sensitive to the number of images used to understand the scene, and do not benefit from any level of object understanding to help them fill in the gaps.
Deep Learning Based Feature Envy Detection Boosted by Real-World Examples

Feature envy is one of the well-recognized code smells that should be removed by software refactoring. A major challenge in feature envy detection is that traditional approaches are less accurate whereas deep learning-based approaches are suffering from the lack of high-quality large-scale training data. Although existing refactoring detection tools could be employed to discover real-world feature envy examples, the noise (i.e., false positives) within the resulting data could significantly influence the quality of the training data as well as the performance of the models trained on the data. To this end, in this paper, we propose a sequence of heuristic rules and a decision tree-based classifier to filter out false positives reported by state-of-the-art refactoring detection tools. The data after filtering serve as the positive items in the requested training data. From the same subject projects, we randomly select methods that are different from positive items as negative items. With the real-world examples (both positive and negative examples), we design and train a deep learning-based binary model to predict whether a given method should be moved to a potential target class. Different from existing models, it leverages additional features, i.e., coupling between methods and classes (CBMC) and the message passing coupling between methods and classes (MCMC) that have not yet been exploited by existing approaches. Our evaluation results on real-world open-source projects suggest that the proposed approach substantially outperforms the state of the art in feature envy detection, improving precision and recall by 38.5% and 20.8%, respectively.
Gitor: Scalable Code Clone Detection by Building Global Sample Graph

Code clone detection is about finding out similar code fragments, which has drawn much attention in software engineering since it is important for software maintenance and evolution. Researchers have proposed many techniques and tools for source code clone detection, but current detection methods concentrate on analyzing or processing code samples individually without exploring the underlying connections among code samples.
Improving Quantum Circuit Synthesis with Machine Learning

In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that mini-mize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs. Unitary synthesis, the process of finding a quantum circuit that implements some target unitary matrix, is able to solve this problem optimally in many cases. However, current bottom-up unitary synthesis algorithms are limited by their exponentially growing run times. We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. This paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries. QSeed maintains low gate counts and offers a speedup of 3.7 x in synthesis time over the state of the art for a 64 qubit modular exponentiation circuit, a core component in Shor's factoring algorithm. QSeed's performance improvements also generalize to families of circuits not seen during the training process.
QWalkVis: Quantum Walks Visualization Application

Quantum walks (QWs) are the quantum analogue to classical random walks. We present visualizations for quantum walks and show how they can be used to teach quantum concepts such as superposition and interference. Using our Quantum Walks Visualization Application (QWalkVis) for visualizing quan-tum walks lets the user select the dimensions, number of states, and number of steps in the walk and generates probabilistic plots on-the-fly. Users can view a plot for each step of the walk, allowing them to compare the probability distributions as time progresses. Visualizations share an important space in education; QWalkVis was created to aid students in learning about quantum walks and foundational quantum concepts through an interactive design. We highlight some potential use cases of QWalkVis for both self-directed student learning and the education in a classroom.
Entanglement Distribution in the Quantum Internet: An Optimal Decision Problem Formulation

The entanglement distribution process is widely en-visioned as one of the key functionalities of the Quantum Internet. Its engineering is, thus, foundational to effectively implement communication protocols in quantum networks. In this paper, we exploit the Markov Decision Process formalism to model the entanglement distribution as an optimal decision problem. Furthermore, we analyze the impact of reward functions on two key performance metrics, namely: the average distribution time and the average distributed cluster size. From this analysis, we gain some insights for choosing a reward function that meets suitable figures of merit for an overlaying communication protocol.
One Nine Availability of a Photonic Quantum Computer on the Cloud Toward HPC Integration

The integration of Quantum Computers (QC) within High-Performance Computing (HPC) environments holds significant promise for solving real-world problems by leveraging the strengths of both computational paradigms. However, the integration of a complex QC platform in an HPC infrastructure poses several challenges, such as operation stability in non-laboratory like environments, and scarce access for maintenance. Currently, only a few fully-assembled QCs currently exist worldwide, employing highly heterogeneous and cutting-edge technologies. These platforms are mostly used for research purposes, and often bear closer resemblance to laboratory assemblies rather than production-ready, stable, and consistently-performing turnkey machines. Moreover, public cloud services with access to such quantum computers are scarce and their availability is generally limited to few days per week. In November 2022, we introduced the first cloud-accessible general-purpose quantum computer based on single photons. One of the key objectives was to maintain the platform's availability as high as possible while anticipating seamless compatibility with HPC hosting environment. In this article, we describe the design and implementation of our cloud-accessible quantum computing platform, and demonstrate one nine availability (92%) for external users during a six-month period, higher than most online services. This work lay the foundation for advancing quantum computing accessibility and usability in hybrid HPC-QC infrastructures.
Numerical Circuit Synthesis and Compilation for Multi-State Preparation

Near-term quantum computers have significant error rates and short coherence times, so compilation of circuits to be as short as possible is essential. Two types of compilation problems are typically considered: circuits to prepare a given state from a fixed input state, called “state preparation”; and circuits to implement a given unitary operation, for example by “unitary synthesis”. In this paper we solve a more general problem: the transformation of a set of m states to another set of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> states, which we call “multi-state preparation”. State preparation and unitary synthesis are special cases; for state preparation, m=1, while for unitary synthesis, m is the dimension of the full Hilbert space. We generate and optimize circuits for multi-state preparation numerically. In cases where a top-down approach based on matrix decompositions is also possible, our method finds circuits with substantially (up to 40 %) fewer two-qubit gates. We discuss possible applications, including efficient preparation of macroscopic superposition (“cat”) states and synthesis of quantum channels.
Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans

Practical quantum computing (QC) is still in its in-fancy and problems considered are usually fairly small, especially in quantum machine learning when compared to its classical counterpart. Image processing applications in particular require models that are able to handle a large amount of features, and while classical approaches can easily tackle this, it is a major challenge and a cause for harsh restrictions in contemporary QC. In this paper, we apply a hybrid quantum machine learning approach to a practically relevant problem with real world-data. That is, we apply hybrid quantum transfer learning to an image processing task in the field of medical image processing. More specifically, we classify large CT-scans of the lung into COVID-19, CAP, or Normal. We discuss quantum image embedding as well as hybrid quantum machine learning and evaluate several approaches to quantum transfer learning with various quantum circuits and embedding techniques.
Generation and Distribution of GHZ States in Quantum Networks

In quantum networks, multipartite entangled states distributed over the network are useful in implementing and supporting many quantum network applications for communications, sensing, and computing. The focus of our work is to de-velop techniques to efficiently generate distributed Greenberger-Horne-Zeilinger (GHZ) states, a special class of multipartite entanglement states. Prior works on generating GHZ states have focused on the objective of minimizing the number of maximally entangled (bipartite) pairs (EPs), while ignoring the stochastic nature of quantum processes and assuming uniform network links for generating EPs. In contrast, in our work, we take into consideration the stochastic nature of quantum networks, and focus on maximizing the expected generated rate of the GHZ states under given fidelity constraints. In this context, we develop two efficient generation schemes, viz., Fusion - Retain - only and General Fusion, comprised of optimal or near-optimal sub-steps. Both schemes, at a high-level, first determine a way to “connect” the nodes over which the GHZ state is to distributed, and then, determine a sequence of fusion operations on the EPs created over the connections. Using extensive simulations over a quantum network simulator (NetSquid), we demonstrate the effectiveness of our developed techniques and show that our schemes outperform prior work as well as a simple centralized approach by up to orders of magnitude while generating GHZ states of tolerable fidelity.
Optimization at the Interface of Unitary and Non-unitary Quantum Operations in PCOAST

The Pauli-based Circuit Optimization, Analysis and Synthesis Toolchain (PCOAST) was recently introduced as a framework for optimizing quantum circuits. It converts a quantum circuit to a Pauli-based graph representation and provides a set of optimization subroutines to manipulate that internal representation as well as methods for re-synthesizing back to a quantum circuit. In this paper, we focus on the set of subroutines which look to optimize the PCOAST graph in cases involving unitary and non-unitary operations as represented by nodes in the graph. This includes reduction of node cost and node number in the presence of preparation nodes, reduction of cost for Clifford operations in the presence of preparations, and measurement cost reduction using Clifford operations and the classical remapping of measurement outcomes. These routines can also be combined to amplify their effectiveness. We evaluate the PCOAST optimization subroutines using the Intel® Quantum SDK on examples of the Variational Quantum Eigensolver (VQE) algorithm. This includes synthesizing a circuit for the simultaneous measurement of a mutually commuting set of Pauli operators. We find for such measurement circuits the overall average ratio of the maximum theoretical number of two-qubit gates to the actual number of two-qubit gates used by our method to be 7.91.
A Turing Test: Are Ai Chatbots Behaviorally Similar to Humans?

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Design by Contract for Deep Learning APIs

Deep Learning (DL) techniques are increasingly being incorporated in critical software systems today. DL software is buggy too. Recent work in SE has characterized these bugs, studied fix patterns, and proposed detection and localization strategies. In this work, we introduce a preventative measure. We propose design by contract for DL libraries, DL Contract for short, to document the properties of DL libraries and provide developers with a mechanism to identify bugs during development. While DL Contract builds on the traditional design by contract techniques, we need to address unique challenges. In particular, we need to document properties of the training process that are not visible at the functional interface of the DL libraries. To solve these problems, we have introduced mechanisms that allow developers to specify properties of the model architecture, data, and training process. We have designed and implemented DL Contract for Python-based DL libraries and used it to document the properties of Keras, a well-known DL library. We evaluate DL Contract in terms of effectiveness, runtime overhead, and usability. To evaluate the utility of DL Contract, we have developed 15 sample contracts specifically for training problems and structural bugs. We have adopted four well-vetted benchmarks from prior works on DL bug detection and repair. For the effectiveness, DL Contract correctly detects 259 bugs in 272 real-world buggy programs, from well-vetted benchmarks provided in prior work on DL bug detection and repair. We found that the DL Contract overhead is fairly minimal for the used benchmarks. Lastly, to evaluate the usability, we conducted a survey of twenty participants who have used DL Contract to find and fix bugs. The results reveal that DL Contract can be very helpful to DL application developers when debugging their code.