Reviewerly/exHarmony huggingface dataset:
BFSig: Leveraging File Significance in Bus Factor Estimation

Software projects experience the departure of developers due to various reasons. As developers are one of the main sources of knowledge in software projects, their absence will inevitably result in a certain degree of knowledge depletion. Bus Factor (BF) is a metric to evaluate how this knowledge loss can affect the project's continuity. Conventionally, BF is calculated as the smallest set of developers, removing over half the project knowledge upon departure. Current state-of-the-art approaches measure developers' knowledge by the number of authored files, utilizing version control system (VCS) information. However, numerous studies have shown that files in software projects have different significance. In this study, we explore how weighting files according to their significance affects the performance of two prevailing BF estimators. We derive significance scores by computing five well-known graph metrics from the project's dependency graph: PageRank, In-/Out-/All-Degree, and Betweenness Centralities. Furthermore, we introduce BFSig , a prototype of our approach. Finally, we present a new dataset comprising reported BF scores collected by surveying software practitioners from five prominent Github repositories. Our results indicate that BFSig outperforms the baselines by up to an 18% reduction in terms of Normalized Mean Absolute Error (NMAE). Moreover, BFSig yields 18% fewer False Negatives in identifying potential risks associated with low BF. Besides, our respondent confirmed BFSig versatility by showing its ability to assess the BF of the project's subfolders. In conclusion, we believe to estimate BF from authorship, software components of higher importance should be assigned heavier weight. Currently, BFSig exclusively explores the topological characteristics of these components. Nevertheless, considering attributes such as code complexity and bug proneness could potentially enhance the performance of BFSig.
Optimal Layout Synthesis for Quantum Circuits as Classical Planning

In Layout Synthesis, the logical qubits of a quantum circuit are mapped to the physical qubits of a given quantum hardware platform, taking into account the connectivity of physical qubits. This involves inserting SWAP gates before an operation is applied on distant qubits. Optimal Layout Synthesis is crucial for practical Quantum Computing on current error-prone hardware: Minimizing the number of SWAP gates directly mitigates the error rates when running quantum circuits. In recent years, several approaches have been proposed for minimizing the required SWAP insertions. The proposed exact approaches can only scale to a small number of qubits. In this paper, we provide two encodings for Optimal Layout Synthesis as a classical planning problem. We use optimal classical planners to synthesize the optimal layout for a standard set of benchmarks. Our results show the scalability of our approach compared to previous leading approaches. We can optimally map circuits with 9 qubits onto a 14 qubit platform, which could not be handled before by exact methods.
Invited Paper: VerilogEval: Evaluating Large Language Models for Verilog Code Generation

The increasing popularity of large language models (LLMs) has paved the way for their application in diverse domains. This paper proposes a benchmarking framework tailored specifically for evaluating LLM performance in the context of Verilog code generation for hardware design and verification. We present a comprehensive evaluation dataset consisting of 156 problems from the Verilog instructional website HDLBits. The evaluation set consists of a diverse set of Verilog code generation tasks, ranging from simple combinational circuits to complex finite state machines. The Verilog code completions can be automatically tested for functional correctness by comparing the transient simulation outputs of the generated design with a golden solution. We also demonstrate that the Verilog code generation capability of pretrained language models could be improved with supervised fine-tuning by bootstrapping with LLM generated synthetic problem-code pairs.
Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators Through Training with Right-Censored Gaussian Noise

Compute-in-Memory (CiM), built upon non-volatile memory (NVM) devices, is promising for accelerating deep neural networks (DNNs) owing to its in-situ data processing capability and superior energy efficiency. To battle device variations, noise injection training is commonly used, which perturbs weights with Gaussian noise during training to make the model more robust to weight variations. Despite its prevalence, however, existing successes are mostly empirical, and very little theoretical support is available. Even the most fundamental questions such as why Gaussian but not other types of noises should be used is not answered. In this work, through formally analyzing the effect of injecting Gaussian noise in training to improve the k-th percentile performance (KPP), a realistic worst-case performance metric, for the first time we provide a theoretical justification of the effectiveness of the approach. We further show that surprisingly Gaussian noise is not the best option, contrary to what has been taken for granted in the literature. Instead, a right-censored Gaussian noise significantly improves the KPP of DNNs. We further propose an automated method to determine the optimal hyperparameters for injecting this right-censored Gaussian noise during the training process. Our method achieves up to a 26% improvement in KPP compared to the state-of-the-art methods employed to enhance DNN robustness under the impact of device variations.
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 theo-retical 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 number of required iterations. 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.
Leveraging the Verifier’s Dilemma to Double Spend in Bitcoin

We describe and analyze perishing mining, a novel block-withholding mining strategy that lures profit-driven miners away from doing useful work on the public chain by releasing block headers from a privately maintained chain. We then introduce the dual private chain (DPC) attack, where an adversary that aims at double spending increases its success rate by intermittently dedicating part of its hash power to perishing mining. We detail the DPC attack’s Markov decision process, evaluate its double spending success rate using Monte Carlo simulations. We show that the DPC attack lowers Bitcoin’s security bound in the presence of profit-driven miners that do not wait to validate the transactions of a block before mining on it.
Differentiable Quantum Architecture Search for Quantum Reinforcement Learning

Differentiable quantum architecture search (DQAS) is a gradient-based framework to design quantum circuits automatically in the NISQ era. It was motivated by such as low fidelity of quantum hardware, low flexibility of circuit architecture, high circuit design cost, barren plateau (BP) problem, and periodicity of weights. People used it to address error mitigation, unitary decomposition, and quantum approximation optimization problems based on fixed datasets. Quantum reinforcement learning (QRL) is a part of quantum machine learning and often has various data. QRL usually uses a manually designed circuit. However, the pre-defined circuit needs more flexibility for different tasks, and the circuit design based on various datasets could become intractable in the case of a large circuit. The problem of whether DQAS can be applied to quantum deep Q-learning with various datasets is still open. The main target of this work is to discover the capability of DQAS to solve quantum deep Q-learning problems. We apply a gradient-based framework DQAS on reinforcement learning tasks and evaluate it in two different environments - cart pole and frozen lake. It contains input- and output weights, progressive search, and other new features. The experiments conclude that DQAS can design quantum circuits automatically and efficiently. The evaluation results show significant outperformance compared to the manually designed circuit. Furthermore, the performance of the automatically created circuit depends on whether the super-circuit learned well during the training process. This work is the first to show that gradient-based quantum architecture search is applicable to QRL tasks.
Quantum Generative Adversarial Networks For Anomaly Detection In High Energy Physics

The standard model (SM) of particle physics represents a theoretical paradigm for the description of the fundamental forces of nature. Despite its broad applicability, the SM does not enable the description of all physically possible events. The detection of events that cannot be described by the SM, which are typically referred to as anomalous, and the related potential discovery of exotic physical phenomena is a non-trivial task. The challenge becomes even greater with next-generation colliders that will produce even more events with additional levels of complexity. The additional data complexity motivates the search for unsupervised anomaly detection methods that do not require prior knowledge about the underlying models. In this work, we develop such a technique. More explicitly, we employ a quantum generative adversarial network to identify anomalous events. The method learns the background distribution from SM data and, then, determines whether a given event is characteristic for the learned background distribution. The proposed quantum-powered anomaly detection strategy is tested on proof-of-principle examples using numerical simulations and IBM Quantum processors. We find that the quantum generative techniques using ten times fewer training data samples can yield comparable accuracy to the classical counterpart for the detection of the Graviton and Higgs particles. Additionally, we empirically compute the capacity of the quantum model and observe an improved expressivity compared to its classical counterpart.
Random Quantum Neural Networks for Noisy Image Recognition

The application of classical Random Neural Networks (RNN) has been restricted to deterministic digital systems that generate probability distributions instead of the stochastic characteristics of random spiking signals. To optimize the utilization of the stochastic properties inherent in RNN, we propose a new category of supervised Random Quantum Neural Networks (RQNN) that incorporate a resilient training methodology. The RQNN under consideration utilizes a combination of classical and quantum algorithms, incorporating superposition state and angle encoding characteristics that draw inspiration from quantum information theory. Additionally, the model incorporates the stochastic random spiking property of neuron information encoding, which is known to exhibit spatial-temporal features similar to those observed in the brain. The proposed RQNN model has undergone thorough validation, relying on hybrid classical-quantum algorithms through a real IBM quantum processor. The proposed RQNN model is tested on the MNIST, Fashion-MNIST, and KMNIST datasets. The results indicate that it achieved an average classification accuracy of 59.6% when presented with unseen noisy test images. The experimental results demonstrate the efficacy and robustness of the proposed RQNN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The early version of the work is posted in arXiv: https://doi.org/10.48550/arXiv.2203.01764. in classifying noisy images that have not been previously encountered.
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-Based Gradients

Variational quantum algorithms (VQAs) have attracted a lot of attention from the quantum computing community for the last few years. Their hybrid quantum-classical nature with relatively shallow quantum circuits makes them a promising platform for demonstrating the capabilities of noisy intermediate scale quantum (NISQ) devices. Although the classical machine learning community focuses on gradient-based parameter optimization, finding near-exact gradients for variational quantum circuits (VQCs) with the parameter-shift rule introduces a large sampling overhead. Therefore, gradient-free optimizers have gained popularity in quantum machine learning circles. Among the most promising candidates is the simultaneous perturbation stochastic approximation (SPSA) algorithm, due to its low computational cost and inherent noise resilience. We introduce a novel approach that uses the approximated gradient from SPSA in combination with state-of-the-art gradient-based classical optimizers. We demonstrate numerically that this out-performs both standard SPSA and the parameter-shift rule in terms of convergence rate and absolute error in simple regression tasks. The improvement of our novel approach over SPSA with stochastic gradient descent (SGD) is even amplified when shot- and hardware-noise are taken into account. We also demonstrate that error mitigation does not significantly affect our results.
Quantum Simulations for Carbon Capture on Metal-Organic Frameworks

Direct air capture of Carbon Dioxide is a technical solution that does not rely on natural processes to capture CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> from the atmosphere. In DAC, the filter material is designed to specifically bind CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> molecules. Hence a high-capacity filter is sought. We aim to leverage the potential of quantum computing to improve the filters used in DAC. Metal-Organic Frameworks (MOFs) have high surface area and tunable pore sizes which makes them an attractive material for gas storage and separation. Using the variational quantum eigensolver (VQE) algorithm, we find the minimum of the potential energy surface (PES) by first considering only the active site of the MOF (the metal ion). For complex systems, we employ Density Matrix Embedding Theory and use VQE as a fragment solver at the binding site. Techniques like deparameterisation are used to minimise the count of trainable parameters. We present results of ideal and noisy simulations as well as from a real hardware device. Resources are estimated for MOFs unit cell. The findings from our study demonstrates the potential of quantum computing to effectively perform quantum simulations of strongly correlated fragments.
The Role of Entanglement in Quantum-Relaxation Based Optimization Algorithms

Quantum Random Access Optimizer (QRAO) is a quantum-relaxation based optimization algorithm proposed by Fuller et al. that utilizes Quantum Random Access Code (QRAC) to encode multiple variables of binary optimization in a single qubit. Differing from standard quantum optimizers such as QAOA, it utilizes the eigenstates of local quantum Hamiltonians that are not diagonal in the computational basis. There are indications that quantum entanglement may not be needed to solve binary optimization problems with standard quantum optimizers because their maximal eigenstates of diagonal Hamiltonians include classical states. In this study, while quantumness does not always improve the performance of quantum relaxations, we observed that there exist some instances in which quantum relaxation succeeds to find optimal solutions with the help of quantumness. Our results suggest that QRAO not only can scale the instances of binary optimization problems solvable with limited quantum computers but also can benefit from quantum entanglement,
Constant Depth Code Deformations in the Parity Architecture

We present a protocol to encode and decode arbitrary quantum states in the parity architecture with constant circuit depth using measurements, local nearest-neighbor and single-qubit operations only. While this procedure typically requires a quadratic overhead of simultaneous qubit measurements, it allows for a simple and low-depth implementation of logical multi-qubit gates in the parity encoding via code deformation. We discuss how such encoding and decoding schemes can be used to flexibly change the size and shape of the underlying code to enable a more efficient implementation of quantum gates or algorithms. We apply the new findings to the QAOA which leads to a constant depth implementation using local gates at the same optimization performance as the standard, potentially non-local, QAOA approach without the parity encoding. Furthermore, we show that our method can reduce the depth of implementing the quantum Fourier transform by a factor of two when allowing measurements.
Pooling Techniques in Hybrid Quantum-Classical Convolutional Neural Networks

Quantum machine learning has received significant interest in recent years, with theoretical studies showing that quantum variants of classical machine learning algorithms can provide good generalization from small training data sizes. However, there are notably no strong theoretical insights about what makes a quantum circuit design better than another, and comparative studies between quantum equivalents have not been done for every type of classical layers or techniques crucial for classical machine learning. Particularly, the pooling layer within convolutional neural networks is a fundamental operation left to explore. Pooling mechanisms significantly improve the performance of classical machine learning algorithms by playing a key role in reducing input dimensionality and extracting clean features from the input data. In this work, an in-depth study of pooling techniques in hybrid quantum-classical convolutional neural networks (QCCNNs) for classifying 2D medical images is performed. The performance of four different quantum and hybrid pooling techniques is studied: mid-circuit measurements, ancilla qubits with controlled gates, modular quantum pooling blocks and qubit selection with classical postprocessing. We find similar or better performance in comparison to an equivalent classical model and QCCNN without pooling and conclude that it is promising to study architectural choices in QCCNNs in more depth for future applications.
SnCQA: A hardware-efficient equivariant quantum convolutional circuit architecture

We propose SnCQA, a set of hardware-efficient variational circuits of equivariant quantum convolutional circuits respective to permutation symmetries and spatial lattice symmetries with the number of qubits n. By exploiting permutation symmetries of the system, such as lattice Hamiltonians common to many quantum many-body and quantum chemistry problems, Our quantum neural networks are suitable for solving machine learning problems where permutation symmetries are present, which could lead to significant savings of computational costs. Aside from its theoretical novelty, we find our simulations perform well in practical instances of learning ground states in quantum computational chemistry, where we could achieve comparable performances to traditional methods with few tens of parameters. Compared to other traditional variational quantum circuits, such as the pure hardware-efficient ansatz (pHEA), we show that SnCQA is more scalable, accurate, and noise resilient (with 20× better performance on 3 × 4 square lattice and 200% - 1000% resource savings in various lattice sizes and key criterions such as the number of layers, parameters, and times to converge in our cases), suggesting a potentially favorable experiment on near-time quantum devices.
Limits on Revocable Proof Systems, With Implications for Stateless Blockchains

Motivated by the goal of building a cryptocurrency with succinct global state, we introduce the abstract notion of a revocable proof system. We prove an information-theoretic result on the relation between global state size and the required number of local proof updates as statements are revoked (e.g., coins are spent). We apply our result to conclude that there is no useful trade-off point when building a stateless cryptocurrency: the system must either have a linear-sized global state (in the number of accounts in the system) or require a near-linear rate of local proof updates. The notion of a revocable proof system is quite general and also provides new lower bounds for set commitments, vector commitments and authenticated dictionaries.
Zero-Shot Video Moment Retrieval Using BLIP-Based Models

Video Moment Retrieval (VMR) is a challenging task at the intersection of vision and language, with the goal to retrieve relevant moments from videos corresponding to natural language queries. State-of-the-art approaches for VMR often rely on large amounts of training data including frame-level saliency annotations, weakly supervised pre-training on speech captions, and signals from additional modalities such as audio, which can be limiting in practical scenarios. Moreover, most of these approaches make use of pre-trained spatio-temporal backbones for aggregating temporal features across multiple frames, which incurs significant training and inference costs. To address these limitations, we propose a zero-shot approach with sparse frame-sampling strategies that does not rely on additional modalities and performs well with feature extraction from just individual frames. Our approach uses Bootstrapped Language-Image Pre-training based models (BLIP/BLIP-2), which have been shown to be effective for various downstream vision-language tasks, even in zero-shot settings. We show that such models can be easily repurposed as effective, off-the-shelf feature extractors for VMR. On the QVHighlights benchmark for VMR, our approach outperforms both zero-shot approaches and supervised approaches (without saliency score annotations) by at least $$25\%$$ and $$21\%$$ respectively, on all metrics. Further, we also show that our approach is comparable to state-of-the-art supervised approaches trained on saliency score annotations and additional modalities, with a gap of at most $$7\%$$ across all metrics.