Reviewerly/exHarmony huggingface dataset:
Quantum Circuit Optimization through Iteratively Pre-Conditioned Gradient Descent

Gate-based quantum algorithms are typically implemented by circuits consisting of many single-qubit and multi-qubit gates operating on a quantum input state, followed by measurements of the output state. For certain quantum subroutines, such as initial state preparation and quantum Fourier transforms, explicit decompositions of the circuit in terms of single-qubit and two-qubit maximally entangling gates may exist. However, they often lead to large-depth circuits that are challenging for noisy intermediate-scale quantum (NISQ) hardware. Additionally, exact decompositions might only exist for some modular quantum circuits. Therefore, it is essential to find gate combinations that approximate these circuits to high fidelity with potentially low depth. Gradient-based optimization has been used to find such approximate decompositions. Still, these traditional optimizers often run into problems of slow convergence requiring many iterations, and performing poorly in the presence of noise, a factor that is especially relevant for NISQ hardware. Here we present iteratively preconditioned gradient descent (IPG) for optimizing quantum circuits and demonstrate performance speedups for state preparation and implementation of quantum algorithmic subroutines. IPG is a noise-resilient, higher-order algorithm that has shown promising gains in convergence speed for classical optimizations, converging locally at a linear rate for convex problems and superlinearly when the solution is unique. Specifically, we show an improvement in fidelity by a factor of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> for preparing a 4-qubit W state and a maximally entangled 5-qubit GHZ state with compared to other commonly used classical optimizers tuning the same ansatz. Such faster convergence with promise for noise-resilience could provide advantages for quantum algorithms on NISQ hardware, especially since the cost of running each iteration on a quantum computer is substantially higher than the classical optimizer step.
llvm2CryptoLine: Verifying Arithmetic in Cryptographic C Programs

Correct implementations of cryptographic primitives are essential for modern security. These implementations often contain arithmetic operations involving non-linear computations that are infamously hard to verify. We present llvm2CryptoLine, an automated formal verification tool for arithmetic operations in cryptographic C programs. llvm2CryptoLine successfully verifies 51 arithmetic C programs from industrial cryptographic libraries OpenSSL, wolfSSL and NaCl. Most of the programs are verified fully automatically and efficiently. A screencast that showcases llvm2CryptoLine can be found at https://youtu.be/QXuSmja45VA. Source code is available at https://github.com/fmlab-iis/llvm2cryptoline.
Deep-Learning Model Extraction Through Software-Based Power Side-Channel

Deep learning (DL) techniques have been increasingly applied across various applications, facing a growing number of security threats. One such threat is model extraction, an attack that steals the Intellectual Property of DL models, either by recovering the same functionality or retrieving high-fidelity models. Current model extraction methods can be categorized as learning-based or cryptanalytic, with the latter relying on model queries and computational methods to recover parameters. However, these are limited to shallow neural networks and are computationally prohibitive for deeper DL models. In this paper, we propose leveraging software-based power analysis, specifically the Intel Running Average Power Limit (RAPL) technique, for DL model extraction. RAPL allows us to measure power leakage of the most popular activation function, ReLU, through a software interface. Consequently, the ReLU branch direction can be leaked in the software power side-channel, a vulnerability common in many state-of-the-art DL frameworks. We introduce a novel methodology for model extraction Algorithm from input gradient assisted by side channel information. We implement our attack on the oneDNN framework, the most popular library on Intel processors. Compared to prior work, our model extraction, assisted by the software power side-channel, only requires 0.8% of the queries to retrieve as-layer MLP. We also successfully apply our method to a common Convolutional Neural Network (CNN) - Lenet-5. To the best of our knowledge, this is the first work that extracts CNN models with more than 5 layers based solely on queries and software.
Secure-by-Construction Design Methodology for CPUs: Implementing Secure Speculation on the RTL

Spectre and Meltdown attacks proved Transient Execution Side Channels to be a notable challenge for designing secure microarchitectures. Various countermeasures against these threats were proposed on the electronic system level. However, addressing all possible attack scenarios requires the design and analysis of bit-and cycle-accurate implementations. We present a novel secure-by-construction RTL design methodology based on a new hardware protection framework underpinned by a generic control infrastructure that can be integrated into industry-grade microarchitectures. The methodology uses formal verification to systematically detect possible leakage paths and to customize the generic infrastructure accordingly for the design. We propose an iterative flow which semi-automatically leads to an RTL design that is guaranteed to be secure w.r.t. transient execution attacks. A case study for the methodology is conducted on BOOMv3, an open-source RISC-V processor with a deep out-of-order pipeline, and the resulting secure RTL design is benchmarked on an FPGA setup. Our design outperforms a design based on conservative countermeasures, improving the incurred overhead by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{3}\times/\boldsymbol{4}\times$</tex> (depending on the threat model) while maintaining the same level of security.
Toward a Unified Hybrid HPCQC Toolchain

In the expanding field of quantum computing (QC), efficient and seamless integration of QC and high performance computing (HPC) elements (e.g., quantum hardware, classical hardware, and software infrastructure on both sides) plays a crucial role. This paper addresses the development of a unified toolchain designed for hybrid quantum-classical systems. Our work proposes a design for a unified hybrid high performance computing - quantum computing (HPCQC) toolchain that tackles pressing issues such as scalability, cross-technology execution, and ahead-of-time (AOT) optimization.
Benchmarking Chain Strength: An Optimal Approach for Quantum Annealing

Quantum annealing (QA) is a promising optimization technique used to find global optimal solution of a combinatorial optimization problem by leveraging quantum fluctuations. In QA, the problem being solved is mapped onto the quantum processing unit (QPU) composed of qubits through a procedure called minor-embedding. The qubits are connected by a network of couplers, which determine the strength of the interactions between the qubits. The strength of the couplers that connect qubits within a chain is often referred to as the chain strength. The appropriate balance of chain strength is equally imperative in enabling the qubits to interact with one another in a way that is strong enough to obtain the optimal solution, but not excessively strong so as not to bias the original problem terms. To this end, we address the problem of identifying the optimal chain strength through the utilization of Path Integral Monte Carlo (PIMC) quantum simulation algorithm. The results indicate that our judicious choice of chain strength parameter facilitates enhancements in quantum annealer performance and solution quality, thereby paving the way for QA to compete with, or potentially outperform, classical optimization algorithms.
Machine Learning-Based Predictive Modeling for Designing Transmon Superconducting Qubits

Scaling of superconducting-based quantum computers has been possible since the invention of transmon qubits due to their low charge noise sensitivity. However, pursuing fault-tolerant and computationally powerful quantum processors may require incorporating more qubits, which may amplify the complexity of the large design space exploration and simulation. Inspired by the recent CMOS Machine Learning assisted electronic design automation to solve similar issues, we claim this work is the first to demonstrate applying machine learning in superconducting circuit design. To start, we attempt to predict the characteristics of individual transmon qubits with a machine learning-based approach based on the simulation data collected with relevant software frameworks like Qiskit Metal and ANSYS Electronics. Our approach aims to perform forward modeling to predict the qubit engineering parameters such as frequency. On the other hand, the machine learning-based model can also infer, through inverse modeling, the candidate sets of design space parameters like the device dimensions satisfying a particular specification while obeying the geometrical constraint of the transmon types. This work paves an alternative way for future applications in designing and automating superconducting quantum computing circuits.
Decision Diagrams for Symbolic Verification of Quantum Circuits

With the rapid development of quantum computing, automatic verification of quantum circuits becomes more and more important. While several decision diagrams (DDs) have been introduced in quantum circuit simulation and verification, none of them supports symbolic computation. Algorithmic manipulations of symbolic objects, however, have been identified as crucial, if not indispensable, for several verification tasks. This paper proposes the first decision-diagram approach for operating symbolic objects and verifying quantum circuits with symbolic terms. As a notable example, our symbolic tensor decision diagrams (symbolic TDD) could verify the functionality of the 160-qubit quantum Fourier transform circuit within three minutes. Moreover, as demonstrated on Bernstein-Vazirani algorithm, Grover's algorithm, and the bit-flip error correction code, the symbolic TDD enables efficient verification of quantum circuits with user-supplied oracles and/or classical controls.
Single Qubit State Estimation on NISQ Devices with Limited Resources and SIC-POVMs

Current quantum computers have the potential to overcome classical computational methods, however, the capability of the algorithms that can be executed on noisy intermediate-scale quantum devices is limited due to hardware imperfections. Estimating the state of a qubit is often needed in different quantum protocols, due to the lack of direct measurements. In this paper, we consider the problem of estimating the quantum state of a qubit in a quantum processing unit without conducting direct measurements of it. We consider a parameterized measurement model to estimate the quantum state, represented as a quantum circuit, which is optimized using the quantum tomographic transfer function. We implement and test the circuit using the quantum computer of the Technical Research Centre of Finland as well as an IBM quantum computer. We demonstrate that the set of positive operator-valued measurements used for the estimation is symmetric and informationally complete. Moreover, the resources needed for qubit estimation are reduced when direct measurements are allowed, keeping the symmetric property of the measurements.
Hiperwalk: Simulation of Quantum Walks with Heterogeneous High-Performance Computing

The Hiperwalk package is designed to facilitate the simulation of quantum walks using heterogeneous high-performance computing, taking advantage of the parallel processing power of diverse processors such as CPUs, GPUs, and acceleration cards. This package enables the simulation of both the continuous-time and discrete-time quantum walk models, effectively modeling the behavior of quantum systems on large graphs. Hiperwalk features a user-friendly Python package frontend with comprehensive documentation, as well as a high-performance C-based inner core that leverages parallel computing for efficient linear algebra calculations. This versatile tool empowers researchers to better understand quantum walk behavior, optimize implementation, and explore a wide range of potential applications, including spatial search algorithms.
Automatically Resolving Dependency-Conflict Building Failures via Behavior-Consistent Loosening of Library Version Constraints

Python projects grow quickly by code reuse and building automation based on third-party libraries. However, the version constraints associated with these libraries are prone to mal-configuration, and this forms a major obstacle to correct project building (known as dependency-conflict (DC) building failure). Our empirical findings suggest that such mal-configured version constraints were mainly prepared manually, and could essentially be refined for better quality to improve the chance of successful project building. We propose a LooCo approach to refining Python projects’ library version constraints by automatically loosening them to maximize their solutions, while keeping the libraries to observe their original behaviors. Our experimental results with real-life Python projects report that LooCo could efficiently refine library version constraints (0.4s per version loosening) by effective loosening (5.5 new versions expanded on average) automatically, and transform 54.8% originally unsolvable cases into solvable ones (i.e., successful building) and significantly increase solutions (21 more on average) for originally solvable cases.
Optimistic Entanglement Purification in Quantum Networks

Noise and photon loss encountered on quantum channels pose a major challenge for reliable entanglement generation in quantum networks. In near-term networks, heralding is required to inform endpoints of successfully generated entanglement. If after heralding, entanglement fidelity is too low, entanglement purification may be utilized to probabilistically increase fidelity. Traditionally, purification protocols proceed as follows: generate heralded EPR pairs, execute a series of quantum operations on two or more pairs between two nodes, and classically communicate results to check for success. Purification may require several rounds while qubits are stored in memories, vulnerable to decoherence. In this work, we explore notions of optimistic purification, wherein classical communication required for heralding and purification is delayed, possibly to the end of the process. Optimism reduces the overall time EPR pairs are stored in memory, increasing fidelity while possibly decreasing EPR pair rate due to decreased heralding and purification failure. We apply optimism to the entanglement pumping scheme, ground- and satellite-based EPR generation sources, and current state-of-the-art purification circuits that include several measurement and purification checkpoints. We evaluate performance in view of a number of parameters, including link length, EPR source rate and fidelity; and memory coherence time. We show that while our optimistic protocol increases fidelity, the traditional approach may even decrease fidelity for longer distances. We study the trade-off between rate and fidelity under entanglement-based QKD, and find that optimistic schemes can yield higher rates compared to non-optimistic counterparts, with most advantages seen in scenarios with low initial fidelity and short coherence times.
How Practitioners Expect Code Completion?

Code completion has become a common practice for programmers during their daily programming activities. It automatically predicts the next tokens or statements that the programmers may use. Code completion aims to substantially save keystrokes and improve the programming efficiency for programmers. Although there exists substantial research on code completion, it is still unclear what practitioner expectations are on code completion and whether these expectations are met by the existing research. To address these questions, we perform a study by first interviewing 15 professionals and then surveying 599 practitioners from 18 IT companies about their expectations on code completion. We then compare the practitioner expectations with the existing research by conducting a literature review of papers on code completion published in major publication venues from 2012 to 2022. Based on the comparison, we highlight the directions desirable for researchers to invest efforts toward developing code completion techniques for meeting practitioner expectations.
Knowledge-Based Version Incompatibility Detection for Deep Learning

Version incompatibility issues are rampant when reusing or reproducing deep learning models and applications. Existing techniques are limited to library dependency specifications declared in PyPI. Therefore, these techniques cannot detect version issues due to undocumented version constraints or issues involving hardware drivers or OS. To address this challenge, we propose to leverage the abundant discussions of DL version issues from Stack Overflow to facilitate version incompatibility detection. We reformulate the problem of knowledge extraction as a Question-Answering (QA) problem and use a pre-trained QA model to extract version compatibility knowledge from online discussions. The extracted knowledge is further consolidated into a weighted knowledge graph to detect potential version incompatibilities when reusing a DL project. Our evaluation results show that (1) our approach can accurately extract version knowledge with 84% accuracy, and (2) our approach can accurately identify 65% of known version issues in 10 popular DL projects with a high precision (92%), while two state-of-the-art approaches can only detect 29% and 6% of these issues with 33% and 17% precision respectively.
Reinforcement Learning Quantum Local Search

Quantum Local Search (QLS) is a promising approach that employs small-scale quantum computers to tackle large combinatorial optimization problems through local search on quantum hardware. However, the random selection of the sub-problem to solve in QLS may not be efficient. In this study, we propose a reinforcement learning (RL) based approach for training an agent to improve sub-problem selection in QLS beyond random selection. Our results demonstrate that the RL agent effectively enhances the average approximation ratio of QLS on fully-connected random Ising problems, indicating the potential of combining RL techniques with Noisy Intermediate-scale Quantum (NISQ) algorithms. This research opens a promising direction for integrating RL into quantum computing to enhance the performance of optimization tasks.
Chinese Text Spotter Exploiting Spatial Semantic Information in Scene Text Images

Chinese scene text spotting plays a significant role in computer vision applications as it involves extracting and interpreting Chinese information from scene images. However, existing methods often lack specialized designs for complex spatial semantic information in Chinese text images, leading to various degrees of false detections in Chinese scene text images. In this paper, we present SSTS, a novel Spatial Semantic Text Spotter for Chinese text image detection and recognition. SSTS utilizes a concise Transformer encoder and decoder architecture to effectively model spatial semantic information and predict the spatial orientation of Chinese text, reducing the likelihood of false detections caused by different combinations of Chinese characters in various orientations. Experimental results on Chinese scene public datasets demonstrate the superiority of our proposed method. In particular, SSTS achieves a new record of 78.5% in the 1-NED metric on the ReCTS dataset.
Parameter-Free Basis Allocation for Efficient Multiple Metric Learning

Abstract Metric learning involves learning a metric function for distance measurement, which plays an important role in improving the performance of classification or similarity-based algorithms. Multiple metric learning is essential for efficiently reflecting the local properties between instances, as single metric learning has limitations in reflecting the nonlinear structure of complex datasets. Previous research has proposed a method for learning a smooth metric matrix function through data manifold to address the challenge of independently learning multiple metrics. However, this method uses the basic distance-based clustering algorithm to set the anchor points, which are the basis for local metric learning, and the number of basis metrics is dependent on the user. We propose a new method that can assign sophisticated anchor points by iteratively partitioning to identify mixed clusters of multi-class instances and cluster the most similar class instances together. In an experiment, we demonstrate the reliability of the automatically set parameter by comparison with the distribution of error rates according to the number of basis metrics of the existing algorithm. Furthermore, we show the superior performance of the proposed method over a fixed parameter setting of existing algorithms and confirm the relative classification accuracy superiority through performance comparison with baseline algorithms.
A Proposal for Estimating Court Boundaries and Displaying Player Positions and Trajectories in Broadcast Handball Videos

This study proposes a method for estimating court boundaries and displaying player positions and trajectories for broadcast handball videos. The videos typically cover only about one-third of the court, and the camera angle changes from left to right to follow the players. Initially, an object detection algorithm YOLOv5 is used to detect a goal object in the video, and the camera direction is determined from its position. Court boundaries are estimated in two ways: one using a bounding box around the goal object, and the other with a red area marked on the court. Once the court boundaries are estimated, DeepSort, an object tracking model, is employed to track the players, and their positions and movements are displayed on a top view of the court. The proposed methods were compared by conducting experiments on videos from different venues, and the results indicate that the estimation rates of the two methods were 80.7% and 85.3%, respectively.
Simple Detection Scheme and Performance Evaluation for Quantum Illumination Using Quasi-Bell State

Quantum illumination has been a hot topic in quantum sensing since first proposed by S. Lloyd in 2008. Quantum illumination is an entanglement-based protocol that is able to detect the existence of a target in an environment with low-energy transmissivity and strong thermal noise. The Bell state and the two-mode squeezed vacuum (TMSV) state as an entangled light source have been widely discussed. In our previous study, we clarified that quantum illumination using the quasi-Bell state offers good performance at error probability even though easing the above-mentioned restrictions on the environment. In this study, we considered and discussed a physical implementation of a measurement scheme for the quasi-Bell state-based quantum illumination using a beam splitter and photon counters. As the result, although the measurement scheme is not an optimum scheme, it still offers a clear advantage over that using the TMSV state, which allows optimum quantum detection, under almost situations.