Reviewerly/exHarmony huggingface dataset:
Quantum Computer Metrics and HPC Center Environmental Sensor Data Analysis Towards Fidelity Prediction

A tight integration of quantum accelerators with high-performance computers (HPC) necessitates relocating the often highly sensitive quantum devices from controlled laboratory environments to noisy data centers. Various factors impacting fidelity must be tracked and mitigated to ensure the performance of quantum computers in such environments. This paper describes a monitoring system utilizing Internet of Things (IoT) technology incorporating multiple sensors to gather and feed data into a data center-wide database. The data collected is then analyzed to identify correlations between fidelity and environmental factors. Using this data, we propose a novel, adaptive calibration mechanism in which a machine learning model predicts the optimal calibration timing for the quantum computer based on fidelity prediction with varying environmental conditions. In the larger scope, this research aims to enhance our understanding of fidelity-altering factors and develop strategies for maintaining optimal quantum computer performance in real-world, noisy data center environments.
Crosstalk-Based Parameterized Quantum Circuit Approximation

In this paper, we propose an ansatz approximation approach for variational quantum algorithms (VQAs) that uses one of the hardware's main attributes, its crosstalk behavior, as its main approximation driver. By utilizing crosstalk-adaptive scheduling, we are able to apply a circuit-level approximation/optimization to our ansatz. Our design procedure involves first characterizing the hardware's crosstalk and then approximating the circuit by a desired level of crosstalk mitigation, all while effectively reducing its duration and gate counts. We demonstrate the effect of crosstalk mitigation on expressibility, trainability, and entanglement: key components that drive the utility of parameterized circuits. We tested our approach on real quantum hardware against a base configuration, and our results showed superior performance for the circuit-level optimized ansatz over a base ansatz for two quantum chemistry benchmarks. We take into consideration that applications vary in their response to crosstalk, and we believe that this approximation strategy can be used to create ansatze that are expressive, trainable, and with crosstalk mitigation levels tailored for specific workloads.
Falcon: Accelerating Homomorphically Encrypted Convolutions for Efficient Private Mobile Network Inference

Efficient networks, e.g., MobileNetV2, EfficientNet, etc, achieves state-of-the-art (SOTA) accuracy with lightweight computation. However, existing homomorphic encryption (HE)-based two-party computation (2PC) frameworks are not op-timized for these networks and suffer from a high inference overhead. We observe the inefficiency mainly comes from the packing algorithm, which ignores the computation character-istics and the communication bottleneck of homomorphically encrypted depthwise convolutions. Therefore, in this paper, we propose Falcon, an effective dense packing algorithm for HE-based 2PC frameworks. Falcon features a zero-aware greedy packing algorithm and a communication-aware operator tiling strategy to improve the packing density for depth wise convo-lutions. Compared to SOTA HE-based 2PC frameworks, e.g., CrypTFlow2, Iron and Cheetah, Falcon achieves more than 15.6 x, 5.1 x and 1.8 x latency reduction, respectively, at operator level. Meanwhile, at network level, Falcon allows for 1.4 % and 4.2% accuracy improvement over Cheetah on CIFAR-100 and Tiny Imagenet datasets with iso-communication, respecitvely.
A QIR Toolchain with XACC

We present a work-in-progress to create a software toolchain that links the Quantum Intermediate Representation (QIR) to the hardware-agnostic execution framework XACC. The novelty of this work is an implementation of the QIR specification for use in the XACC framework by translating the QIR to an XACC intermediate representation (XIR) and then illustrating how this toolchain will be able to represent both quantum and classical computations. The anticipated impact includes the expansion of quantum programming languages that can be executed on quantum computing hardware and the utilization of QPU hardware agnostic properties of XACC framework.
Accelerating Polynomial Modular Multiplication with Crossbar-Based Compute-in-Memory

Lattice-based cryptographic algorithms built on ring learning with error theory are gaining importance due to their potential for providing post-quantum security. However, these algorithms involve complex polynomial operations, such as polynomial modular multiplication (PMM), which is the most time-consuming part of these algorithms. Accelerating PMM is crucial to make lattice-based cryptographic algorithms widely adopted by more applications. This work introduces a novel high-throughput and compact PMM accelerator, X-Poly, based on the crossbar (XB)-type compute-in-memory (CIM). We identify the most appropriate PMM algorithm for XB-CIM. We then propose a novel bit-mapping technique to reduce the area and energy of the XB-CIM fabric, and conduct processing engine (PE)-level optimization to increase memory utilization and support different problem sizes with a fixed number of XB arrays. X-Poly design achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.1\times 10^{6}$</tex> PMM operations/s throughput and offers 200 x latency improvement compared to the CPU-based implementation. It also achieves 3.9 x throughput per area improvement compared with the state-of-the-art CIM accelerators.
mpiQulacs: A Scalable Distributed Quantum Computer Simulator for ARM-based Clusters

Quantum computer simulators running on classical computers are essential for developing real quantum computers and emerging quantum applications. In particular, state vector simulators, which store a full state vector in memory and update it in every quantum operation, can be used to simulate an arbitrary form of quantum circuits, debug quantum applications, and validate future quantum computers. Since the time and space complexity grows exponentially with the number of qubits and easily exceeds the capability of a single machine, various distributed state vector simulators are developed. However, these are tuned for common CPUs and GPUs, not for ARM CPUs. Therefore, we develop a distributed state vector simulator, mpiQulacs, that is optimized for large-scale simulation on A64FX-based cluster systems. A64FX is a 48-core ARM-based CPU having high-bandwidth memory (HBM2) and also used in one of the world's top supercomputers, Fugaku. As computational optimization, we vectorize qubit operations with Scalable Vector Extension (SVE) to fully utilize its HBM2. To reduce the amount of inter-node communication for scalable simulation, mpiQulacs provides a fused-swap gate that collectively performs multiple swap operations as one operation and a quantum circuit optimizer that automatically adds fused-swap gates to a circuit. We evaluate weak and strong scaling of mpiQulacs with up to 39 qubits on our 512-node A64FX-based cluster system. The result shows that mpiQulacs achieves high-scalability on hundreds of nodes in benchmark and practical quantum circuits, and its performance is comparable with the existing well-known distributed state vector simulators.
Side Channel-Assisted Inference Attacks on Machine Learning-Based ECG Classification

The Electrocardiogram (ECG) measures the electrical cardiac activity generated by the heart to detect abnormal heartbeats and heart attacks. However, the irregular occurrence of the abnormalities demands continuous monitoring of heartbeats. Machine learning techniques are leveraged to automate this task, reducing the labor required during monitoring. In recent years, many companies have launched products with ECG monitoring and irregular heartbeat alerts. Among all classification algorithms, the time series-based algorithm dynamic time warping (DTW) is widely adopted to undertake the ECG classification task. Though progress has been achieved, the DTW-based ECG classification also introduces a new attack vector: the potential leakage of patients' diagnostic results. This paper investigates the potential of side channel-assisted inference attacks on the prevalent DTW-based ECG classification model. In particular, we first identify a vulnerability of DTW for ECG classification, that is, the correlation between warping path choice and prediction results. Based on the vulnerability, we further leverage two types of side-channel attacks, i.e., cache-based side-channel attack Flus+Reload, and trace-based side-channel attack with hardware performance counters, to assess the potential of stealing machine learning-based ECG input samples' labels. Afterward, we build prototypes that leverage Flush+Reload and hardware performance counters to monitor warping path selection with training ECG data, and then construct a predictor to establish a relation between side-channel observations and labels of input ECG samples. Based on experiments, we find that the Flush+Reload-based inference leakage can achieve up to 92.1% and 81.1% attack success rate with Flush+Reload and hardware performance counters to identify the labels of the two ECG samples in DTW.
AdaptivePaste: Intelligent Copy-Paste in IDE

In software development, it is common for programmers to copy-paste or port code snippets and then adapt them to their use case. This scenario motivates the code adaptation task – a variant of program repair which aims to adapt variable identifiers in a pasted snippet of code to the surrounding, preexisting context. However, no existing approach has been shown to effectively address this task. In this paper, we introduce AdaptivePaste, a learning-based approach to source code adaptation, based on transformers and a dedicated dataflow-aware deobfuscation pre-training task to learn meaningful representations of variable usage patterns. We demonstrate that AdaptivePaste can learn to adapt Python source code snippets with 67.8% exact match accuracy. We study the impact of confidence thresholds on the model predictions, showing the model precision can be further improved to 85.9% with our parallel-decoder transformer model in a selective code adaptation setting. To assess the practical use of AdaptivePaste we perform a user study among Python software developers on real-world copy-paste instances. The results show that AdaptivePaste reduces dwell time to nearly half the time it takes to port code manually, and helps to avoid bugs. In addition, we utilize the participant feedback to identify potential avenues for improvement.
Review of Literature on Human Activity Detection and Recognition

Purpose: The objective of this research article is to methodically combine the existing literature on Human Activity Recognition (HAR) and provide an understanding of the present state of the HAR literature. Additionally, the article aims to suggest an appropriate HAR system that can be used for detecting real-time activities such as suspicious behavior, surveillance, and healthcare. Objective: This review study intends to delve into the current state of human activity detection and recognition methods, while also pointing towards promising avenues for further research and development in the field, particularly with regards to complex and multi-task human activity recognition across different domains. Design/Methodology/Approach: A systematic literature review methodology was adopted by collecting and analyzing the required literature available from international and national journals, conferences, databases and other resources searched through the Google Scholar and other search engines. Findings/Result: The systematic review of literature uncovered the various approaches of Human activity detection and recognition. Even though the prevailing literature reports the investigations of several aspects of Human activity detection and recognition, there is still room for exploring the role of this technology in various domains to enhance its robustness in detecting and recognizing of multiple human actions from preloaded CCTV cameras, which can aid in detecting abnormal and suspicious activities and ultimately reduce aberrant human actions in society. Originality/Value: This paper follows a systematic approach to examine the factors that impact the detection and recognition of Human activity and suggests a concept map. The study undertaken supplements the expanding literature on knowledge sharing highlighting its significance. Paper Type: Review Paper.
Single-Qubit Gates Matter for Optimising Quantum Circuit Depth in Qubit Mapping

Quantum circuit transformation (QCT, a.k.a. qubit mapping) is a critical step in quantum circuit compilation. Typically, QCT is achieved by finding an appropriate initial mapping and using SWAP gates to route the qubits such that all connectivity constraints are satisfied. The objective of QCT can be to minimise circuit size or depth. Most existing QCT algorithms prioritise minimising circuit size, potentially overlooking the impact of single-qubit gates on circuit depth. In this paper, we first point out that a single SWAP gate insertion can double the circuit depth, and then propose a simple and effective method that takes into account the impact of single-qubit gates on circuit depth. Our method can be combined with many existing QCT algorithms to optimise circuit depth. The Qiskit SABRE algorithm has been widely accepted as the state-of-the-art algorithm for optimising both circuit size and depth. We demonstrate the effectiveness of our method by embedding it in SABRE, showing that it can reduce circuit depth by up to 50% and 27% on average on, for instance, Google Sycamore and 117 real quantum circuits from MQTBench.
SATformer: Transformer-Based UNSAT Core Learning

This paper introduces SATformer, a novel Transformer-based approach for the Boolean Satisfiability (SAT) problem. Rather than solving the problem directly, SATformer approaches the problem from the opposite direction by focusing on unsatisfiability. Specifically, it models clause interactions to identify any unsatisfiable sub-problems. Using a graph neural network, we convert clauses into clause embeddings and employ a hierarchical Transformer-based model to understand clause correlation. SATformer is trained through a multi-task learning approach, using the single-bit satisfiability result and the minimal unsatisfiable core (MUC) for UNSAT problems as clause supervision. As an end-to-end learning-based satisfiability classifier, the performance of SATformer surpasses that of NeuroSAT significantly. Furthermore, we integrate the clause predictions made by SATformer into modern heuristic-based SAT solvers and validate our approach with a logic equivalence checking task. Experimental results show that our SATformer can decrease the runtime of existing solvers by an average of 21.33%.
QDoor: Exploiting Approximate Synthesis for Backdoor Attacks in Quantum Neural Networks

Quantum neural networks (QNNs) succeed in object recognition, natural language processing, and financial analysis. To maximize the accuracy of a QNN on a Noisy Intermediate Scale Quantum (NISQ) computer, approximate synthesis modifies the QNN circuit by reducing error-prone 2-qubit quantum gates. The success of QNNs motivates adversaries to attack QNNs via backdoors. However, naively transplanting backdoors designed for classical neural networks to QNNs yields only low attack success rate, due to the noises and approximate synthesis on NISQ computers. Prior quantum circuit-based backdoors cannot selectively attack some inputs or work with all types of encoding layers of a QNN circuit. Moreover, it is easy to detect both transplanted and circuit-based backdoors in a QNN. In this paper, we propose a novel and stealthy backdoor attack, QDoor, to achieve high attack success rate in approximately-synthesized QNN circuits by weaponizing unitary differences between uncompiled QNNs and their synthesized counterparts. QDoor trains a QNN behaving normally for all inputs with and without a trigger. However, after approximate synthesis, the QNN circuit always predicts any inputs with a trigger to a predefined class while still acts normally for benign inputs. Compared to prior backdoor attacks, QDoor improves the attack success rate by 13x and the clean data accuracy by 65% on average. Furthermore, prior backdoor detection techniques cannot find QDoor attacks in uncompiled QNN circuits.
Exploring the Potential of Qutrits for Quantum Optimization of Graph Coloring

Recent hardware demonstrations and advances in circuit compilation have made quantum computing with higher- dimensional systems (qudits) on near-term devices an attractive possibility. Some problems have more natural or optimal en- codings using qudits over qubits. We explore this potential by formulating graph 3-coloring, a well-known and difficult problem with practical applications, using qutrits, and solve it using the quantum approximate optimization algorithm (QAOA). Qutrit- based cost and mixer Hamiltonians are constructed along with appropriate quantum circuits using qutrit gates. We run noiseless simulations using PennyLane to compare the formulation against qubit-based QAOA, and analyze the solution quality and resources required. Preliminary results show that the qutrit encoding finds more accurate solutions with a comparable set of hyperparameters, uses half as many qudits, and has a notably smaller circuit depth per layer than an efficient qubit encoding. This work suggests that qutrits may be useful in solving some problems on near-term devices, however further work is required to assess their potential in a noisy environment.
New Evidence from the Monetary Policy Communication and Inflation Expectations Nexus in Africa

Motivated by the dearth of evidence, we examine the impact of monetary policy communication on inflation expectations in Africa. Employing text-mining techniques to analyse the monetary policy statements of six African central banks, we compute a novel index of monetary policy communication (MPCI) using the extracted word counts, readability, sentiment and tone indicators. We provide new evidence of a positive and statistically significant impact of sentiments, tone and the MPCI on inflation expectations in Africa. Our results also reveal a rising influence of sentiments in anchoring expectations during the COVID-19 pandemic era. Our results are robust to alternative measures of inflation expectation and to global economic uncertainty.We recommend concise and lucid policy statements alongside statements with tone and policy decision consistency for anchoring inflation expectations. Furthermore, investors should carefully analyse and respond to signals from central bank communication, incorporating these insights into their investment strategies and risk management practices.
VanQiRA: A Vanishing-State-Based Framework for Quantum Circuit Runtime Assertion

Runtime verification is crucial in quantum computation as quantum systems are susceptible to environmental perturbation. Automating the detection of runtime errors allows an early fallback or correction and can drastically improve the efficiency of quantum computation experiments. However, prior work is either limited to asserting specific quantum states or ad hoc assertion circuit construction. We overcome these limitations by exploiting vanishing states as a general framework, which is flexible in providing trade-offs between error-detection power and assertion circuit complexity. Experiments show the unique benefits of our method in generality and effectiveness, improving quantum circuit execution efficiency and success rates in various benchmarks.
Quantum Architecture Search for Quantum Monte Carlo Integration via Conditional Parameterized Circuits with Application to Finance

Classical Monte Carlo algorithms can theoretically be sped up on a quantum computer by employing amplitude estimation (AE). To realize this, an efficient implementation of state-dependent functions is crucial. We develop a straightforward approach based on pretraining parameterized quantum circuits, and show how they can be transformed into their conditional variant, making them usable as a subroutine in an AE algorithm. To identify a suitable circuit, we propose a genetic optimization approach that combines variable ansatzes and data encoding. We apply our algorithm to the problem of pricing financial derivatives. At the expense of a costly pretraining process, this results in a quantum circuit implementing the derivatives' payoff function more efficiently than previously existing quantum algorithms. In particular, we compare the performance for European vanilla and basket options.
Minimum-Length Chain Embedding for the Phase Unwrapping Problem on D-Wave's Pegasus Graph

In this paper, we study the potential capability of quantum annealing in solving the phase unwrapping problem, an instance of hard computational problems. To solve the phase unwrapping problem using quantum annealing, we deploy the D-Wave Advantage machine which is currently the largest available quantum annealer. The structure of this machine, however, is not compatible with our problem graph structure. Consequently, the problem graph needs to be mapped into the target (Pegasus) graph, and this minor embedding significantly affects the quality of the results. Based on our experiment and also D-Wave's reports, the lower chain lengths can result in a better performance of quantum annealing. In this paper, we propose a new minor embedding algorithm that has the lowest possible chain length for minor embedding the graph of the phase unwrapping problem onto the Pegasus graph. The obtained results using this embedding strongly outperform the results of Auto-embedding provided by the D- Wave's minorminer tool both in the quality of the solution obtained and in the length of the chain.
Mixed-Dimensional Quantum Circuit Simulation with Decision Diagrams

Quantum computers promise to solve several categories of problems faster than classical computers ever could. Current research mostly focuses on qubits, i.e., systems where the unit of information can assume only two levels. However, the underlying physics of most (if not all) of the technological platforms supports more than two levels, commonly referred to as qudits. Performing computations with qudits increases the overall complexity while, at the same time, reducing the number of operations and providing a lower error rate. Furthermore, qudits with different number of levels can be mixed in one system to ease the experimental control and keep representations as compact as possible. Exploiting these capabilities requires dedicated software support to tackle the increased complexity in an automated and efficient fashion. In this paper, we present a qudit simulator that handles mixed-dimensional systems based on Decision Diagrams (DDs). More precisely, we discuss the type of decision diagram introduced as underlying data structure as well as the resulting implementation. Experimental evaluations demonstrate that the proposed solution is capable of efficiently simulating mixed-dimensional quantum circuits, with specific use cases including more than 100 qudits in one circuit. The source code of the simulator is available via github.com/cda-tdum/MiSiM under the MIT license.