Reviewerly/exHarmony huggingface dataset:
Invited Paper: Accelerating Next-G Wireless Communications with FPGA-Based AI Accelerators

5G and beyond 5G wireless communication has revolutionized our daily lives. However, the increased bandwidth and data rate transfer in 5G present challenges, particularly in the domain of data receiving and recovery tasks. Orthogonal frequency-division multiplexing (OFDM) Symbol detection plays a pivotal role in ensuring efficient and error-free transmission in next-G communications. To ensure optimal performance and address potential bottlenecks and errors, propose a Field-Programmable Gate Array (FPGA)-based AI accelerator to accelerate symbol detection, which holds immense potential for enhancing signal recovery in MIMO systems and unlocking the full efficiency of 5G technology. To be more specific, we employ a hardware-verified Echo State Network (ESN) as the symbol detection method in the multiple-input and multiple-output (MIMO)-OFDM system. The ESN, a hardware-efficient Recurrent Neural Network, features a fixed reservoir network structure and fewer trainable parameters in the output layer. To validate our approach in real time, we construct a Software-defined Radio (SDR) platform. This platform allows us to collect datasets in real-world scenarios using antennas. Our experiment showcases a promising average bit-error rate (BER) of. 04 in the performance of our FPGA-based design under realistic conditions. We were able to achieve 3.3 times the throughput with an increase of only 21.4% LUT and 33 % FF in resource utilization for maximal processing speed design which is relatively lower in comparison with the ESN implementation for SISO systems. [1] In addition, we reduced the BRAM memory and DSP IP usage by 50% and 33.3% respectively.
Effect of Repetitions and Entanglement on Performance of Pauli Feature Map

Quantum Support Vector Classifier (QSVC) is a quantum machine learning model that solves classification problems. QSVC leverages quantum advantage by utilizing a quantum kernel. A quantum kernel requires a Pauli Feature Map (a data encoding circuit) to encode the classical data into the quantum state space. A Pauli Feature Map's performance can be impacted by two of its key attributes-repetitions and entanglement. In this work, we study the performance of Pauli Feature Map-in terms of QSVC execution time and accuracy-for different combinations of repetitions and entanglement on two different datasets.
Full State Quantum Circuit Simulation Beyond Memory Limit

Quantum circuit simulation (QCS) is essential in the noisy intermediate scale quantum (NISQ) era when real quantum computers are scarce. However, fully tracking the states of a quantum system in QCS is highly challenging due to the exponential memory growth that significantly limits the computational reach of classical systems for QCS. Though it is straightforward to leverage secondary storage to extend the scale of QCS, excessive data movement between memory and storage dominates the simulation time, making this solution unrealistic. To tackle this challenge, we identify an intrinsic property of QCS and implement an open-source framework to effectively reduce data movement by >116x. We evaluate the framework on various benchmarks and demonstrate 4x memory reduction with only <20% overhead. On a memory constrained system, we show that it extends the scale of QCS to 32 qubits (64 GB memory requirement) while existing simulators are bounded to 28 qubits (4 GB memory requirement). Our implementation can be accessed via https://github.com/Zhaoyilunnn/qdao.
quAPL: Modeling Quantum Computation in an Array Programming Language

Most contemporary quantum programming languages describe computation as circuits, using a host classical counterpart to drive the execution of quantum programs. However, the circuit model adds expensive complexity to quantum algorithmic development and decreases the transparency of connections between syntax and formal semantics in quantum programs. We argue that producing a high-level quantum programming language without reference to circuits is possible and necessary. We summarize desirable features in future high-level quantum programming languages and provide evidence supporting array pro-gramming languages as a natural paradigm for quantum algorithmic expression at the circuit level and beyond. We highlight why APL is a profitable host programming language to attain this goal progressively. In particular, we demonstrate how features provided by APL, such as native support of complex numbers and matrix operations, naturally capture quan-tum operations while bringing a less cluttered syntax that encodes and encapsulates the linear character of quantum circuit execution. We discuss implementation details of quAPL, an APL library for quantum circuit specification, simulation, and execution intended to provide a gradual ramp toward developing composable procedural abstractions. Finally, we discuss the broader implications of our work and the next steps in our research program.
Predictive Models from Quantum Computer Benchmarks

Holistic benchmarks for quantum computers are essential for testing and summarizing the performance of quantum hardware. However, holistic benchmarks-such as algorithmic or randomized benchmarks-typically do not predict a processor's performance on circuits outside the benchmark's necessarily very limited set of test circuits. In this paper, we introduce a general framework for building predictive models from benchmarking data using capability models. Capability models can be fit to many kinds of benchmarking data and used for a variety of predictive tasks. We demonstrate this flexibility with two case studies. In the first case study, we predict circuit (i) process fidelities and (ii) success probabilities by fitting error rates models to two kinds of volumetric benchmarking data. Error rates models are simple yet versatile capability models which assign effective error rates to individual gates, or more general circuit components. In the second case study, we construct a capability model for predicting circuit success probabilities by applying transfer learning to ResNet50, a neural network trained for image classification. Our case studies use data from cloud-accessible quantum computers and simulations of noisy quantum computers.
Quantum Computing, Math, and Physics (QCaMP): Introducing Quantum Computing in High Schools

The nascent but rapidly growing field of Quantum Information Science and Technology has led to an increased demand for skilled quantum workers and an opportunity to build a diverse workforce at the outset. In order to meet this demand and encourage women and underrepresented minorities in STEM to consider a career in QIST, we have developed a curriculum for introducing quantum computing to teachers and students at the high school level with no prerequisites. In 2022, this curriculum was delivered over the course of two one-week summer camps, one targeting teachers and another targeting students. Here, we present an overview of the objectives, curriculum, and activities, as well as results from the formal evaluation of both camps and the outlook for expanding QCaMP in future years.
Challenges in HPCQC Integration

Quantum computing's potential as an adjunct to high-performance computing (HPC) infrastructure is clear, but the integration of the two (HPCQC) faces numerous challenges. These issues primarily lie in the complex infrastructure needed for integration. Key to the HPCQC integration is the creation of a unified hybrid quantum-classical toolchain that reduces latency and supports various quantum technologies. Other obstacles include hardware-related requirements like maintaining uptime and networking. This poster focuses on the challenges.
BOOST: Block Minifloat-Based On-Device CNN Training Accelerator with Transfer Learning

Adapting CNNs to changing problems is challenging on resource-limited edge devices due to intensive computations, high precision requirements, large storage needs, and high bandwidth. This paper presents BOOST, a novel block minifloat (BM)-based parallel CNN training accelerator on memory- and computation-constrained FPGAs for transfer learning (TL). By updating a small number of layers online, BOOST enables adaptation to changing problems. Our approach utilizes a unified 8-bit BM datatype (bm(2,5) ), i.e., with a sign bit, 2 exponent bits, and 5 mantissa bits, and proposes unified Conv and dilated Conv blocks that support non-unit stride and enable task-level parallelism during back-propagation to minimize latency. For ResNet20 and VGG-like training on CIFAR-10 and SVHN datasets, BOOST achieves near 32-bit floating point accuracy, reducing latency by 21%-43% and BRAM usage by 63%-66% compared to back-propagation training without TL. Notably, BOOST outperforms the prior SOTA works to achieve perbatch throughput of 131 and 209 GOPs for ResNet20 and VGG-like respectively.
Semi-Quantum Random Number Generation

Semi-quantum cryptography involves at least one user who is semi-quantum or “classical” in nature. Such a user can only interact with the quantum channel in a very restricted way. Many semi-quantum key distribution protocols have been developed, some with rigorous proofs of security. Here we show for the first time that quantum random number generation is possible in the semi-quantum setting. We also develop a rigorous proof of security, deriving a bound on the random bit generation rate of the protocol as a function of noise in the channel. Our protocol and proof may be broadly applicable to other quantum and semi-quantum cryptographic scenarios where users are limited in their capabilities.
DeepBurning-MixQ: An Open Source Mixed-Precision Neural Network Accelerator Design Framework for FPGAs

Mixed-precision neural networks (MPNNs) that enable the use of just enough data width for a deep learning task promise significant advantages of both inference accuracy and computing overhead. FPGAs with fine-grained reconfiguration capability can adapt the processing with distinct data width and models, and hence, can theoretically unleash the potential of MPNNs. Nevertheless, commodity DPUs on FPGAs mostly emphasize generality and have limited support for MPNNs especially the ones with lower data width. In addition, primitive DSPs in FPGAs usually have much larger data width than that is required by MPNNs and haven't been sufficiently co-explored with MPNNs yet. To this end, we propose an open source MPNN accelerator design framework specifically tailored for FPGAs. In this framework, we have a systematic DSP-packing algorithm to pack multiple lower data width MACs in a single primitive DSP and enable efficient implementation of MPNNs. Meanwhile, we take DSP packing efficiency into consideration with MPNN quantization within a unified neural network architecture search (NAS) framework such that it can be aware of the DSP overhead during quantization and optimize the MPNN performance and accuracy concurrently. Finally, we have the optimized MPNN fine-tuned to a fully pipelined neural network accelerator template based on HLS and make best use of available resources for higher performance. Our experiments reveal the resulting accelerators produced by the proposed framework can achieve overwhelming advantages in terms of performance, resource utilization, and inference accuracy for MPNNs when compared with both handcrafted counterparts and prior hardware-aware neural network accelerators on FPGAs.
Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach

Quantum Kernel Estimation (QKE) is a technique based on leveraging a quantum computer to estimate a kernel function that is classically difficult to calculate, which is then used by a classical computer for training a Support Vector Machine (SVM). Given the high number of 2-local operators necessary for realizing a feature mapping hard to simulate classically, a high qubit connectivity is needed, which is not currently possible on superconducting devices. For this reason, neutral atom quantum computers can be used, since they allow to arrange the atoms with more freedom. Examples of neutral-atom-based QKE can be found in the literature, but they are focused on graph learning and use the analogue approach. In this paper, a general method based on the gate model is presented. After deriving 1-qubit and 2-qubit gates starting from laser pulses, a parameterized sequence for feature mapping on 3 qubits is realized. This sequence is then used to empirically compute the kernel matrix starting from a dataset, which is finally used to train the SVM. It is also shown that this process can be generalized up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{N}$</tex> qubits taking advantage of the more flexible arrangement of atoms that this technology allows. The accuracy is shown to be high despite the small dataset and the low separation. This is the first paper that not only proposes an algorithm for explicitly deriving a universal set of gates but also presents a method of estimating quantum kernels on neutral atom devices for general problems using the gate model.
Improving phishing detection in Ethereum transaction network using Quantum Machine Learning

Ethereum is one of the most valuable blockchain networks in terms of the total monetary value locked in it, and arguably been the most active network where new blockchain innovations in research and applications are demonstrated. But, this also leads to such transactional networks being susceptible to a wide variety of threats and attacks in an attempt to gain unreasonable advantage or to undermine the value of the users. Even with the state-of-art classical ML algorithms, detecting such attacks is hard. This motivated us explore quantum machine learning algorithms that can probably be better for such hard problems. This paper proposes to improve phishing detection and performance of QML algorithms such as Quantum Support Vector Machine (QSVM) and Variational Quantum Classifier (VQC) by using a cascaded form of Quantum Random Access Coding (QRAC) encoding scheme. We present detailed ablation studies with other encoding strategies, and comparing with classical ML algorithms and quantum algorithms on simulators and real quantum hardware. For QSVM, we have tried two different implementations of QSVM - one using Kernel estimation using quantum circuits on universal quantum computers, and the other using annealers and for VQC, we benchmarked with different ansatzes with different entangling capacities. For both algorithms we tried different feature maps but consistently observe that QRAC-VQC gives 13% improvement and QRAC-QSVM gives 3% improvement over baselines. Overall we observe that QSVM gives least false negatives thus better at detecting phishing nodes even over classical state-of-art graph convolutional networks. Another important and interesting observation is that the performance of these QML algorithms does not drastically drop with currently available noisy hardware and is almost equal to simulator for a quantum device with high quantum volume or performance.
Bounded Quantum Regular Language Generator

We develop a quantum algorithm to verify the correctness of systems modeled by nondeterministic finite-state automata-a problem that is computationally intractable on conventional computers. Specifically, our algorithm solves the language containment problem in three steps. First, we translate a nondeterministic finite-state automaton into a quantum finite state automaton (QFA) circuit. Second, the QFA is embedded into a larger circuit which generates a superposed set of all bounded strings that are marked to indicate whether they are accepted or not. Finally, we amplify the amplitudes of accepted strings so that they are measured more frequently. The last step may be done either by using Grover's algorithm, or by using FnR, a custom algorithm that we have developed for the cases where Grover's algorithm is not effective. Our work represents the first proposal to apply quantum computing to the problem of verifying conventional systems; our approach would facilitate software verification, program analysis, protocol design, and verification of circuits, among other applications.
Role of Blockchain for IoE Infrastructures and Applications

Blockchain technology provides a decentralized, secure, tamper proof, and auditable way to store data. In the Internet of Everything (IoE), blockchain can provide a vital role in ensuring the security of a large amount of generated by devices. Blockchain can ensure the integrity and accuracy of IoE data without being controlled by a centralized authority. By leveraging smart contracts to execute business rules between devices, blockchain may automate transactions and streamline procedures in IoE. Furthermore, the creation of decentralized identity management for IoE devices may be facilitated by using blockchain technology, providing a safe method of confirming their validity, and prohibiting unwanted access to the network. It is essential to thoroughly grasp blockchain technology before incorporating it into IoE applications. This chapter provides a comprehensive overview of blockchain technology and how it might solve particular problems in the IoE setting. A framework is also suggested for creating blockchain-based IoE application solutions. The IoE ecosystem can gain from improved security and privacy features using blockchain technology, leading to a more dependable and resilient infrastructure.
Driver Anomaly Detection Using Skeleton Images

Many unexpected situations can occur while driving that may lead to dangerous accidents. Some of them may be caused by sudden health problems (e.g. heart attack, stroke, total collapse) or by driver inattention (e.g. microsleep, visual distraction). This has motivated the need for developing the methods that are able to monitor the driver’s state in the first step and to prevent the accidents in the second step (e.g. by activating an acoustic signal, or even by taking over driving). In this paper, we propose a method that can be used for detecting the abnormal driving situations. Our approach is based on two main steps. In the first step, the MNIST-like skeleton images are created with the use of human pose detector. In the second step, an appropriate neural network is used for the final classification. Since we also include the anomalies consisting in an unusual trajectory of a certain body part (not only an unusual shape of body, which can be detected from the isolated images), short sequences of images are examined. The LSTM (long short-term memory) autoencoder is used as a main network architecture. The experiments that are presented show that the proposed method achieves better results than other compared methods.
Gradient Boosting Optimized Through Differential Evolution for Predicting the Testing Effort of Software Projects

Software testing (ST) is one of the most important software development life cycle (SDLC) phases and ST effort is often expressed as a percentage of SDLC effort. Unfortunately, in the literature ST effort percentage ranges from 10% to 60%. In the literature most of the machine learning algorithms and metaheuristics for optimizing them have looked at predicting overall SDLC effort without focusing on any specific SDLC phase, including testing. Therefore, this study investigates the application of the STEP of Gradient Boosting (GB) machine learning regression algorithm optimized through Differential Evolution (DE). Its prediction accuracy is compared with those obtained when the GB is also optimized through Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). The performance of GB-DE, GB-PSO, and GB-GA was also compared to that of statistical regression (SR). Seven data sets of actual projects were selected from an international public repository for software projects. The results showed that GB-DE was statistically better than SR in all seven data sets at 95% confidence, whereas GB-PSO and GB-GA were better than SR in four and three data sets, respectively. Thus, we can conclude that GB-DE can be used for STEP of either new projects or enhancement projects developed in either the third or fourth programming language generation.
QuCardio: application of quantum machine learning for detection of cardiovascular diseases

This research is the first of its kind to leverage the power of Quantum Machine Learning (QML) to perform multi-class classification of Cardiovascular Diseases (CVDs). We propose a novel approach that enables multi-class classification with Pegasos Quantum Support Vector Classifier (QSVC). The QSVC and the Pegasos QSVC significantly outperform the classical SVC by a margin of +10.76% and +9.72%, respectively. The paper further ventures into a quantum deep learning based architecture with a novel Quanvolutional Neural Network (QNN) implementation, outperforming the other models by achieving 97.31% accuracy, 97.41% precision, 97.31% recall, 97.30% F1 score, and 99.10% specificity.
Selective Path Automatic Differentiation: beyond uniform distribution on backpropagation dropout

This paper introduces Selective Path Automatic Differentiation (SPAD), a novel approach to reducing memory consumption and mitigating overfitting in gradient-based models for embedded artificial intelligence. SPAD extends the existing Randomized Automatic Differentiation, proposed by Oktay et al and which draws random paths through the backpropagation graph with matrix injection, by enabling alternative probability distributions on the backpropagation graph, thereby enhancing learning performance and memory management. In a specific iteration, SPAD evaluates and ranks multiple paths within the backpropagation graph. Over subsequent iterations, it preferentially follows these higher-ranked paths. This work also presents a compilation-based technique allowing model-agnostic access to random paths, ensuring generalizability across various model architectures, not restricted to deep models. Experimental evaluations conducted across various optimization functions demonstrate an enhanced minimization performance when employing SPAD. Additionally, deep learning experiments with SPAD notably mitigate overfitting, offering benefits akin to those of traditional dropout methods, but with a concomitant decrease in memory usage. We conclude by discussing the unique stochasticity implications of our work and the potential for it to augment other stochastic techniques in the field.
The Road Ahead: DAO-Secured V2X Infrastructures for Safe and Smart Vehicular Management

Vehicle-to-Everything (V2X) technology relies on wireless communication and coordination, aiming to improve road safety and traffic efficiency by orchestrating the interaction among the vehicles, infrastructures, and various entities. However, existing organizational and operational forms of V2X infrastructures encounter significant challenges, such as the capability to guarantee communication security, privacy, and efficiency especially when a centralized management system is used. The emerging Decentralized Autonomous Organizations and Operations (DAOs) present a solution to these challenges. DAOs embed managerial and operational protocols within a blockchain via smart contracts, facilitating smooth and efficient management of organizations without the necessity for centralized oversight. This letter introduces DAOs into the coordination of V2X infrastructures. Through a tripartite functional architecture including organization, coordination, and execution, we can achieve intelligent collaborative management among road infrastructures and vehicles. The DAO-secured V2X infrastructures are anticipated to augment the safety and efficiency of vehicular management in complex road scenarios, holding applicable potential for developing the intelligent roadways of tomorrow.