Reviewerly/exHarmony huggingface dataset:
Feedback-Based Steering for Quantum State Preparation

State preparation is an essential component in quantum information science. A recently developed steering protocol utilizes a sequence of generalized measurements on a detector to steer a quantum system towards a desired state. However, it is designed as an open-loop technique that requires accurate modeling of the overall quantum system and can be prone to errors. To address this challenge, we propose a closed-loop control technique that introduces feedback to the steering protocol, providing robustness to noise and faster state convergence. We introduce two strategies for feedback: (1) a gradient-based active steering protocol that changes the detector-system coupling conditioned on the detector's readout and (2) tuning the fixed detector-system coupling via model-free reinforcement learning. We study the effectiveness of these strategies under various noise models, including both incoherent and decoherent noise, and discuss potential applications in quantum technologies.
An Improved Rotation-equivariant Detector for Aerial Object Detection

Aerial object detection faces various complex scenarios, such as rotation, different scales, and occlusion. However, the design of rotational detection regression loss has been a bottleneck due to boundary discontinuity and its inconsistency with the dynamic detection metric. This paper introduces a rotational isodenaturation detector for aerial object detection, which builds upon the Redet model and further improves its performance. We leverage a multi-level feature representation and preserve high-resolution feature information obtained from the previous step by using average pooling downsampling. To enhance feature extraction and capture multilayer feature information, we employ a two-way feature fusion network. For regression loss, we propose a method based on Gaussian Wasserstein distance, which converts the rotating bounding box into a two-dimensional Gaussian distribution, approximating the unit loss of non-differentiable rotating matter caused by Gaussian Wasserstein distance (GWD). Extensive experiments on the challenging aerial image dataset DOTA-v1.0 demonstrate that our method achieves significant performance improvement in aerial object detection tasks, with an mAP of 76.44%. This result is 1.08% higher than the mAP of the baseline model.
Fast training of fully-connected Boltzmann Machines on an Adiabatic Quantum Computer

We demonstrate how to leverage thermal noise within an Adiabatic Quantum Computer (AQC) to train a complete Boltzmann Machine (BM). In contrast to previous implementations, our fully-connected BM includes hidden units and we sample both negative and positive statistics from the AQC. By training the network on a standard benchmark Bars and Stripes dataset, the maximally parallelized classical algorithm takes 11.4 times longer to reach the same likelihood as the quantum algorithm, indicating that AQCs may expedite BM training. The ability of AQCs to sample Boltzmann-distributed configurations quickly has the potential to impact BMs training as well as influence the field of stochastic machine learning.
Combating Alert Fatigue in the Security Operations Centre

It is generally recognised that the demand for experienced cybersecurity professionals outstrips supply by a considerable margin. In consequence, those who are in post are often overworked and under stress. One symptom of this that arises in the Security Operations Centre (SOC) context goes by the name of ‘alert fatigue’ — the SOC analyst is effectively blinded by the sheer volume of system-generated messages demanding attention, most of which turn out not to have security significance. This paper reports on work carried out within the AISOC (AI Security Operations Centre) project, a collaboration between Birmingham City University (BCU) and METCLOUD, a managed service provider. The aim of AISOC is to use Artificial Intelligence (AI) and Data Science (DS) techniques to create an intelligent assistant that will reduce the workload and amplify the skills of analysts working in SOCs, resulting in more efficient and effective detection of, response to, and remediation of cyber-attacks. In the process it will increase job satisfaction and staff retention, and enable a more scalable business model. The research contribution presented is a novel approach, named CHILL (Continuous Human-in-the-Loop Learning) whereby human expert and AI agent work as a team, with knowledge being transferred from expert to AI apprentice over time. It also emphasises the ability to interpret observed phenomena in terms of root causes. An early implementation of AISOC is currently being used by METCLOUD analysts on a trial basis alongside their current software tools. Feedback so far validates the concept of an intelligent assistant to reduce workload and to allow more time to be spent on problematic cases of potentially high security significance. The research was informed by a ‘deep dive’ analysis of real alert data generated from events produced by the Windows audit system. This identified two measures we estimate will reduce the number of Windows logon alerts processed by analysts by nearly 90%.
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances

In recent years, Variational Quantum Algorithms (VQAs) have emerged as a promising approach for solving optimization problems on quantum computers in the NISQ era. However, one limitation of VQAs is their reliance on fixed-structure circuits, which may not be taylored for specific problems or hardware configurations. A leading strategy to address this issue are Adaptative VQAs, which dynamically modify the circuit structure by adding and removing gates, and optimize their parameters during the training. Several Adaptative VQAs, based on heuristics such as circuit shallowness, entanglement capability and hardware compatibility, have already been proposed in the literature, but there is still lack of a systematic comparison between the different methods. In this paper, we aim to fill this gap by analyzing three Adaptative VQAs: Evolutionary Variational Quantum Eigensolver (EVQE), Variable Ansatz (VAns), already proposed in the literature, and Random Adapt-VQE (RA-VQE), a random approach we introduce as a baseline. In order to compare these algorithms to traditional VQAs, we also include the Quantum Approximate Optimization Algorithm (QAOA) in our analysis. We apply these algorithms to QUBO problems and study their performance by examining the quality of the solutions found and the computational times required. Additionally, we investigate how the choice of the hyperparameters can impact the overall performance of the algorithms, highlighting the importance of selecting an appropriate methodology for hyperparameter tuning. Our analysis sets benchmarks for Adaptative VQAs designed for near-term quantum devices and provides valuable insights to guide future research in this area.
Fast Quantum Gate Design with Deep Reinforcement Learning Using Real-Time Feedback on Readout Signals

The design of high-fidelity quantum gates is difficult because it requires the optimization of two competing effects, namely maximizing gate speed and minimizing leakage out of the qubit subspace. We propose a deep reinforcement learning algorithm that uses two agents to address the speed and leakage challenges simultaneously. The first agent constructs the qubit in-phase control pulse using a policy learned from rewards that compensate short gate times. The rewards are obtained at intermediate time steps throughout the construction of a full-length pulse, allowing the agent to explore the landscape of shorter pulses. The second agent determines an out-of-phase pulse to target leakage. Both agents are trained on real-time data from noisy hardware, thus providing model-free gate design that adapts to unpredictable hardware noise. To reduce the effect of measurement classification errors, the agents are trained directly on the readout signal from probing the qubit. We present proof-of-concept experiments by designing X and square root of X gates of various durations on IBM hardware. After just 200 training iterations, our algorithm is able to construct novel control pulses up to two times faster than the default IBM gates, while matching their performance in terms of state fidelity and leakage rate. As the length of our custom control pulses increases, they begin to outperform the default gates. Improvements to the speed and fidelity of gate operations open the way for higher circuit depth in quantum simulation, quantum chemistry and other algorithms on near-term and future quantum devices.
Mitigating CNOT Errors via Noise-aware Token Swapping

Due to the limited decoherence time of qubits in the Noisy Intermediate-Scale Quantum (NISQ) era, optimizing the size and depth of logical quantum circuits for efficient implementation on sparsely connected physical architectures is crucial. In this work, we extend the Approximate Token Swapping (ATS) algorithm by incorporating qubit priority and the size of permutation cycles to be routed. We refer to this algorithm as Priority-ATS (PATS), which also considers CNOT gate errors while constructing the routing schedule for the qubits. We provide theoretical justification for the superior effectiveness of PATS over ATS and experimentally demonstrate its ability to improve the fidelity of the output state. Furthermore, we use depolarizing error channels to model the noisy CNOT gates, where each adjacent qubit pair has a distinct error rate. By employing realistic error rates, we showcase the robust improvement in output fidelity when using PATS as opposed to a noise-oblivious ATS routing scheme.
Tackling the Qubit Mapping Problem with Permutation-Aware Synthesis

We propose a scalable, hierarchical qubit mapping and routing algorithm that harnesses the power of circuit synthesis. First, we decompose large circuits into subcircuits small enough to be directly resynthesized. For each block, we pre-synthesize them for all permutations of its input and output qubits. Following this offline step, we employ a permutation-aware, block-based generalization of the popular SABRE mapping algorithm. This mapping step stitches together blocks by choosing an input-output permutation that minimizes intrablock gate count and required inter-block communication (SWAP and bridge gates). Our approach has a twofold advantage: 1) circuit synthesis may eliminate more two-qubit gates than other optimizing compilers; 2) considering all permutations of input and output qubits eliminates communication operations transparently. In contrast, other mapping algorithms can only introduce communication operations. We show that we can produce better-quality circuits than commercial compilers: shorter by up to 68% (18% on average) fewer gates than Qiskit, up to 36% (9% on average) fewer gates than Tket. We outperform BQSkit, a permutation-unaware, synthesis-based compiler, by up to 67% (21% on average) fewer gates. We also exceed experimental optimal mappers such as OLSQ in quality (10.7% shorter circuits) and time to solution. Our scalable, heuristic approach can be seamlessly integrated into any quantum circuit compiler or optimization infrastructure, and it applies well to any qubit technology, such as superconducting and trapped ions.
Invited Paper: Dilithium Hardware-Accelerated Application Using OpenCL-Based High-Level Synthesis

Post-quantum cryptography (PQC) has been gaining attention in the last few years due to the evolution of quantum computers and the need to replace traditional, quantum-attack-insecure cryptography schemes with quantum-attack-resistant schemes. Lattice-based cryptography (LBC) constitutes a highly promising post-quantum solution (Quantum Resistant), but implementations in software or hardware are challenging due to the use of operations based on large-size polynomials. LBC selected schemes for standardization by the National Institute of Standards and Technology (NIST), rely on matrix-to-matrix multiplications of high-order polynomials, having performance bottlenecks that are solved using the Number-Theoretic Transform (NTT). One of the NIST-selected schemes for digital signature (DS) is the CRYSTALS-Dilithium scheme, which uses <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N=256$</tex> degee polynomials. In this paper a Hardware/Software (HW/SW) co-design solution is proposed for all security levels of Dilithium, utilizing the OpenCL framework and Vitis High-Level Synthesis (HLS) tool. In our work, the HW/SW co-design OpenCL mechanisms are analyzed extensively and communication overheads between the hardware kernel and an ARM processor are identified, while appropriate techniques are proposed in order to bypass the I/O time-bottleneck on a real-world application. The proposed implementation runs on the ARM Processing System (PS) of a Xilinx Multi-Processor System on Chip (MPSoC) system, utilizing the MPSoC FPGA Programmable Logic (PL) in order to accelerate the calculations relative to the heavy matrix-multiplication operation. Finally, the proposed HW/SW codesigned solution is realized as a real-world Linux-based Dilithium DS executable and manages to achieve realistic performance gain, in terms of time execution, versus a CPU-only execution ranging from 2-23% (depending on the utilized CPU Clock Frequency).
MITA: Multi-Input Adaptive Activation Function for Accurate Binary Neural Network Hardware

Binary Neural Networks (BNN) have binarized neuron and connection values so that their accelerators can be realized by extremely efficient hardware. However, there is a significant accuracy gap between BNNs and networks with wider bit-width. Conventional BNNs binarize feature maps by static globally-unified thresholds, which makes the produced bipolar image lose local details. This paper proposes a multi-input activation function to enable adaptive thresholding for binarizing feature maps: (a) At the algorithm level, instead of operating each input pixel independently, adaptive thresholding dynamically changes the threshold according to surrounding pixels of the target pixel. When optimizing weights, adaptive thresholding is equivalent to an accompanied depth-wise convolution between normal convolution and binarization. Accompanied weights in the depth-wise filters are ternarized and optimized end-to-end. (b) At the hardware level, adaptive thresholding is realized through a multi-input activation function, which is compatible with common accelerator architectures. Compact activation hardware with only one extra accumulator is devised. By equipping the proposed method on FPGA, 4.1% accuracy improvement is achieved on the original BNN with only 1.1% extra LUT resource. Compared with State-of-the-art methods, the proposed idea further increases network accuracy by 0.8% on the Cifar-10 dataset and 0.4% on the ImageNet dataset.
A Nonlinear Regression for Characterizing Two-Qubit Processors

We articulate a nonlinear regression to characterize the input state and measurement parameters of two-qubit computing systems. We represent state and (one-qubit product) measurement parameters and their constraints as products of Pauli and identity matrices. We apply our nonlinear regression on in-house simulated data. Our simulation models (i) nonideal state initialization, (ii) state dephasing and relaxation errors that occur during initialization, gate application, and measurement, (iii) gate control errors, and (iv) nonideal measurement. Our numerical regression returns physical, nontrivial state and measurement parameterizations with, respectively, high fidelity and small operator distance in our test cases. We are still experimenting with and refining this method and. here. present early findings.
Improved YOLOv5 Object Detection Algorithm for Remote Sensing Images

Remote sensing image target detection using deep neural network has emerged as a significant area of research in recent years. However, in the context of remote sensing images, it is common to encounter complex image content, arbitrary orientations of targets, dense arrangement of targets, and small targets. Current object detectors are susceptible to both false detections and missed detections in complex scenes. To improve the recognition accuracy, we propose an enhanced object detection model based on YOLOv5. Firstly, we utilize a new CNN component called SPDConv to replace strided convolution layers and pooling layers. Secondly, in order to improve the performance of the feature pyramid network and improve the ability of data feature fusion, we propose a new method called CrossFPN. Finally, we introduce the attention mechanism CBAM to improve the accuracy of model recognition. The results, using the mAP50-95 metric, demonstrate that the enhanced YOLOv5 model achieves an average accuracy of 62.S% on the DIOR datasets, which is S% higher than the traditional model. Therefore, the advanced YOLOv5 model is more effective in detecting targets in remote sensing images.
Space-Efficient Embedding of the Clique Cover Problem for Quantum Optimization

Current quantum computer prototypes exhibit various strengths and weaknesses based on their individual architectures. Consequently, the development of flexible circuit design approaches becomes a critical tool. To tackle this challenge in the case of clique cover optimization, we propose a novel embedding method of this problem, efficiently reducing the width of the required quantum circuit exponentially in the number of cliques. We compare this new embedding based on a Polynomial Unconstrained Binary Optimization reformulation to the traditional one-hot encoded quadratic problem and present numerical simulations of the Quantum Approximate Optimization Algorithm for both methods, studying in particular the solution quality. The results obtained from the numerical experiments provide valuable insights into the performance gains achieved by employing our space-efficient embedding method, verified through both analytical and stochastic simulation benchmarks. Our results highlight the effectiveness of the space-efficient embedding method for the clique cover problem and extend the set of problems formulated with this technique.
Dispatching Shots Among Multiple Quantum Computers: An Architectural Proposal

Quantum Computing is continuously evolving and expanding. As time goes by, more and more Quantum Computer implementations become available, each of them with their own features. In such a scenario, it can be difficult for developers to identify which Quantum Computer is the most suitable for their needs. In this paper, different from current works presenting strategies to select only one Quantum Computer, we propose a change of perspective. Indeed, due to the probabilistic nature of Quantum Mechanics, performing a computation in a Quantum Computer usually requires iterating the same execution many times (called shots), to eventually end with a distribution of the final results. Leveraging this need, our architecture enables selecting many Quantum Computers for the same circuit and spreading the shots among them. Such a mechanism offers also the possibility for developers to access the partial distributions obtained from the output of a subset of the selected computers. Finally, our architecture proposes to decouple the decision process from the actual execution of such a decision, by enabling developers to encode their specific custom policies.
Multimodal multi-label text classification based on bidirectional Transformer

Research on multi-label classification has developed from traditional single-modal analysis to complex classification using multiple modalities. However, single-modal input models are insufficient for effectively analyzing the increasingly rich multi-modal data on the Internet. Therefore, a ResNeXt-Bert method is proposed: introducing models for image and text information input to overcome the limitations of single-modal input data. The ResNeXt is used to extract features of images, and contextual embedding is input using early fusion method, and then fed into a pre-trained multi-layer bidirectional Transformer encoder model for classification. On the MM-IMDB dataset for studying multi-label classification problems, the micro F1 value reached 64.7%, which is better than other models. The experimental results show that introducing image and text information can indeed improve the multi-label text classification ability of the model.
QuCS: A Lecture Series on Quantum Computer Software and System

In this era of incessant advancements in quantum computing, bridging the gap between quantum algorithms' hardware requisites and available devices has become crucial. A prime focus in this context is the Software and System Level support for quantum computers, which has shown promising potential in significantly decreasing this gap. However, a noteworthy deficit of quantum software and system level-focused courses has been observed in academia worldwide. Addressing this deficiency, this paper proposes the Quantum Computer Systems (QuCS) Lecture Series. The QuCS Lecture Series aims to enhance the visibility of quantum computing software and system level and foster diverse participation in quantum computing research across multiple universities worldwide. It is envisioned as an inclusive platform to bring together individuals of diverse backgrounds, catalyzing cross-cultural collaboration and innovation in this burgeoning field. The lecture series begins with an introductory session elucidating the core concepts and fundamentals of quantum computing. This foundational knowledge will be built upon in subsequent sessions, highlighting cutting-edge research trends and recent findings in quantum software and system level. This paper provides a comprehensive overview of the QuCS Lecture Series, detailing the format, the gamut of topics to be covered, and their significance. It emphasizes the potential impact of the series on accelerating progress towards quantum supremacy and fostering a diverse, global community of quantum computing researchers and practitioners. The QuCS Lecture Series has already hosted nearly 40 lectures with over 40 confirmed speakers from more than eight different countries and from both academia and industry, QuCS also attracted more than 1000 subscribers from all over the world.
Rapid-INR: Storage Efficient CPU-Free DNN Training Using Implicit Neural Representation

Implicit Neural Representation (INR) is an innovative approach for representing complex shapes or objects without explicitly defining their geometry or surface structure. Instead, INR represents objects as continuous functions. Previous research has demonstrated the effectiveness of using neural networks as INR for image compression, showcasing comparable performance to traditional methods such as JPEG. However, INR holds potential for various applications beyond image compression. This paper introduces Rapid-INR, a novel approach that utilizes INR for encoding and compressing images, thereby accelerating neural network training in computer vision tasks. Our methodology involves storing the whole dataset directly in INR format on a GPU, mitigating the significant data communication overhead between the CPU and GPU during training. Additionally, the decoding process from INR to RGB format is highly parallelized and executed on-the-fly. To further enhance compression, we propose iterative and dynamic pruning, as well as layer-wise quantization, building upon previous work. We evaluate our framework on the image classification task, utilizing the ResNet-18 backbone network and three commonly used datasets with varying image sizes. Rapid-INR reduces memory consumption to only 5% of the original dataset size and achieves a maximum 6× speedup over the PyTorch training pipeline, as well as a maximum 1.2× speedup over the DALI training pipeline, with only a marginal decrease in accuracy. Importantly, Rapid-INR can be readily applied to other computer vision tasks and backbone networks with reasonable engineering efforts. Our implementation code is publicly available at https://github.com/sharc-lab/Rapid-INR.