Reviewerly/exHarmony huggingface dataset:
A Method to Improve Serendipity of Recommendation Lists Based on Collaborative Metric Learning

Collaborative Metric Learning (CML) is a recommendation model based on an embedded representation trained by implicit data, i.e., the behavioral histories of clicking and browsing. CML learns a metric space to embed users and items considering not only the relationship between users and items, but also item–item similarity and user–user similarity. Moreover, CML can recommend items close to each user in the trained space that match the preferences of the user. However, CML tends to be influenced strongly by popular items among many users, and the accuracy of embedded representations of other minor items is often neglected. It is necessary to learn the embedded representations of minor items that match user preferences with higher accuracy to provide unexpected recommendations of items that users may not be aware of in advance. In this article, a method is proposed to learn the embedded representations that capture user preferences by weighting loss functions according to the number of observations of implicit data and to make unexpected and effective recommendations, including minor items. Finally, the proposed method is applied to an actual movie evaluation dataset, and the usefulness of the proposed method in making unexpected recommendations based on user preferences is demonstrated.
Streaming Quantum Gate Set Tomography Using the Extended Kalman Filter

Closed-loop control algorithms for real-time calibration of quantum processors require efficient filters that can estimate physical error parameters based on streams of measured quantum circuit outcomes. Development of such filters is complicated by the highly nonlinear relationship relationship between observed circuit outcomes and the magnitudes of elementary errors. In this work, we apply the extended Kalman filter to data from quantum gate set tomography to provide a streaming estimator of the both the system error model and its uncertainties. Our numerical examples indicate extended Kalman filtering can achieve similar performance to maximum likelihood estimation, but with dramatically lower computational cost. With our method, a standard laptop can process one- and two-qubit circuit outcomes and update gate set error model at rates comparable with current experimental execution.
Modeling of Errors in Quantum Computers with Generated Structural Circuits

Errors is one major challenge in quantum computing. Presently, general error correction cannot be deployed practically. Other ways to reduce error rates require a precise knowledge about errors. This information is for example used in tooling to improve overall circuit error rates. Doing so can increase the sizes of practical circuits on current systems and therefore allow for larger hybrid high performance computing and quantum computing applications. Calibrations of quantum hardware are used to reduce error rates and report error rates. Because calibrations take a significant amount of time they are typically only run once per day. The error rates found are only valid during calibration as error rates drift, can change rapidly and by a factor of 2–3. In this paper, we show how to use experiments to precisely calculate estimates of error rates for quantum hardware. We show that by running generated structural test circuits, we can model error rates which are closer to the actual hardware error rates than the older calibration data. The produced estimates are vital inputs to several parts of a combined high performance computing and quantum computing system.
Reinforcement Learning for Gate Synthesis in Noisy Quantum Systems

We present a gate synthesis application of the Deep Deterministic Policy Gradient (DDPG) algorithm for continuous control in a noisy quantum environment. Gate synthesis plays a crucial role in quantum computing, and optimizing control strategies for gate synthesis is of significant interest. The proposed approach leverages a DNN-based actor-critic architecture to optimize gate synthesis performance. The effectiveness of the algorithm is evaluated through simulations in a noiseless and noisy quantum environment, demonstrating its ability to achieve high-fidelity gate synthesis, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(10^{-3})$</tex> , despite the presence of Markovian damping terms. The results highlight the potential of DDPG and reinforcement learning techniques for addressing continuous control challenges in quantum computing applications.
Moodle Plugin for Real-time Face Recognition in Online Exams for Systems Engineering and Computer Science Students at Universidad Continental, Huancayo, Peru.

Facial recognition systems on learning platforms like Moodle enable fast and efficient evaluation, automating the detection of impersonation in online exams. This project implements a Moodle plugin that performs facial recognition on the student attempting to take a quiz. It consists of two parts for authentication through a webcam: the first part occurs before accessing the quiz, and the second part involves surveillance through a camera during the quiz. The plugin was developed using the PHP and JavaScript programming languages, as well as Face-api. The JavaScript API detects and recognizes faces in the web browser using Tiny Yolo v2 and FaceRecognizerNet. It compares a student’s photo or image with the image from their webcam while they are taking the quiz. The Moodle plugin achieved an average error probability of 39.39% for face detection similarity distance.
SAM: A Scalable Accelerator for Number Theoretic Transform Using Multi-Dimensional Decomposition

With an increasing focus on data security in today's computer systems, homomorphic encryption and zero-knowledge proofs are becoming widely used tools in privacy-preserving computing. Number theoretic transform (NTT) is a key primitive that dominates the performance of these algorithms, and thus becomes an attractive target for domain-specific acceleration. Prior NTT accelerators mostly support only fixed and small NTT sizes, which are insufficient for the diverse parameter requirements of different cryptographic algorithms and applications. In this paper, we propose an FPGA-based, scalable NTT accelerator that uses multi-dimensional decomposition to efficiently support various NTT sizes. The hardware uses a limited and fixed amount of compute and storage resources on-chip. An arbitrary-sized NTT task is decomposed into fixed-sized small NTT kernels that match the on-chip hardware and thus execute with high efficiency. We further incorporate techniques to optimize both off-chip and on-chip data transfers under such complicated decomposed execution. Overall, our accelerator balances between on-chip compute throughput and off-chip memory bandwidth. It can flexibly scale to very large NTT tasks, and outperforms prior FPGA-based NTT accelerators by over 2× at these large sizes.
Compiling a Simple Chemistry Application to Quantum Error Correction Primitives

Many exciting results have been seen in the field of quantum error correction, including initial demonstrations on real hardware and resource estimates for real-world applications. In this work, we bridge the gap between these two developments by providing a detailed compilation of the quantum phase estimation circuit for a simple chemistry application to the rotated planar code. We find that implementing even a simple chemistry circuit requires 900 qubits and 2,300 quantum error correction rounds, emphasising the need for improved error correction techniques specifically targeting the early fault-tolerant regime. Preprint available at [arXiv: 2307.03233].
Towards Efficient Automatic Oracle Synthesis and Resource Estimation Using QDK and QIR

Automatic oracle synthesis (AOS) provides a promising route to speed up the process of constructing a complex quantum oracle. Here, we expand the existing AOS in the Microsoft Quantum Development Kit (QDK) by adding new functionalities and introducing a new workflow. We also optimize the AOS process by reducing the number of T gates using measurement-based uncomputation. Furthermore, we compare the resource requirements of the oracles generated using AOS to those of the existing QDK library oracles using the Azure Quantum Resource Estimator. The results suggest that the oracles generated using AOS perform better in runtime but use more qubits compared to the corresponding QDK library oracle. With the presented workflow, one can easily implement and estimate the quantum resources required for oracles with complex arithmetic functions. Finally, we present a strategy that can further reduce the number of physical qubits needed in AOS.
A Hybrid Classical-Quantum HPC Workload

A strategy for the orchestration of hybrid classical-quantum workloads on supercomputers featuring quantum devices is proposed. The method makes use of heterogeneous job launches with Slurm to interleave classical and quantum computation, thereby reducing idle time of the quantum components. To better understand the possible shortcomings and bottlenecks of such a workload, an example application is investigated that offloads parts of the computation to a quantum device. It executes on a classical high performance computing (HPC) system, with a server mimicking the quantum device, within the multiple program, multiple data (MPMD) paradigm in Slurm. Quantum circuits are synthesized by means of the Classiq software suite according to the needs of the scientific application, and the Qiskit Aer circuit simulator computes the state vectors. The Harrow-Hassidim-Lloyd (HHL) quantum algorithm for linear systems of equations is used to solve the algebraic problem from the discretization of a linear differential equation. Communication takes place over the message passing interface (MPI), which is broadly employed in the HPC community. Extraction of state vectors and circuit synthesis are the most time consuming, while communication is negligible in this setup. The present test bed serves as a basis for more advanced hybrid workloads eventually involving a real quantum device.
Complement Grover's Search Algorithm: An Amplitude Suppression Implementation

Grover's search algorithm was a groundbreaking advancement in quantum algorithms, providing a quadratic speedup of querying for items. Since the creation of this algorithm it has been utilized in various ways, including in preparing specific states for a general circuit. However, as the number of desired items increases, so does the gate complexity of the sub-process that conducts the query. To counter this complexity, an extension of Grover's search algorithm is derived where the focus of the query is on the undesirable items in order to suppress the amplitude of the queried items. To display the efficacy of this extension, the algorithm is implemented as a sub-process into QAOA and applied to a traveling salesman problem. For a basis of comparison, the results are compared against QAOA.
Quantum Negative Sampling Strategy for Knowledge Graph Embedding with Variational Circuit

Knowledge graph is a collection of facts, known as triples(head, relation, tail), which are represented in form of a network, where nodes are entities and edges are relations among the respective head and tail entities. Embedding of knowledge graph for facilitating downstream tasks such as knowledge graph completion, link prediction, recommendation, has been a major area of research recently in classical machine learning. Because the size of knowledge graphs are becoming larger, one of the natural choices is to exploit quantum computing for knowledge graph embedding. Recently, a hybrid quantum classical model for knowledge graph embedding has been studied in which a variational quantum circuit is trained. One of the important aspects in knowledge graph embedding is the sampling of negative triples, which plays a crucial role in efficient training of the model. In classical machine learning various negative sampling strategies have been studied. In quantum knowledge graph embedding model, although we can use these strategies in principle, it is natural to ask if we can exploit quantum advantage in negative sampling. In this article we study such a negative sampling strategy, which exploits quantum superposition, and evaluate the model's performance with a knowledge graph database.
Towards Quantum Software Requirements Engineering

Quantum software engineering (QSE) is receiving increasing attention, as evidenced by increasing publications on topics, e.g., quantum software modeling, testing, and debugging. However, in the literature, quantum software requirements engineering (QSRE) is still a software engineering area that is relatively less investigated. To this end, in this paper, we provide an initial set of thoughts about how requirements engineering for quantum software might differ from that for classical software after making an effort to map classical requirements classifications (e.g., functional and extra-functional requirements) into the context of quantum software. Moreover, we provide discussions on various aspects of QSRE that deserve attention from the Quantum software engineering community.
Zero Noise Extrapolation on Logical Qubits by Scaling the Error Correction Code Distance

In this work, we migrate the quantum error mitigation technique of Zero-Noise Extrapolation (ZNE) to fault-tolerant quantum computing. We employ ZNE on logically encoded qubits rather than physical qubits. This approach will be useful in a regime where quantum error correction (QEC) is implementable but the number of qubits available for QEC is limited. Apart from illustrating the utility of a traditional ZNE approach (circuit-level unitary folding) for the QEC regime, we propose a novel noise scaling ZNE method specifically tailored to QEC: distance scaled ZNE (DS-ZNE). DS-ZNE scales the distance of the error correction code, and thereby the resulting logical error rate, and utilizes this code distance as the scaling ‘knob’ for ZNE. Logical qubit error rates are scaled until the maximum achievable code distance for a fixed number of physical qubits, and lower error rates (i.e., effectively higher code distances) are achieved via extrapolation techniques migrated from traditional ZNE. Furthermore, to maximize physical qubit utilization over the ZNE experiments, logical executions at code distances lower than the maximum allowed by the physical qubits on the quantum device are executed in parallel across the device, thereby reducing overall circuit execution costs. We validate our proposal with numerical simulation for the surface code and confirm that ZNE lowers the logical error rates and increases the effective code distance beyond the physical capability of the quantum device. For instance, at a physical code distance of 11, the DS-ZNE effective code distance is 17, and at a physical code distance of 13, the DS-ZNE effective code distance is 21. When the proposed technique is compared against unitary folding ZNE under the constraint of a fixed number of executions of the quantum device, DS-ZNE outperforms unitary folding by up to 92% in terms of the post-ZNE logical error rate.
Divide and Conquer for Combinatorial Optimization and Distributed Quantum Computation

Scaling the size of monolithic quantum computer systems is a difficult task. As the number of qubits within a device increases, a number of factors contribute to decreases in yield and performance. To meet this challenge, distributed architectures composed of many networked quantum computers have been proposed as a viable path to scalability. Such systems will need algorithms and compilers that are tailored to their distributed architectures. In this work we introduce the Quantum Divide and Conquer Algorithm (QDCA), a hybrid variational approach to mapping large combinatorial optimization problems onto distributed quantum architectures. This is achieved through the combined use of graph partitioning and quantum circuit cutting. The QDCA, an example of application-compiler co-design, alters the structure of the variational ansatz to tame the exponential compilation overhead incurred by quantum circuit cutting. The result of this cross-layer co-design is a highly flexible algorithm which can be tuned to the amount of classical or quantum computational resources that are available, and can be applied to both near- and long-term distributed quantum ar-chitectures. We simulate the QDCA on instances of the Maximum Independent Set problem and find that it is able to outperform similar classical algorithms. We also evaluate an 8-qubit QDCA ansatz on a superconducting quantum computer and show that circuit cutting can help to mitigate the effects of noise. Our work demonstrates how many small-scale quantum computers canwork together to solve problems 85 % larger than their own qubit count, motivating the development and potential of large-scale distributed quantum computing.
Automatic Kernel Generation for Large Language Models on Deep Learning Accelerators

Large language model (LLM) is a promising trend to sustain accuracy growth with billions of parameters for various application domains. Deep learning (DL) accelerators that are typically designed in spatial architecture are potential platforms to handle the substantial computational demands of LLMs. Exploiting DL accelerators for LLMs require high-performance kernels, which are manually optimized or automatically generated. While prior automatic works reduce development costs, they fail to find optimal or near-optimal kernels. This is because the kernel design space is large containing many invalid kernels, and non-convex containing many local minima. In this paper, we propose an automatic kernel generation framework for large language models on deep learning accelerators. The key idea is the reinforcement learning (RL) formulation to generate a kernel with multi-step decision-making. We first develop a high-quality action space to satisfy architectural constraints of accelerator. Then, we provide a practical RL implementation by devising a policy network with Transformer and variance reduction techniques for gradients. Experimental results show our framework achieves average 3.5× speedup on TensorCore compared with exploration-based Ansor; 2.6× speedup on Simba compared with solver-based CoSA. Also, our framework achieves better energy efficiency compared to the state-of-the-art works.
Towards the Quantum Internet: Entanglement Rate Analysis of High-Efficiency Electro-Optic Transducer

Coherent microwave-optical photon conversion is a key enabling technology for the Quantum Internet, since it allows entanglement distribution among superconducting-based network nodes. A high-efficiency electro-optic transducer based on three-dimensional superconducting radio-frequency (SRF) technology has recently been proposed at Fermilab. In this paper, we evaluate the impact of Fermilab's transduction hardware on the entanglement generation probability and the entanglement rate, which are two critical metrics from a communication engineering perspective. Furthermore, we provide some guidelines to increase the direct entanglement rate, i.e., the rate before any entanglement purification process. Our analysis shows that, through high-efficiency transduction, it is possible to reduce the network overhead. This preliminary work paves the way toward distantly interconnected superconducting quantum processors.
A Holistic Approach to Quantum Ethics Education: The Quantum Ethics Project quantumethicsproject.org

This paper first provides an overview of the growing subfield of quantum ethics, including a working definition; research to date into social, economic, and political implications of quantum technologies; and directions for future research. Second, it introduces the Quantum Ethics Project (QEP), its activities to date, and its organizing philosophy. The third section reports on QEP's ongoing curriculum development work, i.e. creating one of the first full-length courses on Ethics and Social Impacts of Quantum Technology. We outline the pedagogical approach being taken in the course design, including key learning outcomes, topic areas, teaching methods, and rationale. Finally, we discuss current limitations and future areas of attention, such as drawbacks to teaching ethical reasoning and ideas for assessment and implementation.
Testing Coreference Resolution Systems without Labeled Test Sets

Coreference resolution (CR) is a task to resolve different expressions (e.g., named entities, pronouns) that refer to the same real-world en- tity/event. It is a core natural language processing (NLP) component that underlies and empowers major downstream NLP applications such as machine translation, chatbots, and question-answering. De- spite its broad impact, the problem of testing CR systems has rarely been studied. A major difficulty is the shortage of a labeled dataset for testing. While it is possible to feed arbitrary sentences as test inputs to a CR system, a test oracle that captures their expected test outputs (coreference relations) is hard to define automatically. To address the challenge, we propose Crest, an automated testing methodology for CR systems. Crest uses constituency and depen- dency relations to construct pairs of test inputs subject to the same coreference. These relations can be leveraged to define the meta- morphic relation for metamorphic testing. We compare Crest with five state-of-the-art test generation baselines on two popular CR systems, and apply them to generate tests from 1,000 sentences randomly sampled from CoNLL-2012, a popular dataset for corefer- ence resolution. Experimental results show that Crest outperforms baselines significantly. The issues reported by Crest are all true positives (i.e., 100% precision), compared with 63% to 75% achieved by the baselines.
Towards Feature-Based Analysis of the Machine Learning Development Lifecycle

The safety and trustworthiness of systems with components that are based on Machine Learning (ML) require an in-depth understanding and analysis of all stages in its Development Lifecycle (MLDL). High-level abstractions of desired functionalities, model behaviour, and data are called features, and they have been studied by different communities across all MLDL stages. In this paper, we propose to support Software Engineering analysis of the MLDL through features, calling it feature-based analysis of the MLDL. First, to achieve a shared understanding of features among different experts, we establish a taxonomy of existing feature definitions currently used in various MLDL stages. Through this taxonomy, we map features from different stages to each other, discover gaps and future research directions and identify areas of collaboration between Software Engineering and other MLDL experts.
Understanding the Bug Characteristics and Fix Strategies of Federated Learning Systems

Federated learning (FL) is an emerging machine learning paradigm that aims to address the problem of isolated data islands. To preserve privacy, FL allows machine learning models and deep neural networks to be trained from decentralized data kept privately at individual devices. FL has been increasingly adopted in missioncritical fields such as finance and healthcare. However, bugs in FL systems are inevitable and may result in catastrophic consequences such as financial loss, inappropriate medical decision, and violation of data privacy ordinance. While many recent studies were conducted to understand the bugs in machine learning systems, there is no existing study to characterize the bugs arising from the unique nature of FL systems. To fill the gap, we collected 395 real bugs from six popular FL frameworks (Tensorflow Federated, PySyft, FATE, Flower, PaddleFL, and Fedlearner) in GitHub and StackOverflow, and then manually analyzed their symptoms and impacts, prone stages, root causes, and fix strategies. Furthermore, we report a series of findings and actionable implications that can potentially facilitate the detection of FL bugs.