Reviewerly/exHarmony huggingface dataset:
Solar Power Forecasting by Machine Learning Methods in a Co-located Wind and Photovoltaic Plant

Solar energy is a promising and sustainable solution for the depletion of fossil fuel reserves and the mitigation of climate change impacts. The modular, versatile, and cost-effective nature of solar power, especially photovoltaic installations, have led to rapid increase in their adoption. Accurate solar power forecasting is crucial for seamlessly integrating solar power into the electrical grid and planning energy operations for households, industries and regions. This research compares various machine learning methods for power forecasting in a co-located wind and solar power plant. The results indicate that boosting methods, particularly XGBoost and LightGBM provide high-performance RMSE and MSE results. For MAE, an LSTM and a hybrid CNN-LSTM-Attention model perform best. Additionally, the study analyzes the impact of various input sets. This paper significantly advances existing literature by conducting a comprehensive comparative analysis of a wide array of machine learning methods, assessing their efficacy across multiple feature sets.
Performance Comparison of Fixed and Dual Axis Solar Tracking Systems

Energy satisfaction has become a critical global concern in our modern era. Historically, our reliance on nonrenewable fossil fuels to meet energy needs has led to various problems, including their limited availability and harmful environmental impact. Consequently, the search for alternative energy sources has intensified, with solar energy emerging as a prominent option. To harness solar energy effectively, uniaxial and dual-axis solar tracking systems have gained widespread usage. In this study, we have developed a solar tracking system that aims to achieve optimal power efficiency. Our project specifically focuses on designing a dual-axis solar tracking system to maximize energy efficiency. A key aspect of our design is to ensure that the solar panel receives sunlight perpendicularly to its surface, thereby enabling the most efficient energy capture. To simplify the solar tracking process and reduce maintenance costs, we have incorporated a low-power microprocessor into our system. Furthermore, comparative tests have been conducted to evaluate the performance of our installed dual-axis solar panel against a fixed panel. The results clearly demonstrate that the implementation of dual-axis solar panels can significantly increase power production.
Message from the Chairs: 2023 IEEE International Conference on Quantum Computing and Engineering

It is our distinct pleasure and honor to welcome you all to the Fourth IEEE International Conference on Quantum Computing and Engineering (QCE23), also known as IEEE Quantum Week 2023. With your outstanding contributions and participation, QCE23 offers valuable opportunities to interact with experts in a full range of quantum technologies, from quantum device engineering to quantum computing and applications.
A Recursively Partitioned Approach to Architecture-Aware ZX Polynomial Synthesis and Optimization

The synthesis of quantum circuits from phase gadgets in the ZX-calculus facilitates quantum circuit optimization. Our work provides an alternative formulation for the architecture-aware synthesis algorithm of PauliOpt [13] by replacing the stochastic approach of PauliOpt with a heuristic based search and utilizes a divide and conquer method to synthesize an optimized circuit from a ZX polynomial. We provide a comparison of our algorithm with PauliOpt and other state-of-the-art optimization libraries. While we note poorer performance for highly structured circuits, as in the QAOA formulation for Max-Cut, we demonstrate a significant advantage for randomized circuits, which highlights the advantages of utilizing an architecture-aware methodology.
Comparing Quantum Service Offerings

With the emergence of quantum computing, a growing number of quantum devices is accessible via cloud offerings. However, due to the rapid development of the field, these quantum-specific service offerings vary significantly in capabilities and requirements they impose on software developers. This is particularly challenging for practitioners from outside the quantum computing domain who are interested in using these offerings as parts of their applications. In this paper, we compare several devices based on different hardware technologies and provided through different offerings, by conducting the same experiment on each of them. By documenting the lessons learned from our experiments, we aim to support developers in the usage of quantum-specific offerings and illustrate the differences between predominant quantum hardware technologies.
On Refactoring Quantum Programs in Q#

Refactoring is the process of restructuring existing computer code without changing its external behavior. Refactoring aims to improve the design, structure, and implementation of software while preserving its functionality. While classical programs have benefited from various refactoring methods, the field of quantum programming lacks dedicated refactoring techniques. This paper bridges this gap by presenting a preliminary catalog of refactorings designed for quantum programs in Q#. The paper also highlights the importance of tool support in automating the refactoring process for quantum programs.
Towards Hamiltonian Simulation with Decision Diagrams

This paper proposes a novel approach to Hamiltonian simulation using Decision Diagrams (DDs), which are an exact representation based on exploiting redundancies in representations of quantum states and operations. While the simulation of Hamiltonians has been studied extensively, scaling these simulations to larger or more complex systems is often challenging and may require approximations or new simulation methods altogether. DDs offer such an alternative that has not yet been applied to Hamiltonian simulation. In this work, we investigate the behavior of DDs for this task. To this end, we review the basics of DDs such as their construction and present how the relevant operations for Hamiltonian simulation are implemented in this data structure-leading to the first DD-based Hamiltonian simulation approach. Based on several series of evaluations and comparisons, we then discuss insights about the performance of this complementary approach. Overall, these studies show that DDs indeed may offer a promising new data structure which, for certain examples, can provide orders of magnitudes of improvement compared to the state-of-the-art, yet also comes with its own, fundamentally different, limitations.
QVis: A Visual Analytics Tool for Exploring Noise and Errors in Quantum Computing Systems

We present the preliminary design and results of QVis, a visual analytics tool for exploring quantum device performance data. QV is helps uncover temporal and multivariate variations in noise properties of quantum devices. We describe the implementations of these methods as well as applications to the analysis of a 127-qubit data set derived from the IBM washington processor over a 16-month period. Both human-interactive and semi-automated analytic methods are included to address requirements for visual exploration, thresholding, and clustering techniques. Our application of QVis to real-world scenarios demonstrates the ability to reveal noteworthy patterns in the behavior of the critical characterization metrics.
MLpronto: A tool for democratizing machine learning

The democratization of machine learning is a popular and growing movement. In a world with a wealth of publicly available data, it is important that algorithms for analysis of data are accessible and usable by everyone. We present MLpronto, a system for machine learning analysis that is designed to be easy to use so as to facilitate engagement with machine learning algorithms. With its web interface, MLpronto requires no computer programming or machine learning background, and it normally returns results in a matter of seconds. As input, MLpronto takes a file of data to be analyzed. MLpronto then executes some of the more commonly used supervised machine learning algorithms on the data and reports the results of the analyses. As part of its execution, MLpronto generates computer programming code corresponding to its machine learning analysis, which it also supplies as output. Thus, MLpronto can be used as a no-code solution for citizen data scientists with no machine learning or programming background, as an educational tool for those learning about machine learning, and as a first step for those who prefer to engage with programming code in order to facilitate rapid development of machine learning projects. MLpronto is freely available for use at https://mlpronto.org/.
An Observational Study on React Native (RN) Questions on Stack Overflow (SO)

Mobile applications are continuously increasing in prevalence. One of the main challenges in mobile application development is creating cross-platform applications. To facilitate developing cross-platform applications, the software engineering community created several solutions, one of which is React Native (RN), which is a popular cross-platform framework. The software engineering literature demonstrated the effectiveness of Stack Overflow (SO) in providing real-world perspectives on a variety of technical subjects. Therefore, this study aims to gain a better understanding of the stance of RN on SO. We identified and analyzed 131,620 SO RN-related questions. Moreover, we observed how the interest toward RN on SO evolves over time. Additionally, we utilized Latent Dirichlet Allocation (LDA) to identify RN-related topics that are discussed within the questions. Afterward, we utilized a number of proxy measures to estimate the popularity and difficulty of these topics. The results revealed that interest toward RN on SO was generally increasing. Moreover, RN-related questions revolve around six topics, with the topics of layout and navigation being the most popular and the topic of iOS issues being the most difficult. Software engineering researchers, practitioners, educators, and RN contributors may find the results of this study beneficial in guiding their future RN efforts.
Quantum Computing Simulation

Quantum computing is one of the emerging technologies with its immense capability, allowing us to solve computationally challenging problems more efficiently. Gaining a real quantum computer for running and testing algorithms is very costly. In order for developers and researchers to build and test algorithms, they can use simulators that can model the behaviour of a real quantum computer inside a classical computer. Simulating a quantum system on a classical machine is complex and requires a lot of processing power. This paper introduces some basic algorithms and methodologies we used to simulate an ideal multi-qubit simulator in classical terms
Leveraging API Specifications for Scaffolding Quantum Applications

Quantum computing is still in its early stages, and developers used to high-level tools are encountering difficulties in developing hybrid classical-quantum systems. This has created the need for the adaptation of classical tools or the development of new tools to approach the quantum world and enable more efficient development. Quantum computers are accessed through cloud providers, which has led to the use of Service-Oriented Architectures. Although there are currently tools and standards available for improving the development of classical services, they need to be adapted for quantum services. In this work, we propose the adaptation of classic tools to be used in the development of hybrid systems, extending classic specifications such as OpenAPI and AsyncAPI to offer developers a set of tools closer to the environment they are used to. The provided implementation is focused on Amazon Braket as the quantum computing service provider.
Distributed Coordinate Descent Algorithm for Variational Quantum Classification

Quantum Machine Learning (QML) is one of promising applications for near-term quantum computing. Two popular methods in QML are kernel methods and variational methods. Variational methods, which consist of parametrized quantum circuits (PQCs) to encode data and define classifiers, work faster in theory (i.e., O(N) to learn from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> training examples) than kernel methods, which use quantum circuits to compute O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) elements of kernel matrices. However, in practice when dealing with large N, it is necessary to speed up variational methods due to the slow quantum gates. In this work, we propose a parallelization of training variational quantum classifiers to utilize the availability of many quantum devices with dozens of qubits. In contrast to existing parallelization of variational methods with gradient-based algorithms, we develop a novel distributed coordinate descent algorithm to optimize parametrized gates of variational quantum circuits. There are several gradient-free methods to optimize PQCs that have been shown to converge faster. Here, by focusing on the so-called Free-axis selection (Fraxis) method, we show how the gradient-free methods can be parallelized, and demonstrate their efficacies by running the algorithm on both simulators and IBM Quantum devices. We confirm the proposed algorithm achieves high classification accuracy and gains almost-linear speedup with the degree of parallelization.
Exploring Architecture of Qiskit Runtime for Educational Enablement

A multitude of programming languages and frame-works exist for quantum computing today (Qiskit, Intel Quantum SDK, Pennylane, Cirq, Microsoft Quantum SDK, Yao.jl, etc.). Simultaneously, a given language or framework plays a vital role in a particular area of STEM education, and focuses on specialised features that can benefit specific domain(s) of computing & science. Accessing quantum computing compute resources from a software framework requires the development of integrations for every hardware platform, which becomes a challenge in the long run. The Qiskit Runtime environment is composed of Primitives: Sampler/Estimator, Sessions, and a rich set of Error mitigation techniques and compiler optimization methods. In this paper, we show how the recent architecture of Qiskit Runtime environment, now accepting QASM strings into REST API calls, starts to bridge the gap between programming languages & quantum computing frameworks. More specifically, we highlight the students' needs of interfacing between languages & frameworks, along with the needed development for an individual to generate accepted inputs which complete a quantum program.
Quantum Steering of Surface Error Correcting Codes

Surface codes provide a promising path towards large-scale fault-tolerant quantum computers. However, outside the difficulty in engineering qubits, their theoretical realization is hindered by a number of technical implementation details, including the initialization of an encoded quantum state on contemporary quantum computers. We propose a solution to overcome these challenges by utilizing recent theoretical developments in measurement-induced quantum steering. An encoded quantum state is prepared by repeatedly performing the following steps: (1) entangling qubits via a specifically chosen operation, (2) performing measurement on some of the qubits, and (3) resetting the measured qubits' states. We demonstrate our results using numerical simulations of surface codes, noting convergence of state fidelity, and commenting on choices for parameter selection.
A Uniform Representation of Classical and Quantum Source Code for Static Code Analysis

The emergence of quantum computing raises the question of how to identify (security-relevant) programming errors during development. However, current static code analysis tools fail to model information specific to quantum computing. In this paper, we identify this information and propose to extend classical code analysis tools accordingly. Among such tools, we identify the Code Property Graph to be very well suited for this task as it can be easily extended with quantum computing specific information. For our proof of concept, we implemented a tool which includes information from the quantum world in the graph and demonstrate its ability to analyze source code written in Qiskit and OpenQASM. Our tool brings together the information from the classical and quantum world, enabling analysis across both domains. By combining all relevant information into a single detailed analysis, this powerful tool can facilitate tackling future quantum source code analysis challenges.
Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.
Scaling Limits of Quantum Repeater Networks

Quantum networks (QNs) are a promising platform for secure communications, enhanced sensing, and efficient distributed quantum computing. However, due to the fragile nature of quantum states, these networks face significant challenges in terms of scalability. In this paper, the scaling limits of quantum repeater networks (QRNs) are analyzed. The goal of this work is to maximize the overall length, or scalability of QRNs such that long-distance quantum communications is achieved while application-specific quality-of-service ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> oS) requirements are satisfied. In particular, a novel joint optimization framework that aims at maximizing QRN scalability, while satisfying <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> oS constraints on the end-to-end fidelity and rate is proposed. The proposed approach optimizes the number of QRN repeater nodes, their separation distance, and the number of distillation rounds to be performed at both link and end-to-end levels. Extensive simulations are conducted to analyze the tradeoffs between QRN scalability, rate, and fidelity under gate and measurement errors. The obtained results characterize the QRN scaling limits for a given QoS requirement. The proposed approach offers a promising solution and design guidelines for future QRN deployments.
A Routing Framework for Quantum Entanglements with Heterogeneous Duration

Entanglement routing is a fundamental problem in the network layer of quantum networks, which has become an increasingly popular research topic. This paper addresses a common problem of existing entanglement routing algorithms: they assume the Synchronized Single-time-slot model (SynSts) that requires all entanglement generation and swapping to be completed in a single time slot. This model does not align with future large-scale quantum networks that will incrementally deploy heterogeneous devices: old ones generate short-duration entanglements while advanced devices can generate long-duration entanglements. Forcing long-duration entanglements to finish in one time slot will result in sub-optimal routing performance. This paper presents a new routing framework for quantum entanglements with heterogeneous duration, including a Synchronous Multi-time-slots (SynMts) routing model and two algorithms to manage routing requests and link states. The framework is designed in such a generalized way that all existing algorithms designed for the SynSts model can still run with the SynMts model while achieving higher performance. We conduct experiments using three recently-proposed routing algorithms and find they all achieve evident throughput improvement in the new framework.
A Reference Implementation for a Quantum Message Passing Interface

Quantum computers today are noisy and limited in the number of qubits that can be set with reasonable fidelities and gate times for each system. Since the current limit on the number of qubits is difficult to surpass, it is expected that the future of quantum computing will lie in connecting different quantum computers together to form a quantum network, each with its own limitations. Herein lies the field of distributed quantum computing optimized for large-scale communication. One such system that has been designed is the Quantum Message Passing Interface (QMPI). This interface, unlike the classical MPI, takes advantage of quantum teleportation to communicate between different quantum nodes but leverages the framework built for classical MPI. This general interface is essential as we try to connect various quantum computers, as it can be used to develop distributed quantum algorithms across various nodes. In this work, we develop a reference implementation for a QMPI interface in Qiskit and characterize its performance. In our interface, we have implemented point-to-point and optimized collective operations for larger distributed quantum networks. To test our framework, we demonstrate our implementation with applications that can leverage the QMPI framework including Trotterization of the transverse field Ising model and distributive quantum phase estimation. Lastly, we have developed a new technique for decomposing quantum gates across many nodes which leverages multi-controlled Toffolli gates. This technique beats the state of the art for an adder circuit in terms of fidelity and the number of EPR pairs used.