Top-quark mass measurements: review and perspectives

The top quark is the heaviest elementary particle known and its mass ($m_{\rm top}$) is a fundamental parameter of the Standard Model (SM). The $m_{\rm top}$ value affects theory predictions of particle production cross-sections required for exploring Higgs-boson properties and searching for New Physics (NP). Its precise determination is essential for testing the overall consistency of the SM, to constrain NP models, through precision electroweak fits, and has an extraordinary impact on the Higgs sector, and on the SM extrapolation to high-energies. The methodologies, the results, and the main theoretical and experimental challenges related to the $m_{\rm top}$ measurements and combinations at the Large Hadron Collider (LHC) and at the Tevatron are reviewed and discussed. Finally, the prospects for the improvement of the $m_{\rm top}$ precision during the upcoming LHC runs are briefly outlined.Comment: 18 pages, 2 figures, Preprint submitted to Reviews in Physics (REVIP

Bridging the Gap to Next Generation Power System Planning and Operation with Quantum Computation

Innovative solutions and developments are being inspected to tackle rising electrical power demand to be supplied by clean forms of energy. The integration of renewable energy generations, varying nature loads, importance of active role of distribution system and consumer participation in grid operation has changed the landscape of classical power grids. Implementation of smarter applications to plan, monitor, operate the grid safely are deemed paramount for efficient, secure and reliable functioning of grid. Although sophisticated computations to process gigantic volume of data to produce useful information in a time critical manner is the paradigm of future grid operations, it brings along the burden of computational complexity. Advancements in quantum technologies holds promising solution for dealing with demanding computational complexity of power system related applications. In this article, we lay out clear motivations for seeking quantum solutions for solving computational burden challenges associated with power system applications. Next we present an overview of quantum solutions for various power system related applications available in current literature and suggest future topics for research. We further highlight challenges with existing quantum solutions for exploiting full quantum capabilities. Additionally, this paper serves as a bridge for power engineers to the quantum world by outlining essential quantum computation fundamentals for enabling smoother transition to future of power system computations

A Competitive Showcase of Quantum versus Classical Algorithms in Energy Coalition Formation

The formation of energy communities is pivotal for advancing decentralized and sustainable energy management. Within this context, Coalition Structure Generation (CSG) emerges as a promising framework. The complexity of CSG grows rapidly with the number of agents, making classical solvers impractical for even moderate sizes (number of agents>30). Therefore, the development of advanced computational methods is essential. Motivated by this challenge, this study conducts a benchmark comparing classical solvers with quantum annealing on Dwave hardware and the Quantum Approximation Optimization Algorithm (QAOA) on both simulator and IBMQ hardware to address energy community formation. Our classical solvers include Tabu search, simulated annealing, and an exact classical solver. Our findings reveal that Dwave surpasses QAOA on hardware in terms of solution quality. Remarkably, QAOA demonstrates comparable runtime scaling with Dwave, albeit with a significantly larger prefactor. Notably, Dwave exhibits competitive performance compared to the classical solvers, achieving solutions of equal quality with more favorable runtime scaling

Dynamic Price Incentivization for Carbon Emission Reduction using Quantum Optimization

Demand Side Response (DSR) is a strategy that enables consumers to actively participate in managing electricity demand. It aims to alleviate strain on the grid during high demand and promote a more balanced and efficient use of electricity resources. We implement DSR through discount scheduling, which involves offering discrete price incentives to consumers to adjust their electricity consumption patterns. Since we tailor the discounts to individual customers' consumption, the Discount Scheduling Problem (DSP) becomes a large combinatorial optimization task. Consequently, we adopt a hybrid quantum computing approach, using D-Wave's Leap Hybrid Cloud. We observe an indication that Leap performs better compared to Gurobi, a classical general-purpose optimizer, in our test setup. Furthermore, we propose a specialized decomposition algorithm for the DSP that significantly reduces the problem size, while maintaining an exceptional solution quality. We use a mix of synthetic data, generated based on real-world data, and real data to benchmark the performance of the different approaches

Quadratic quantum speedup in evaluating bilinear risk functions

Computing nonlinear functions over multilinear forms is a general problem with applications in risk analysis. For instance in the domain of energy economics, accurate and timely risk management demands for efficient simulation of millions of scenarios, largely benefiting from computational speedups. We develop a novel hybrid quantum-classical algorithm based on polynomial approximation of nonlinear functions and compare different implementation variants. We prove a quadratic quantum speedup, up to polylogarithmic factors, when forms are bilinear and approximating polynomials have second degree, if efficient loading unitaries are available for the input data sets. We also enhance the bidirectional encoding, that allows tuning the balance between circuit depth and width, proposing an improved version that can be exploited for the calculation of inner products. Lastly, we exploit the dynamic circuit capabilities, recently introduced on IBM Quantum devices, to reduce the average depth of the Quantum Hadamard Product circuit. A proof of principle is implemented and validated on IBM Quantum systems