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

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