Transportation electrification plays a crucial role in mitigating greenhouse gas (GHG)
emissions and enabling the decarbonization of power systems. However, current research …

Electric vehicles have many opportunities to participate in auxiliary operations as research
interest in renewables and green energy sources grows. This paper discusses novel energy …

Using a quantum processor to embed and process classical data enables the generation of
correlations between variables that are inefficient to represent through classical …

It is expected that quantum computers would enable solving various problems that are
beyond the capabilities of the most powerful current supercomputers, which are based on …

Image recognition is one of the primary applications of machine learning algorithms.
Nevertheless, machine learning models used in modern image recognition systems consist …

Existing protocols for benchmarking current quantum coprocessors fail to meet the usual
standards for assessing the performance of high-performance-computing platforms. After a …

A frequent starting point of quantum computation platforms is the two-state quantum system,
ie, the qubit. However, in the context of integer optimization problems, relevant to scheduling …

Quantum annealing is a quantum computing approach widely used for optimization and
probabilistic sampling problems. It is an alternative approach designed due to the limitations …

Climate change presents an existential threat to human societies and the Earth's
ecosystems more generally. Mitigation strategies naturally require solving a wide range of …

We study the correlation clustering problem using the quantum approximate optimization
algorithm (QAOA) and qudits, which constitute a natural platform for such non-binary …