The biggest challenge that quantum computing and quantum machine learning are currently
facing is the presence of noise in quantum devices. As a result, big efforts have been put into …

Central in drug design is the identification of biomolecules that uniquely and robustly bind to
a target protein, while minimizing their interactions with others. Accordingly, precise binding …

Quantum reservoir computing is strongly emerging for sequential and time series data
prediction in quantum machine learning. We make advancements to the quantum noise …

Quantum machine learning (QML) is a discipline that holds the promise of revolutionizing
data processing and problem‐solving. However, dissipation and noise arising from the …

Quantum Extreme Learning Machine (QELM) is an emerging technique that utilizes
quantum dynamics and an easy-training strategy to solve problems such as classification …

Quantum reservoir computing algorithms recently emerged as a standout approach in the
development of successful methods for the noisy intermediate-scale quantum (NISQ) era …

Quantum optical networks are instrumental in addressing the fundamental questions and
enable applications ranging from communication to computation and, more recently …

In the current quantum computing paradigm, significant focus is placed on the reduction or
mitigation of quantum decoherence. When designing new quantum processing units, the …

Here, we propose the use of the majorization-based indicator for quantum computation
complexity introduced in Vallejos et al.(Phys. Rev. A 104: 012602, 2021) as a tool to …

The analytic form of quantum discord by Radhakrishnan et al.(2020) is derived for three
qubits under a non-Markovian dephasing channel, where tripartite entanglement is explicitly …