Search Results for author:
Found 4 papers, 0 papers with code
Beyond the density operator and Tr(ρA): Exploiting the higher-order statistics of random-coefficient pure states for quantum information processing
We succeed in solving this problem by exploiting a fourth-order statistical parameter of state coefficients, in addition to second-order statistics.
Single-preparation unsupervised quantum machine learning: concepts and applications
Our methods are especially useful in a quantum computer, that we propose to more briefly call a "quamputer": BQPT and BHPE simplify the characterization of the gates of quamputers; BQSS and BQSR allow one to design quantum gates that may be used to compensate for the non-idealities that alter states stored in quantum registers, and they open the way to the much more general concept of self-adaptive quantum gates (see longer version of abstract in paper).
Quantum process tomography with unknown single-preparation input states
On the other hand, usual QPT methods require one to be able to prepare many copies of the same (known) input state, which is constraining.
Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability
Unconstrained Pixel-by-pixel NMF (UP-NMF) is a new blind source separation method based on the assumption of a linear mixing model, which can deal with intra-class variability.
