The impossibility of undoing a mixing process is analysed in the context of quantum information theory. The optimal machine to undo the mixing process is studied in the case of pure states, focusing on qubit systems. Exploiting the symmetry of the problem we parametrise the optimal machine in such a way that the number of parameters grows polynomially in the size of the problem. This simplification makes the numerical methods feasible. For simple but non-trivial cases we computed the analytical solution, comparing the performance of the optimal machine with other protocols
We consider the problem of optimally decoding a quantum error correction code -- that is to find the...
The quantum approximate optimization algorithm/quantum alternating operator ansatz (QAOA) is a heuri...
Quantum-limited amplifiers increase the amplitude of quantum signals at the price of introducing add...
Since manipulations of qubits are constrained by the quantum mechanical rules, several classical inf...
We introduce a new decomposition of the multiqubit states of the form purification procedure. The sa...
We consider a quantum computation that only extracts one bit of information per quantum state prepar...
We address the problem of learning an unknown unitary transformation from a finite number of example...
This thesis studies the limits on the performances of inference tasks with quantum data and quantum...
Quantum machine learning studies the application of concepts and techniques originating in machine l...
Unitary transformations formulate the time evolution of quantum states. How to learn a unitary trans...
In classical control theory, tracking refers to the ability to perform measurements and feedback on ...
This thesis reflects works previously published by the author and materials hitherto unpublished on ...
We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded ...
The term "machine learning" especially refers to algorithms that derive mappings, i.e. intput/output...
In this project, quantum cloning machines are analyzed that take in N quantum systems in the same un...
We consider the problem of optimally decoding a quantum error correction code -- that is to find the...
The quantum approximate optimization algorithm/quantum alternating operator ansatz (QAOA) is a heuri...
Quantum-limited amplifiers increase the amplitude of quantum signals at the price of introducing add...
Since manipulations of qubits are constrained by the quantum mechanical rules, several classical inf...
We introduce a new decomposition of the multiqubit states of the form purification procedure. The sa...
We consider a quantum computation that only extracts one bit of information per quantum state prepar...
We address the problem of learning an unknown unitary transformation from a finite number of example...
This thesis studies the limits on the performances of inference tasks with quantum data and quantum...
Quantum machine learning studies the application of concepts and techniques originating in machine l...
Unitary transformations formulate the time evolution of quantum states. How to learn a unitary trans...
In classical control theory, tracking refers to the ability to perform measurements and feedback on ...
This thesis reflects works previously published by the author and materials hitherto unpublished on ...
We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded ...
The term "machine learning" especially refers to algorithms that derive mappings, i.e. intput/output...
In this project, quantum cloning machines are analyzed that take in N quantum systems in the same un...
We consider the problem of optimally decoding a quantum error correction code -- that is to find the...
The quantum approximate optimization algorithm/quantum alternating operator ansatz (QAOA) is a heuri...
Quantum-limited amplifiers increase the amplitude of quantum signals at the price of introducing add...