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Quantum-inspired annealers as Boltzmann generators for machine learning and statistical physics
Alexander E. Ulanov, Egor S. Tiunov, A. I. Lvovsky
Quantum simulators and processors are rapidly improving nowadays, but they are still not able to solve complex and multidimensional tasks of practical value. However, certain numer…
Experimental quantum homodyne tomography via machine learning
E. S. Tiunov, V. V. Tiunova, A. E. Ulanov +2
Complete characterization of states and processes that occur within quantum devices is crucial for understanding and testing their potential to outperform classical technologies fo…
Entangled resource for interfacing single- and dual-rail optical qubits
David Drahi, Demid V. Sychev, Khurram K. Pirov +4
Today's most widely used method of encoding quantum information in optical qubits is the dual-rail basis, often carried out through the polarisation of a single photon. On the othe…
Annealing by simulating the coherent Ising machine
Egor S. Tiunov, Alexander E. Ulanov, A. I. Lvovsky
The coherent Ising machine (CIM) enables efficient sampling of low-lying energy states of the Ising Hamiltonian with all-to-all connectivity by encoding the spins in the amplitudes…