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Alexander Lvovsky

Professor

Research theme

  • Quantum optics & ultra-cold matter

Sub department

  • Atomic and Laser Physics

Research groups

  • Quantum and optical technology
alex.lvovsky@physics.ox.ac.uk
Telephone: +44 (0)1865 272275
Clarendon Laboratory, room 512.40.26
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  • About
  • Publications

Exploratory Combinatorial Optimization with Reinforcement Learning

(2019)

Authors:

Thomas D Barrett, William R Clements, Jakob N Foerster, AI Lvovsky
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Engineering Schrödinger cat states with a photonic even-parity detector

(2019)

Authors:

GS Thekkadath, BA Bell, IA Walmsley, AI Lvovsky
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Experimental quantum homodyne tomography via machine learning

(2019)

Authors:

ES Tiunov, VV Tiunova, AE Ulanov, AI Lvovsky, AK Fedorov
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Entangled resource for interfacing single- and dual-rail optical qubits

(2019)

Authors:

David Drahi, Demid V Sychev, Khurram K Pirov, Ekaterina A Sazhina, Valeriy A Novikov, Ian A Walmsley, AI Lvovsky
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Annealing by simulating the coherent Ising machine

Optics Express Optical Society of America 27:7 (2019) 10288-10295

Authors:

Egor S Tiunov, Alexander E Ulanov, AI Lvovsky

Abstract:

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 of pulsed modes in an optical parametric oscillator (OPO). The interaction between the pulses is realized by means of measurement-based optoelectronic feedforward, which enhances the gain for lower-energy spin configurations. We present an efficient method of simulating the CIM on a classical computer that outperforms the CIM itself, as well as the noisy mean-field annealer in terms of both the quality of the samples and the computational speed. It is furthermore advantageous with respect to the CIM in that it can handle Ising Hamiltonians with arbitrary real-valued node coupling strengths. These results illuminate the nature of the faster performance exhibited by the CIM and may give rise to a new class of quantum-inspired algorithms of classical annealing that can successfully compete with existing methods.
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