Coherent Ising machines -- Quantum optics and neural network
perspectives
- URL: http://arxiv.org/abs/2006.05649v2
- Date: Fri, 25 Sep 2020 04:37:20 GMT
- Title: Coherent Ising machines -- Quantum optics and neural network
perspectives
- Authors: Y. Yamamoto, T. Leleu, S. Ganguli, H. Mabuchi
- Abstract summary: When a pump rate or network coupling rate is increased from below to above threshold, eigenvectors with a smallest eigenvalue of Ising coupling matrix appear near threshold and impede the machine to relax to true ground states.
One approach is to utilize squeezed/anti-squeezed vacuum noise of OPOs below threshold to produce coherent spreading over numerous local minima via quantum noise correlation.
The other approach is to implement real-time error correction feedback loop so that the machine migrates from one local minimum to another during an explorative search for ground states.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A coherent Ising machine (CIM) is a network of optical parametric oscillators
(OPOs), in which the "strongest" collective mode of oscillation at well above
threshold corresponds to an optimum solution of a given Ising problem. When a
pump rate or network coupling rate is increased from below to above threshold,
however, the eigenvectors with a smallest eigenvalue of Ising coupling matrix
[J_ij] appear near threshold and impede the machine to relax to true ground
states. Two complementary approaches to attack this problem are described here.
One approach is to utilize squeezed/anti-squeezed vacuum noise of OPOs below
threshold to produce coherent spreading over numerous local minima via quantum
noise correlation, which could enable the machine to access either true ground
states or excited states with eigen-energies close enough to that of ground
states above threshold. The other approach is to implement real-time error
correction feedback loop so that the machine migrates from one local minimum to
another during an explorative search for ground states. Finally, a set of
qualitative analogies connecting the CIM and traditional computer science
techniques are pointed out. In particular, belief propagation and survey
propagation used in combinatorial optimization are touched upon.
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