A single shot coherent Ising machine based on a network of injection-locked multicore fiber lasers

Babaeian, Masoud; Dan, Nguyen Trung; Demir, Veysi; Akbulut, Mehmetcan; Blanche, P.-A.; Kaneda, Yushi; Guha, Saikat; Neifeld, Mark A.; Peyghambarian, N.

doi:10.1038/s41467-019-11548-4

Cited by 83 publications

(66 citation statements)

References 64 publications

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“…The method of BPM has been well developed for decades and commercial software is also available. Details of the BPM method are presented generally in [43,44], its applications for simulating QWs in photonics lattices [38,39] and multicore fiber lasers in [45,46].…”

Section: Methodsmentioning

confidence: 99%

“…We have our own Matlab codes to simulate beam propagations in complicated structures of waveguides. The method has been successfully applied to simulate and design Yb-doped multicore fiber lasers for the coherent Ising machine [45,46], and also for single-photon quantum QWs in regular and irregular arrays of waveguides [37,38]. Details of the BPM method are presented generally in [43,44], its applications for simulating QWs in photonics lattices [38,39] and multicore fiber lasers in [45,46], and the simulation results of QWs in MCRF and FMCRF are presented as follows.…”

Section: Quantum Walks In Periodic and Quasiperiodic Multicore Ring Fmentioning

confidence: 99%

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Quantum Walks in Periodic and Quasiperiodic Fibonacci Fibers

Nguyen

Khrapko

et al. 2020

Sci Rep

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Quantum walk is a key operation in quantum computing, simulation, communication and information. Here, we report for the first time the demonstration of quantum walks and localized quantum walks in a new type of optical fibers having a ring of cores constructed with both periodic and quasiperiodic Fibonacci sequences, respectively. Good agreement between theoretical and experimental results have been achieved. The new multicore ring fibers provide a new platform for experiments of quantum effects in low-loss optical fibers which is critical for scalability of real applications with large-size problems. Furthermore, our new quasiperiodic Fibonacci multicore ring fibers provide a new class of quasiperiodic photonics lattices possessing both on-and offdiagonal deterministic disorders for realizing localized quantum walks deterministically. The proposed Fibonacci fibers are simple and straightforward to fabricate and have a rich set of properties that are of potential use for quantum applications. Our simulation and experimental results show that, in contrast with randomly disordered structures, localized quantum walks in new proposed quasiperiodic photonics lattices are highly controllable due to the deterministic disordered nature of quasiperiodic systems.

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Section: Methodsmentioning

confidence: 99%

Section: Quantum Walks In Periodic and Quasiperiodic Multicore Ring Fmentioning

confidence: 99%

Quantum Walks in Periodic and Quasiperiodic Fibonacci Fibers

Nguyen

Khrapko

et al. 2020

Sci Rep

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“…Thus, in this case and many of the others (except Section 4.E), the slowly varying time dynamics can be reproduced from iterative optimization steps on the Lagrange function. The coupling between the fiber cores is achieved through amplitude mixing of the laser modes by Spatial Light Modulators at one end of the multicore fiber (10). These Spatial Light Modulators couple light amplitude from the ith core to the j th core according to the prescribed connection weight Jij .…”

Section: B3 Time Dynamics and Iterative Optimization Of The Lagramentioning

confidence: 99%

Physics successfully implements Lagrange multiplier optimization

Vadlamani

Xiao

Yablonovitch

2020

Proc. Natl. Acad. Sci. U.S.A.

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Optimization is a major part of human effort. While being mathematical, optimization is also built into physics. For example, physics has the Principle of Least Action; the Principle of Minimum Power Dissipation, also called Minimum Entropy Generation; and the Variational Principle. Physics also has Physical Annealing, which, of course, preceded computational Simulated Annealing. Physics has the Adiabatic Principle, which, in its quantum form, is called Quantum Annealing. Thus, physical machines can solve the mathematical problem of optimization, including constraints. Binary constraints can be built into the physical optimization. In that case, the machines are digital in the same sense that a flip–flop is digital. A wide variety of machines have had recent success at optimizing the Ising magnetic energy. We demonstrate in this paper that almost all those machines perform optimization according to the Principle of Minimum Power Dissipation as put forth by Onsager. Further, we show that this optimization is in fact equivalent to Lagrange multiplier optimization for constrained problems. We find that the physical gain coefficients that drive those systems actually play the role of the corresponding Lagrange multipliers.

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“…The recent advances in development of physical platforms for optimising combinatorial optimisation problems reveal the future of high-performance computing for the quantum and classical devices. Unconventional computing architectures were proposed for numerous systems including superconducting qubits [1][2][3], CMOS hardware [4], optical parametric oscillators [5,6], memristors [7], lasers [8][9][10], photonic simulators [11,12], trapped ions [13], polariton [14,15] and photon [16] condensates. An attractive opportunity to show the advantageous performance of one system over others becomes a demonstration of the platform's ability to optimise nondeterministic polynomial time (NP) problems that are computationally intractable for the traditional von Neumann architecture machines.…”

Section: Introductionmentioning

confidence: 99%

Complexity continuum within Ising formulation of NP problems

Kalinin

Berloff

2020

Preprint

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A promising approach to achieve computational supremacy over the classical von Neumann architecture explores classical and quantum hardware as Ising machines. The minimisation of the Ising Hamiltonian is known to be NP-hard problem for certain interaction matrix classes, yet not all problem instances are equivalently hard to optimise. We propose to identify computationally simple instances with an `optimisation simplicity criterion'. Such optimisation simplicity can be found for a wide range of models from spin glasses to k-regular maximum cut problems. Many optical, photonic, and electronic systems are neuromorphic architectures that can naturally operate to optimise problems satisfying this criterion and, therefore, such problems are often chosen to illustrate the computational advantages of new Ising machines. We further probe an intermediate complexity for sparse and dense models by analysing circulant coupling matrices, that can be `rewired' to introduce greater complexity. A compelling approach for distinguishing easy and hard instances within the same NP-hard class of problems can be a starting point in developing a standardised procedure for the performance evaluation of emerging physical simulators and physics-inspired algorithms.

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A single shot coherent Ising machine based on a network of injection-locked multicore fiber lasers

Cited by 83 publications

References 64 publications

Quantum Walks in Periodic and Quasiperiodic Fibonacci Fibers

Quantum Walks in Periodic and Quasiperiodic Fibonacci Fibers

Physics successfully implements Lagrange multiplier optimization

Complexity continuum within Ising formulation of NP problems

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