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Related Concept Videos

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

714
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
714

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Related Experiment Video

Updated: Aug 19, 2025

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
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Optimization with photonic wave-based annealers.

A Prabhakar1, P Shah1, U Gautham1

  • 1Indian Institute of Technology Madras, Chennai 600036, India.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|December 4, 2022
PubMed
Summary

Photonic Ising machines show promise for solving complex optimization problems. While classical solvers excel at Max-cut, spatial PIMs outperform as problem size increases for number partitioning.

Keywords:
IsingMax-cutnumber partitioningphotonicsquadratic unconstrained binary optimization

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Area of Science:

  • Physics
  • Computer Science
  • Quantum Computing

Background:

  • Combinatorial optimization (CO) problems are often NP-hard.
  • Quadratic unconstrained binary optimization (QUBO) models map to Ising models.
  • Ising models find solutions via adiabatic spin configuration evolution.

Purpose of the Study:

  • Enhance two Photonic Ising Machines (PIMs): Temporal Multiplexed Coherent Ising Machine (TMCIM) and Spatial Photonic Ising Machine (SPIM).
  • Compare PIM performance against classical (Gurobi) and quantum (D-Wave) solvers.
  • Investigate problem suitability for different PIM architectures.

Main Methods:

  • TMCIM utilizes electro-optic modulator bistability for spin states, tested on Max-cut problems.
  • SPIM uses laser wavefront convolution with spatial light modulators for adiabatic energy minimization, tested on Number Partitioning Problems (NPP).
  • Performance comparison across PIMs, classical, and quantum solvers.

Main Results:

  • Classical solvers remain superior for Max-cut problems.
  • SPIM demonstrates improved performance over classical solvers as NPP size increases.
  • Hardware connectivity is identified as a critical factor for PIM performance.

Conclusions:

  • The choice of PIM architecture is problem-dependent.
  • Further research is needed to identify optimal CO problems for specific PIMs.
  • PIMs offer a potential avenue for tackling large-scale optimization challenges.