Photonic Coherent Ising Machine Solving Combinatorial Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The Ising model, a simple model of ferromagnets in solid-state physics, maps a broad class of combinatorial optimization problems for which no efficient, accurate classical algorithm exists, making it challenging to solve effectively.
Innovation Solution
An integrated photonic coherent Ising machine is developed, comprising a combination of nodes and a connection network that forms an optical computer, using a feedback loop to process values until a steady state is reached at a minimum energy level, solving Ising problems by non-deterministically minimizing energy through a Hamiltonian function-based connection network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If classical algorithms are used to solve Ising problems, then the computational approach is deterministic and well-established, but no efficient and accurate solution exists for the broad class of combinatorial optimization problems
Solution Approach 1:
The patent replaces classical computational algorithms with a physical optical system. The Ising machine uses photonic components (modulators, beam splitters, detectors) to physically simulate the Ising model Hamiltonian, allowing the system to naturally evolve to its ground state through physical laws rather than computational iteration, thus achieving both efficiency and accuracy
Solution Approach 2:
The system dynamically adjusts optical parameters (phase shifts, amplitudes, coupling strengths) to encode the specific Ising problem instance. By changing these physical parameters, the same optical hardware can solve different combinatorial optimization problems efficiently and accurately
2Productivity
If an optical computer is used to solve Ising problems, then the system can efficiently find the minimum energy state, but the device complexity increases due to the need for multiple nodes and connection networks
Solution Approach 1:
The optical system is divided into modular photonic nodes, each representing a spin in the Ising model. These nodes can be independently fabricated and characterized, then assembled into larger systems. Each node contains local optical components (modulators, phase shifters) that can be independently controlled, managing complexity through modular design
Solution Approach 2:
The patent designs universal photonic nodes and connection elements that can be configured to implement different Ising problem instances. The same basic optical components (beam splitters, phase modulators, detectors) serve multiple functions depending on their configuration, reducing the need for specialized components for each problem type
3Measurement precision
If feedback loops are implemented to reach steady state, then the system can accurately determine the minimum energy configuration, but the time required to reach convergence increases
Solution Approach 1:
The system uses periodic modulation of the optical pump or local oscillators to drive the system through controlled evolution toward the ground state. This periodic action allows the system to efficiently sample the energy landscape and converge to the minimum energy configuration faster than continuous evolution would allow
Solution Approach 2:
The patent implements feedback loops where detectors measure the output states, and this information is fed back to modulators to adjust the system configuration. This feedback mechanism guides the system toward the minimum energy state by reinforcing low-energy configurations and suppressing high-energy ones, achieving both accuracy and reasonable convergence time
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for the efficient and accurate solution of Ising problems, such as the traveling salesman problem, by continuously processing values until they reach a minimum energy state, representing a solution to the problem at hand.
Implementation Method 1
a pump to provide coherent light to activate the plurality of nodes
Data Source
AI summary
In example implementations, an apparatus includes a plurality of nodes, a pump coupled to the plurality of nodes and a connection network. In one example, each one of the plurality of nodes may store a value. The pump provides energy to the each one of the plurality of nodes. The connection network may include a two dimensional array of elements, wherein each group of the two dimensional array of elements is in communication with a respective one of the plurality of nodes, wherein the connection network may be tuned with parameters associated with encoding of an Ising problem. The connection network may process the value stored in each one of the plurality of nodes. The Ising problem may be solved by the value stored in each one of the plurality of nodes at a minimum energy level.