Quantum Enhanced Learning Agent for Chip Design Optimization
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Solution Overview
Problem
Classical computing technologies face limitations in efficiently solving complex problems such as chip design optimization and combinatorial optimization, particularly in reinforcement learning and integrated circuit floorplanning, due to their inability to handle the complexity of these tasks effectively.
Innovation Solution
A quantum-enhanced learning agent is developed, utilizing a hybrid quantum-classical computer system that applies quantum gates to update its state and parameters based on input, enabling efficient reinforcement learning and optimization by generating outputs and updating its state through a combination of classical and quantum information processing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If classical computing is used for reinforcement learning and chip design optimization, then the system can handle basic computational tasks, but it cannot efficiently solve complex combinatorial optimization problems and floorplanning tasks
Solution Approach 1:
The patent combines quantum computing and classical computing into a hybrid system. The quantum computer handles specific computational tasks (particularly combinatorial optimization and reinforcement learning) while the classical computer manages other operations, creating a synergistic system that leverages the strengths of both paradigms to solve complex chip design problems efficiently
Solution Approach 2:
The patent introduces a hybrid quantum-classical interface that acts as an intermediary between quantum and classical computational systems. This interface enables seamless communication and data exchange, allowing the system to translate between quantum states and classical representations while maintaining computational efficiency
2Speed
If quantum computing is used to solve complex optimization problems, then computational speed and efficiency increase, but the device complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the computational workload by dividing problems into parts suitable for quantum processing (combinatorial optimization, reinforcement learning) and parts suitable for classical processing. This segmentation allows the system to achieve quantum speedup for specific tasks while avoiding the need for a fully complex quantum system
Solution Approach 2:
The patent employs near-term quantum hardware with limited qubit counts and coherence times, accepting that individual quantum computations may need to be repeated multiple times. This approach uses available quantum resources effectively without requiring long-lived, highly complex quantum states, making the system more practically implementable
3Manufacturing precision
If reinforcement learning is applied to chip floorplanning, then optimization quality improves, but the computational resources and time required increase beyond classical capabilities
Solution Approach 1:
The patent changes the computational parameters by implementing reinforcement learning on quantum hardware, fundamentally altering how optimization problems are solved. This parameter change enables the system to explore the solution space more efficiently, achieving higher optimization quality for chip floorplanning in reduced computational time compared to classical approaches
Data Source
AI summary
A method and apparatus for generating quantum-enhanced learning agents that can be used for optimizing tasks such as time series analysis, natural language processing, reinforcement learning, and combinatorial optimization. The method may be implemented on a hybrid quantum-classical computer. A learning agent is defined having an initial state S1, a set of parameters T1, and an input X1. The set of parameters are updated iteratively based on the input X1. The updated parameter set is generated, the agent state is updated, and an output is generated. Further enhancements include unrolling the agent in time and maintaining multiple copies of the agent across multiple iterations and entangling the copies of the agents. The disclosed technology may be used for computer chip design optimization for arranging chip components on a substrate, where circuit board parameters are efficiently assembled piece by piece, instead of a single optimization solution.