Hybrid Quantum-Classical Optimization Using Angle Memory
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Solution Overview
Problem
Current hybrid quantum-classical computing mechanisms face limitations in solving optimization problems due to the unavailability of Quantum Ram (Q-RAM), leading to non-optimal results as they cannot store chromosome states, and access to quantum computing resources is expensive and scarce.
Innovation Solution
An enhanced hybrid quantum-classical computing mechanism is proposed, utilizing at least one quantum processor unit (QPU) with shared classical memory to store angle memory values for configuration chromosomes, generating state vectors, and selecting the most probable configuration chromosomes through a fitness function, allowing for rotation operations to approximate optimal chromosome states without relying on Q-RAM for storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If Q-RAM is used to store chromosome states, then storage capacity and optimization performance are improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent introduces angle memory as an intermediary classical data structure that stores rotation angles θ instead of directly storing quantum chromosome states in Q-RAM. This mediator enables the system to achieve effective chromosome state management through classical memory, avoiding the need for complex Q-RAM hardware while maintaining the quantum-classical hybrid optimization capability.
2Manufacturing precision
If Q-RAM is implemented for optimal results, then solution quality is improved, but availability and accessibility worsen due to years of development needed
Solution Approach 1:
The patent replaces the expensive, long-development Q-RAM with readily available classical memory structures (angle memory). This substitution uses inexpensive, immediately accessible classical computing resources to achieve practical optimization results, avoiding the need to wait for Q-RAM technology maturity while still enabling quantum-inspired optimization.
3Productivity
If chromosome states are stored in Q-RAM, then optimization performance is improved, but access cost and resource availability worsen
Solution Approach 1:
The patent creates a classical copy (angle memory) of the essential quantum chromosome information (rotation angles θ) that can be stored and manipulated in inexpensive classical memory. This copying approach allows the system to perform multiple optimization iterations using readily available classical resources, avoiding repeated expensive accesses to Q-RAM while maintaining optimization performance.
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 enables effective solving of optimization problems by generating new populations of chromosomes based on modified angle memory values, achieving optimal solutions without the need for Q-RAM storage, thus overcoming the limitations of existing technologies.
Implementation Method 1
generating at least two most probable configuration chromosome from the reinitialized quantum circuit corresponding to a superposition of qubits in a position chromosome
Implementation Method 2
rotating a set of states of the qubit in the configuration chromosome towards the state of the qubit in the best configuration chromosome based on the modification of the stored value of θ in angle memory
Implementation Method 3
selecting one of the at least two most probable configuration chromosomes for each position chromosome after evaluation by a fitness function
Data Source
AI summary
A method of enhanced hybrid quantum-classical computing mechanism for solving optimization problems is disclosed comprising altering a value of a configuration chromosome by storing an angle memory on a shared classical memory. The angle memory corresponds to a predefined configuration chromosome. The method then generates a state vector based on the angle memory and reinitializes a quantum circuit from the state vector. Subsequently, generating at least two most probable configuration chromosome from the reinitialized quantum circuit corresponding to a superposition of qubits in a position chromosome. Subsequently selecting one of the at least two most probable configuration chromosomes for each position chromosome after evaluation by a fitness function.