Quantum Processor Mapping Continuous Variables to Discrete States
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Solution Overview
Problem
Quantum processors face limitations in solving computational problems with non-binary variables, as existing methods require direct mapping of problems to the processor's architecture, which can be impractical for problems with higher-order interactions, and existing approaches are not efficient for continuous or integer variables.
Innovation Solution
A method is introduced to map continuous or integer variables to discrete variables using a mapping function, allowing quantum processors to solve objective functions by generating samples from a probability distribution, where the probability distribution is shaped to increase the likelihood of low-energy states corresponding to desirable outputs, thereby minimizing the objective function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If direct mapping of continuous variable problems to quantum processor architecture is used, then the problem can be solved on quantum hardware, but the approach becomes impractical for problems with higher-order interactions and limits the range of solvable problems
Solution Approach 1:
The patent introduces an intermediary mapping function that translates continuous variable objective functions into discrete variable forms suitable for quantum processors. This mapping function acts as a mediator between the continuous problem space and the discrete quantum hardware, enabling problems with higher-order interactions to be solved without direct complex mapping to the processor architecture.
Solution Approach 2:
The patent transforms the problem parameters by converting continuous variables into discrete variables through a mapping function. This parameter transformation allows the quantum processor to handle continuous variable optimization problems by working with discrete representations, thereby expanding the versatility of solvable problems without increasing device complexity.
2Productivity
If existing quantum processor methods are used for continuous or integer variables, then computation can be performed, but efficiency is reduced due to architectural limitations
Solution Approach 1:
The mapping function serves as an intermediary that prepares continuous or integer variable problems in a format optimized for quantum processor execution. By transforming the problem representation before input to the quantum hardware, the system achieves better computation efficiency while working within existing architectural constraints.
3Reliability
If probability distribution is shaped to increase likelihood of low-energy states, then desirable outputs are more likely obtained, but the probability distribution must be carefully controlled
Solution Approach 1:
The system employs feedback mechanisms to shape and control the probability distribution on the quantum processor. By adjusting the distribution based on the objective function and previous results, the system increases the likelihood of obtaining low-energy states that correspond to desirable outputs, thereby improving solution accuracy while maintaining manageable control complexity.
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 quantum processors to solve computational problems with continuous or integer variables without the need for direct mapping to the processor's architecture, allowing for a broader range of problem solutions and simplifying the programming process, while ensuring efficient minimization of the objective function.
Implementation Method 1
quantum annealing may use quantum effects, such as quantum tunneling, to reach a global energy minimum more accurately and/or more quickly than classical annealing
Implementation Method 2
evolving a system from a known initial Hamiltonian (the Hamiltonian being an operator whose eigenvalues are the allowed energies of the system) to a final Hamiltonian by gradually changing the Hamiltonian
Implementation Method 3
generating samples from a probability distribution, where the probability distribution is shaped to increase the likelihood of low-energy states corresponding to desirable outputs
Data Source
AI summary
Computational techniques for mapping a continuous variable objective function into a discrete variable objective function problem that facilitate determining a solution of the problem via a quantum processor are described. The modified objective function is solved by minimizing the cost of the mapping via an iterative search algorithm.