Hybrid Quantum Processor Sampling for Objective Function Minimization
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Solution Overview
Problem
Existing quantum processors are limited by their architecture, requiring direct mapping of problems to their native formulations, which is impractical for problems with higher-order interactions and reduces the range of solvable problems, especially for combinatorial optimization tasks.
Innovation Solution
Operate quantum processors as sample generators to provide samples from a probability distribution, shaping the distribution using digital computers to solve a broader range of problems without direct mapping, enabling hybrid systems with digital processors to handle more variables and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If quantum processors are used with direct mapping to their native formulations, then the processor can operate in its native architecture, but the range of solvable problems is reduced and problems with higher-order interactions cannot be solved
Solution Approach 1:
The patent introduces an intermediary system comprising a digital computer and a quantum processor working together. The digital computer prepares initial states, executes evolution protocols, and processes results, while the quantum processor performs quantum annealing or adiabatic evolution. This intermediary digital computing layer enables the quantum processor to solve problems that cannot be directly mapped to its native formulation, including problems with higher-order interactions, thereby increasing adaptability without requiring changes to the quantum processor architecture itself.
2Adaptability or versatility
If quantum processors are operated as sample generators with shaped probability distributions, then problems without direct mapping can be solved, but the system requires hybrid operation with digital processors
Solution Approach 1:
The hybrid system achieves universality by combining the quantum processor's ability to generate samples from probability distributions with the digital computer's ability to shape these distributions and handle higher-order interactions. The quantum processor is not limited to its native problem formulation but can be guided to solve a broader class of optimization problems through the collaborative digital-quantum workflow, making the overall system universally applicable to various problem types.
Solution Approach 2:
The system implements feedback loops where the digital computer evaluates samples generated by the quantum processor, shapes the probability distribution based on evaluation results, and feeds back adjusted parameters to the quantum processor for subsequent sampling iterations. This feedback mechanism enables the hybrid system to converge on optimal solutions for problems that cannot be directly mapped to the quantum processor's native formulation.
3Productivity
If the evolution is too fast in adiabatic quantum computation, then the computation time is reduced, but the system can be excited to higher energy states
Solution Approach 1:
The system dynamically adjusts the evolution schedule of the quantum processor during computation. Rather than using a fixed linear schedule, the evolution rate is adaptively controlled to spend more time in critical regions of the Hamiltonian evolution where energy gaps are small, and move faster through regions where gaps are large. This dynamic scheduling allows the system to maintain high ground state accuracy while achieving faster overall computation times compared to traditional fixed schedules.
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 solving problems that cannot be directly mapped, reduces dependency on processor architecture, and provides more accurate and faster solutions for combinatorial optimization, especially in minimization problems, while simplifying user interaction with quantum processors.
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
operating the quantum processor as a sample generator providing samples from a probability distribution over the states of the quantum processor
Data Source
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AI summary
Quantum processor based techniques minimize an objective function for example by operating the quantum processor as a sample generator providing low-energy samples from a probability distribution with high probability. The probability distribution is shaped to assign relative probabilities to samples based on their corresponding objective function values until the samples converge on a minimum for the objective function. Problems having a number of variables and/or a connectivity between variables that does not match that of the quantum processor may be solved. Interaction with the quantum processor may be via a digital computer. The digital computer stores a hierarchical stack of software modules to facilitate interacting with the quantum processor via various levels of programming environment, from a machine language level up to an end-use applications level.