Qubit Frequency Optimization Under Interaction and Hardware Constraints
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Solution Overview
Problem
Quantum computing devices face challenges in determining optimal operating frequencies for qubits due to hardware imperfections such as material defects, leading to high-dimensional and non-convex optimization problems that are computationally intractable, especially for larger quantum processors.
Innovation Solution
A method is described to determine qubit operating frequencies by incorporating physics and engineering constraints, breaking the optimization problem into grid-scale, pair-scale, and qubit-scale sub-problems, and using optimization routines to adjust frequencies, reducing the search space and computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optimization methods are used to determine qubit operating frequencies, then comprehensive frequency optimization can be achieved, but computational complexity becomes intractable for larger quantum processors
Solution Approach 1:
The patent divides the high-dimensional optimization problem into multiple low-dimensional sub-problems by segmenting qubits into groups based on their interaction patterns. Each sub-problem optimizes frequencies for a specific group independently, reducing the overall computational complexity from exponential to polynomial scale while maintaining optimization accuracy through iterative refinement.
Solution Approach 2:
The patent transforms the original high-dimensional frequency optimization problem by changing parameters through grouping strategies and constraint formulations. By reparameterizing the problem in terms of group-based frequency relationships rather than individual qubit frequencies, the computational complexity is reduced while preserving the essential optimization objectives.
2Reliability
If hardware imperfections such as material defects are present, then qubit operation accuracy decreases, but the optimization problem becomes more difficult to solve
Solution Approach 1:
The patent applies local quality by allowing different frequency optimization strategies for different qubit groups based on their specific hardware characteristics and defect profiles. Each group can have customized frequency assignments that account for local hardware imperfections, rather than applying a uniform optimization approach across all qubits.
Solution Approach 2:
The patent performs preliminary characterization of hardware imperfections and defect patterns before executing the frequency optimization. By pre-identifying problematic qubits and their interaction patterns, the optimization algorithm can proactively avoid problematic frequency assignments and focus computational resources on critical optimization targets.
3Productivity
If qubit frequencies are optimized independently, then computational efficiency improves, but interaction between qubits is not adequately considered
Solution Approach 1:
The patent segments qubits into interaction-based groups where frequencies are optimized collectively for each group rather than independently for each qubit. This maintains computational efficiency by limiting the optimization scope to manageable group sizes while ensuring that interaction accuracy is preserved within each group through joint optimization.
Solution Approach 2:
The patent merges the optimization of frequencies for qubits that interact with each other, treating them as a coupled system within each group. By combining their frequency optimization into a unified sub-problem, the method ensures that interaction requirements are satisfied while maintaining overall computational efficiency through the grouping structure.
Data Source
AI summary
Methods, systems, and apparatus for determining frequencies at which to operate interacting qubits arranged as a two dimensional grid in a quantum device. In one aspect, a method includes the actions of defining a first cost function that characterizes technical operating characteristics of the system. The cost function maps qubit operation frequency values to a cost corresponding to an operating state of the quantum device; applying one or more constraints to the defined first cost function to define an adjusted cost function; and adjusting qubit operation frequency values to vary the cost according to the adjusted cost function such that the operating state of the quantum device is improved.