Mixed-Integer Programming for Qubit Frequency Collision Mitigation
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Solution Overview
Problem
The challenge in scaling fixed-frequency quantum computing architectures is mitigating errors caused by lattice frequency collisions, which occur when qubit frequencies become too close, leading to frequency crowding and reduced gate fidelities.
Innovation Solution
The method involves using a computerized mixed-integer programming solver to define qubit collision types and constraints, iteratively minimizing collisions by optimizing qubit frequency tuning plans, and facilitating physical qubit tuning according to these plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed-frequency qubit architecture is used, then device complexity is reduced, but frequency collisions occur leading to reduced gate fidelities
Solution Approach 1:
The patent applies parameter changes by adjusting qubit frequencies from fixed to tunable values. The mixed-integer programming solver determines optimal frequency assignments that avoid collisions while maintaining architectural simplicity. This resolves the contradiction by changing the frequency parameter from static to dynamically optimized values.
2Reliability
If qubit frequencies are tuned to avoid collisions, then gate fidelity is improved, but tuning precision requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating optimal frequency assignments using mixed-integer programming before the tuning process. The solver determines target frequencies that maximize spacing and avoid collisions, providing a precise roadmap for the tuning process. This reduces the precision burden during actual tuning by establishing optimal targets in advance.
Solution Approach 2:
The patent introduces an intermediary optimization layer between qubit fabrication and final tuning. The mixed-integer programming solver acts as a mediator that translates physical constraints into optimized frequency assignments, which then guide the tuning process. This intermediary step simplifies the tuning precision requirements by providing pre-processed optimal targets.
3Reliability
If comprehensive collision constraints are applied, then frequency collision mitigation is improved, but computation time for tuning plan generation increases
Solution Approach 1:
The patent applies segmentation by dividing the qubit lattice into groups and applying collision constraints systematically to each group. The mixed-integer programming solver processes constraints in an organized manner, evaluating collision types categorically. This segmentation approach makes the comprehensive constraint evaluation computationally manageable while maintaining thorough collision mitigation.
Solution Approach 2:
The patent implements a two-stage optimization process where the solver first minimizes collision counts, then separately maximizes frequency margins. This partial action approach breaks down the comprehensive optimization into manageable stages, reducing computation time while achieving thorough collision mitigation through the sequential application of optimization objectives.
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 improves tuning precision and accuracy, enhances yield by reducing frequency collisions, and increases the speed of frequency tuning plan generation, resulting in higher-quality quantum computing systems.
Implementation Method 1
The LASIQ (Laser Annealing of Stochastically Impaired Qubits) technique has been developed to increase collision-free yield of transmon lattices by selectively trimming (i.e., tuning) individual qubit frequencies via laser thermal annealing.
Data Source
AI summary
Define a plurality of qubit collision types and a plurality of constraints. For a group of qubits, use a computerized mixed-integer programming solver to, subject to the constraints, iteratively minimize collisions by minimizing a sum of products of weights multiplied by an amount of frequency collisions for given ones of the constraints of each one of the collision types. Output a frequency tuning plan for the group of qubits, based on the iterative minimization. Facilitate tuning physical qubits in accordance with the frequency tuning plan.