Genetic Search for Dynamical Decoupling in Noisy Quantum Circuits
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Solution Overview
Problem
Existing methods struggle to empirically identify effective dynamical decoupling sequences for error suppression in quantum computing, as they require matching quantum device and circuit noise criteria, which is complex and dynamic.
Innovation Solution
A genetic algorithm is employed to generate and evolve dynamical decoupling sequences, evaluating their fitness on a training quantum circuit to identify optimal sequences for error suppression, which can be applied to a target quantum circuit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If theoretical dynamical decoupling sequences are used, then error suppression is improved, but device compatibility and effectiveness are worsened due to mismatch with actual hardware noise characteristics
Solution Approach 1:
The patent implements a feedback mechanism where the genetic algorithm uses actual quantum device performance data (fidelity measurements) to evaluate and evolve dynamical decoupling sequences. The algorithm iteratively refines sequences based on measured outcomes from the specific quantum hardware, creating a closed-loop optimization process that adapts sequences to real device characteristics rather than relying on theoretical models alone.
Solution Approach 2:
The system enables the quantum device to identify its own optimal error suppression sequences through autonomous experimentation. The genetic algorithm automatically tests candidate sequences on the actual quantum hardware, measures performance, and evolves sequences without requiring external expertise in noise characterization or manual tuning, allowing the device to self-optimize for its specific noise profile.
2Reliability
If exhaustive search for optimal sequences is performed, then error suppression is improved, but computational complexity and time are worsened
Solution Approach 1:
The patent transforms the static, exhaustive search approach into a dynamic, evolutionary process. Instead of checking all possible sequences systematically, the genetic algorithm dynamically evolves sequences through selection, crossover, and mutation operations guided by fitness evaluations. This dynamic approach efficiently navigates the search space by focusing computational resources on promising regions rather than uniformly exploring all possibilities.
Solution Approach 2:
The system changes the optimization parameters from fixed theoretical assumptions to adaptive, data-driven parameters. The genetic algorithm adjusts sequence parameters (pulse timings, durations, types) based on actual device performance feedback, transforming the search from a theoretical parameter space to an empirically optimized parameter space that adapts to the specific quantum hardware characteristics.
3Reliability
If user expertise in noise characterization is required, then sequence effectiveness is improved, but ease of operation is worsened
Solution Approach 1:
The system enables the quantum device to identify its own optimal error suppression sequences through autonomous experimentation. The genetic algorithm automatically tests candidate sequences on the actual quantum hardware, measures performance, and evolves sequences without requiring external expertise in noise characterization or manual tuning, allowing the device to self-optimize for its specific noise profile.
Solution Approach 2:
The patent implements a feedback mechanism where the genetic algorithm uses actual quantum device performance data (fidelity measurements) to evaluate and evolve dynamical decoupling sequences. The algorithm iteratively refines sequences based on measured outcomes from the specific quantum hardware, creating a closed-loop optimization process that adapts sequences to real device characteristics rather than relying on theoretical models alone.
Data Source
AI summary
A method, system, and computer program product for empirically identifying a dynamical decoupling sequence for error suppression on a quantum computer. A training quantum circuit, with an equivalent circuit structure to the target quantum circuit, is selected to identify the optimal dynamical decoupling sequence. A population of dynamical decoupling sequences represented as sequences in a genetic algorithm is generated. Offsprings of a selected set of sequences (“parents”) from the population are then generated via reproduction and mutation forming candidate dynamical decoupling sequences, which refer to those dynamical decoupling sequences that may be selected to be used as a “parent” in a subsequent iteration for generating offsprings or selected as the dynamical decoupling sequence to run on the target quantum circuit. One or more dynamical decoupling sequences are selected from the candidate dynamical decoupling sequences based on their fitness with the objective function.