Quantum Control Sequences With Distortion-Aware Gate Optimization
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Solution Overview
Problem
Existing quantum control systems face challenges in generating robust and high-fidelity control sequences for quantum devices, particularly due to nonlinear distortions and uncertainties in classical control hardware, which affect the performance and scalability of quantum systems.
Innovation Solution
A control framework that incorporates a distortion model representing nonlinear relationships between control signals and input signals, using optimal control theory to iteratively modify control sequences, and accounts for uncertainties in classical control hardware, enabling robust quantum gate design and operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optimal control theory is used to generate control sequences, then control precision is improved, but computational complexity increases
Solution Approach 1:
The distortion model is pre-characterized and stored in memory before control sequence generation. By pre-computing and storing the nonlinear relationship between control signals and actual system response, the system avoids real-time complex calculations during control sequence optimization, reducing computational complexity while maintaining high control precision through the use of the pre-established distortion model
Solution Approach 2:
A distortion model acts as an intermediary between the control signal generator and the quantum system. This intermediate model compensates for nonlinear distortions by pre-processing control signals, allowing the optimization algorithm to work with corrected signals rather than raw signals, thereby improving control precision without proportionally increasing computational complexity
2Reliability
If distortion models are incorporated into control frameworks, then reliability is improved, but device complexity increases
Solution Approach 1:
The control system performs self-characterization by automatically generating distortion models through empirical measurements of its own hardware components. The system measures actual control signal responses and uses this data to build distortion models that compensate for hardware nonlinearities, improving reliability through self-calibration without requiring external complex modeling apparatus
Solution Approach 2:
The distortion model incorporates uncertainty parameters that are dynamically adjusted based on measured hardware variations. By parameterizing the distortion model with measurable hardware characteristics and uncertainty bounds, the system improves reliability through adaptive compensation while keeping the framework complexity manageable through parameter-based rather than structure-based complexity
3Manufacturing precision
If iterative optimization is used to generate control sequences, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
Initial control sequences are pre-generated using simplified models or heuristic methods before being refined through iterative optimization. This preliminary action provides a good starting point for the iterative process, reducing the number of iterations needed to achieve high quantum gate fidelity and thereby reducing the time loss associated with iterative optimization
Solution Approach 2:
The iterative optimization process uses feedback from distortion model predictions to guide adjustments in control sequences. By incorporating real-time feedback from the distortion model about expected hardware nonlinearities, the optimization converges faster to high-fidelity solutions, improving quantum gate fidelity while minimizing the time penalty of iteration
Data Source
AI summary
In some aspects, a control system interacts with a quantum system. In some instances, the quantum system includes qubits that respond to a control signal generated by the control system, and the control system is configured to generate the control signal in response to an input signal. A control sequence (which may include, for example, a sequence of values for the input signal) can be generated by a computing system based on a target operation to be applied to the qubits. The control sequence can be generated based on the target operation, a quantum system model, a distortion model and possibly other information. The quantum system model represents the quantum system and includes a control parameter representing the control signal. The distortion model represents a nonlinear relationship between the control signal and the input signal. The control sequence is applied to the quantum system by operation of the control system.