Quantum GRAPE Pulse Optimization with QubitPulseOpt Bridges Sim-to-Real Gap in Noisy Intermediate-Scale Quantum Computation

The pursuit of reliable quantum computation faces a significant hurdle in the form of noise and instability within current quantum processors. Rylan Malarchick from Embry-Riddle Aeronautical University, along with colleagues, addresses this challenge by presenting a new framework for designing quantum gates that actively compensates for real-world hardware imperfections. This work introduces a rigorously tested software platform, QubitPulseOpt, which constructs a highly accurate simulation of a quantum processor, incorporating parameters that reflect actual hardware behaviour. By optimising gate pulses using a technique called GRAPE within this realistic simulation, the team demonstrates a substantial reduction in gate errors, a 77% improvement compared to standard methods, and establishes a new standard for trustworthy quantum control software through extensive verification and adherence to stringent safety-critical coding practices.

Hardware-Calibrated Quantum Pulse Optimisation Framework

QubitPulseOpt is a new open-source software framework that enhances quantum optimal control by incorporating realistic hardware characteristics, addressing the disconnect between theoretical optimization and practical implementation on noisy quantum devices. Key to its functionality is the creation of digital twins of qubits, utilizing realistic parameters like T1, T2, and frequency obtained from hardware calibration data, allowing for more accurate simulation of pulse performance. The framework employs the GRAPE algorithm to identify optimal control pulses that maximize gate fidelity, surpassing the performance of traditional analytical solutions. Simulations demonstrate a 77% reduction in gate error compared to standard Gaussian pulses when using GRAPE-optimized pulses within a hardware-representative noise environment.

The framework prioritizes code quality, verification, and reproducibility with a comprehensive 864-test suite achieving 74% code coverage and adherence to safety-critical coding standards. Currently, the implementation focuses on simulating the optimization process and validating results, with plans to move towards a closed-loop, real-time hardware-in-the-loop optimization system. QubitPulseOpt represents a significant step towards making quantum optimal control a practical tool for near-term quantum applications by focusing on realistic hardware modeling and robust software engineering practices, providing a solid foundation for developing and validating optimized pulses before deployment on actual quantum devices.

Real Hardware Aware Pulse Optimisation

Scientists developed QubitPulseOpt, a Python framework designed to optimize quantum control pulses while directly addressing the limitations imposed by real-world quantum hardware. The study pioneers a workflow that begins with establishing API connectivity to quantum cloud platforms, specifically IQM’s Garnet processor, a 20-qubit system. This connectivity enables the framework to access and incorporate current device parameters into the optimization process. The core of the methodology involves constructing a high-fidelity Lindblad master equation simulation, which accurately models qubit behavior by incorporating hardware-representative noise parameters, including both T1 and T2times.

The team then implemented the Gradient Ascent Pulse Engineering (GRAPE) algorithm within this simulation environment, utilizing gradient-based optimization to identify pulse sequences that maximize gate fidelity. This approach surpasses the performance of traditional analytical solutions, particularly when operating within realistic noise models. Researchers demonstrated a significant 77% reduction in gate error when comparing GRAPE-optimized pulses to standard pulses within these hardware-representative simulations. To ensure reliability, the study employed a rigorous software engineering approach, incorporating an 864-test verification suite achieving 74% code coverage, with over 85% coverage of critical hardware integration modules.

All hardware integration tests utilize mocked API responses, ensuring reproducibility without requiring access to actual quantum hardware. Furthermore, the codebase adheres to NASA JPL Power-of-10 safety-critical coding standards, guaranteeing numerical stability and reproducibility throughout the optimization process. This combination of advanced optimization techniques and robust software engineering establishes a new paradigm for trustworthy quantum control software, bridging the gap between simulation and real-world performance.

GRAPE Optimisation Reduces Gate Errors Significantly

Scientists achieved a substantial reduction in gate error using pulses optimized with the Gradient Ascent Pulse Engineering (GRAPE) algorithm, as demonstrated through rigorous simulation. This breakthrough stems from the development of QubitPulseOpt, a new Python framework designed to bridge the gap between idealized simulations and the realities of noisy intermediate-scale quantum (NISQ) hardware. The framework establishes API connectivity to IQM’s Garnet quantum processor, a 20-qubit superconducting device, and constructs a high-fidelity simulation environment incorporating realistic hardware parameters, such as T1 = 50 microseconds and T2 = 70 microseconds. Researchers verified the framework’s reliability with an extensive 864-test verification suite, achieving 74% code coverage and adhering to NASA JPL Power-of-10 safety-critical coding standards. This commitment to software engineering ensures the trustworthiness and reproducibility of the results. While the work focuses on simulation, the framework is designed to facilitate rapid pulse development for deployment on actual quantum hardware.

Realistic Hardware Improves Quantum Gate Fidelity

This work presents QubitPulseOpt, a rigorously tested Python framework designed to improve the fidelity of quantum gate operations on noisy intermediate-scale quantum (NISQ) computers. The core achievement lies in bridging the gap between simulated optimization and real-world hardware performance by incorporating hardware-representative parameters into the optimization process. The framework connects to quantum processors, such as the IQM Garnet system, and constructs a detailed “digital twin” based on live calibration data, accurately reflecting device-specific characteristics like relaxation and dephasing rates. Using this digital twin, researchers demonstrate that the gradient ascent pulse engineering (GRAPE) algorithm, when optimized with realistic parameters, achieves a substantial 77% reduction in gate error compared to standard Gaussian pulses.

This improvement stems from actively mitigating the effects of decoherence and control noise, which are major limitations in current quantum technology. The framework’s reliability is further underscored by a comprehensive 864-test verification suite, ensuring numerical stability and adherence to stringent safety-critical coding standards. Future research directions include exploring more sophisticated noise models and extending the framework to optimize multi-qubit gate operations, ultimately paving the way for more robust and reliable quantum computations.