CAREER: Quantum Pulse Processing--Robust and Programmable Quantum Control for Near-Term Quantum Simulation

Quantum simulation offers exciting opportunities to explore complex phenomena that are too difficult for traditional computers to handle, such as how materials might exhibit superconductivity at high temperatures or the behavior of fundamental particles and forces. Advancements in quantum simulation could transform fields like materials science, drug discovery, and fundamental physics.

However, current quantum computers, known as Noisy-Intermediate Scale Quantum (NISQ) devices, face challenges with errors and limited computing power. This project aims to overcome these obstacles by improving NISQ devices using a technique called Quantum Pulse Processing. This approach integrates advanced control techniques with algorithm design to minimize noise and enhance the efficiency of quantum simulations, paving the way for applications across physics, chemistry, and engineering.

This research will contribute novel tools and frameworks that significantly enhance NISQ devices’ ability to address computational tasks beyond classical reach, bridging digital and analog quantum methods, and supporting the development of a quantum-ready workforce through open-source tools and educational resources. By fostering scientific progress in quantum technology, this work supports the National Science Foundation’s mission and strengthens the nation’s leadership in this transformative field.

The project leverages Quantum Signal Processing (QSP) principles, extending them to a quantum pulse-level control framework to facilitate more flexible and efficient quantum programming. By integrating pulse-level control optimization into quantum simulation, this research aims to create a hybrid framework that combines the programmability of gate-based methods with the efficiency of analog pulse-level approaches.

Key research directions include developing scalable multi-qubit control protocols to mitigate analog errors, creating robust hybrid digital-analog simulation algorithms that improve programmability and error resilience, and implementing scalable characterization methods for quantum control. Additionally, engineered dissipation methods will be employed to improve simulation fidelity against realistic noise, especially for simulating intricate physical phenomena like high-temperature superconductivity.

Validation and deployment will be conducted through collaborative experimentation with academic and industry partners, with algorithms tested on diverse quantum platforms, including superconducting qubits, ion traps, and neutral atoms.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.