[APS March Meeting] Pulse optimization for error-robust control on cloud-based hardware.
In this APS March Meeting 2021 video we describe an experimental effort designing and deploying error-robust single-qubit operations on IBM Quantum hardware
[APS March Meeting] Reinforcement learning for error-robust control on cloud-based hardware [I].
The noisy nature of today's quantum hardware limits the ability to realize functioning quantum computers
[APS March Meeting] Reinforcement learning for error-robust control on cloud-based hardware [II].
The noisy nature of today's quantum hardware limits the ability to realize functioning quantum computers
[APS March Meeting] Error-robust controls in quantum algorithms.
Current commercial quantum computers are prone to various kinds of noise processes, such as leakage and dephasing, which degrade the performance of quantum algorithms
[APS March Meeting] Noise reconstruction in quantum hardware via convex optimization.
In this APS March Meeting presentation, Dr Li Li Senior Quantum Control Engineer at Q-CTRL outlines that interactions between a quantum system and noisy control hardware, or its environment, critically limits the performance and capabilities of noisy intermediate-scale quantum (NISQ) devices, as well as future quantum computing technologies
[APS March Meeting] Optimized quantum solutions for vehicle routing problems.
Vehicle routing and scheduling are examples of transportation-network operational tasks that can be cast as optimization problems
[APS March Meeting] Noise-robust pulses for parametric entangling gates in superconducting qubits.
For Noisy Intermediate-Scale Quantum (NISQ) devices, incorporating robustness into computing operations is a critical target for enhancing computational capability
Improving quantum hardware with quantum controls
In this webinar we share our American Physical Society March Meeting presentations and host a live Q&A session with the speakers
Automated closed-loop optimizations
This webinar demonstrates how new features in Boulder Opal allow you to automate performance enhancements in quantum hardware through closed-loop experimental optimization
