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Make quantum computing useful with the world’s first performance-management software solution

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Presented by Pranav Santosh Mundada (Q-CTRL)
Excitement about the promise of quantum computers is tempered by the reality that the hardware remains exceptionally fragile and error-prone, forming a bottleneck in the development of novel applications. This talk aims to be accessible to a broad audience and starts with a brief introduction about Q-CTRL and how we bring value to the quantum community. Q-CTRL core technology utilizes the development of AI-based deterministic quantum error suppression workflow and is made accessible through Fire Opal. Fire Opal provides a pathway for algorithm developers to maximize hardware performance in near-term systems, and it forms a necessary component in enabling quantum error correction in future systems. We will discuss the key elements of this workflow and highlight the effectiveness of our techniques by showcasing up to 1000X improvement over the best alternative expert-configured techniques available in the open literature. In all cases, the deterministic error-suppression workflow delivers the highest performance without the need for any additional sampling or randomization overhead.
Excitement about the promise of quantum computers is tempered by the reality that the hardware remains exceptionally fragile and error-prone, forming a bottleneck in the development of novel applications. This talk aims to be accessible to a broad audience and starts with a brief introduction about Q-CTRL and how we bring value to the quantum community. Q-CTRL core technology utilizes the development of AI-based deterministic quantum error suppression workflow and is made accessible through Fire Opal. Fire Opal provides a pathway for algorithm developers to maximize hardware performance in near-term systems, and it forms a necessary component in enabling quantum error correction in future systems. We will discuss the key elements of this workflow and highlight the effectiveness of our techniques by showcasing up to 1000X improvement over the best alternative expert-configured techniques available in the open literature. In all cases, the deterministic error-suppression workflow delivers the highest performance without the need for any additional sampling or randomization overhead.