Quantum Machines, Nvidia use machine learning to get closer to quantum computer
Quantum Machines and Nvidia are collaborating to enhance error-corrected quantum computing using machine learning, focusing on qubit calibration improvements to support error correction and future developments with Blackwell chips.
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Quantum Machines and Nvidia are advancing towards error-corrected quantum computing through a partnership that leverages machine learning. Their collaboration utilizes Nvidia's DGX Quantum platform alongside Quantum Machines' quantum control hardware to enhance qubit calibration on Rigetti quantum chips. This process involves using reinforcement learning to maintain optimal calibration of qubit control pulses, which is crucial for improving quantum computer performance. The companies emphasize that even minor improvements in calibration can lead to significant enhancements in error correction, which is essential for achieving fault-tolerant quantum computing. The initial results of their work indicate that the integration of powerful computing resources can address the complex challenges of quantum error correction. Future plans include expanding this collaboration and making tools available to more researchers, with the expectation that Nvidia's upcoming Blackwell chips will further enhance their capabilities. Overall, while the current achievements are incremental, they represent important steps toward solving critical issues in quantum computing.
- Quantum Machines and Nvidia are collaborating to improve quantum computing through machine learning.
- Their focus is on enhancing qubit calibration to support error correction in quantum systems.
- Minor calibration improvements can lead to significant gains in logical qubit performance.
- The partnership aims to make advanced tools accessible to researchers and developers.
- Future developments will leverage Nvidia's more powerful Blackwell chips for enhanced performance.
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