The models that will power the next generation of AGI may not run on silicon. They will run on the cloud, suspended in a dilution refrigerator, entangled and waiting.
Cloud-based QML is currently a research tool, not a production engine. However, the infrastructure is being laid. Just as cloud computing democratized GPUs for AI in 2012, cloud quantum services are democratizing qubits for AI in 2024.
Enter the cloud.
For the past decade, two revolutionary technologies have evolved on parallel tracks: Quantum Computing and Machine Learning (ML). Quantum promised exponential speedups for specific math problems; Machine Learning promised to find patterns in chaos. The problem? Quantum computers are not laptops. They require temperatures colder than deep space and are prone to errors from the slightest vibration.
The models that will power the next generation of AGI may not run on silicon. They will run on the cloud, suspended in a dilution refrigerator, entangled and waiting.
Cloud-based QML is currently a research tool, not a production engine. However, the infrastructure is being laid. Just as cloud computing democratized GPUs for AI in 2012, cloud quantum services are democratizing qubits for AI in 2024. cloud based quantum machine learning services
Enter the cloud.
For the past decade, two revolutionary technologies have evolved on parallel tracks: Quantum Computing and Machine Learning (ML). Quantum promised exponential speedups for specific math problems; Machine Learning promised to find patterns in chaos. The problem? Quantum computers are not laptops. They require temperatures colder than deep space and are prone to errors from the slightest vibration. The models that will power the next generation
