A new arXiv paper outlines a path to scaling quantum computers from hundreds to millions of qubits by leveraging semiconductor manufacturing techniques and integrating them with classical high-performance computing. The research details resource estimates for applications like quantum chemistry and catalyst design, emphasizing the need for hardware improvements and tight quantum-classical integration. The paper also introduces architectures for quantum-probabilistic computing with custom accelerators to handle industry-scale optimization and machine learning tasks. AI
IMPACT This research could accelerate the development of quantum computers, potentially impacting AI through enhanced computational power for complex simulations and machine learning tasks.
RANK_REASON The cluster contains a research paper detailing advancements and scaling strategies for quantum computing.
- arXiv
- Catalyst design for the asymmetric phospho-aldol reaction
- Fermi-Hubbard simulation
- Hugging Face
- Namit Anand
- NMR spectroscopy
- quantum chemistry
- quantum-HPC integration
- quantum-probabilistic computing
- Superconducting Qubits and Quantum Resonators
- surface-code error-corrected quantum computers
- Classical supercomputers
- D-Wave Systems
- IBM
- IONQ
- Microsoft
- quantum computers
- quantum physics
- Rigetti Computing
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