The future of computing is now with Jay Gambetta - IBM Think 2026
Summary
IBM Think 2026 highlights significant advancements in quantum computing, emphasizing its current utility in solving complex scientific problems previously intractable for classical supercomputers. IBM Research is orchestrating breakthroughs across silicon, quantum, AI, and algorithms, manufacturing quantum chips using 300-mm wafer technology in a 24/7 fab. Since putting the first quantum computer on the cloud in 2016, IBM has fostered a global ecosystem, releasing Qiskit, the first quantum SDK, in 2017. In 2023, quantum systems achieved 2,880 operations on 100 qubits, and a fault-tolerant quantum computer is projected for 2029. The concept of quantum-centric supercomputing, integrating CPUs, GPUs, and QPUs, is enabling real-world applications like the SQD algorithm for molecular simulation. Cleveland Clinic has used this to simulate proteins with over 12,000 atoms, achieving 210 times accuracy improvement in drug discovery. Oak Ridge National Laboratory is applying quantum-centric methods to optimize tritium extraction for fusion energy, using AI for fragment selection and quantum computing for high-precision chemistry. Q-CTRL and IBM have also announced "practical quantum advantage" in material science, demonstrating a 3,000 times speedup (2 minutes vs. 100 hours on a classical cluster) for a 120-qubit problem involving 10,000 two-qubit operations.
Key takeaway
For AI Scientists and Research Scientists working on intractable problems in chemistry, materials science, or drug discovery, IBM's quantum-centric supercomputing offers a viable path to solutions. You should explore hybrid quantum-classical algorithms like SQD and embedded wave function theory to tackle complex simulations, as these methods are demonstrating practical quantum advantage and superior accuracy compared to classical approaches, enabling breakthroughs in areas like protein-ligand binding and fusion energy.
Key insights
Quantum-centric supercomputing, integrating classical and quantum hardware, is achieving practical advantage in complex scientific problems.
Principles
- Integrate quantum and classical computing for optimal problem-solving.
- Open access to quantum hardware accelerates research and development.
- Focus on real-world problems, not just theoretical possibilities.
Method
The SQD algorithm combines quantum computation for input generation with classical computation for iterative refinement, enabling accurate molecular energy state determination. This hybrid approach leverages each processor type's strengths.
In practice
- Simulate large proteins for drug discovery with high accuracy.
- Optimize chemical processes for fusion energy applications.
- Achieve significant speedups in material science simulations.
Topics
- Quantum Computing
- Quantum-Centric Supercomputing
- Drug Discovery
- Fusion Energy
- Practical Quantum Advantage
Best for: AI Scientist, Research Scientist, Director of AI/ML
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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Research.