Quantum Computing Guide for Business Leaders
Three key use cases with the most potential to transform industries
As technology progresses, a quantum-powered future is an increasingly likely scenario. Business leaders believe that quantum computing will help them tackle new challenges, boost operational effectiveness and accelerate the resolution of complex problems. Explore this report for insights on promising applications for optimization, machine learning and simulation across industries.
Key takeaways:
- Quantum isn’t a faster classical computer; it’s a different tool entirely. The report emphasizes quantum’s counterintuitive physics and that it’s not a “super” supercomputer; it may solve currently unsolvable problems while potentially not helping with simple ones.
- Three use cases are emerging as the near-term “center of gravity.” Optimization, machine learning and simulation are three primary areas where quantum could revolutionize industries as capabilities mature.
- The ecosystem is diverse—platform tradeoffs matter. There are multiple approaches to building quantum computers being explored, like superconducting qubits, trapped ions, neutral atoms, photonics and annealers; each has its own tradeoffs (speed vs. stability; flexibility vs. scalability) and plays a different the role of error mitigation.
Quantum is counterintuitive
Traditional computers use lightning-fast math to solve problems from spreadsheets to artificial intelligence, whereas quantum computers derive their power from the counterintuitive physics of atoms. Since they perform calculations differently, quantum computers may be able to solve currently unsolvable problems, while potentially being unable to solve "simple" problems. They are not “super” supercomputers, but something entirely new.
Top three promising applications
Quantum computing is expected to significantly impact areas such as:
- Optimization: Quantum computing can quickly explore and evaluate large spaces of potential solutions, finding optimal or near-optimal solutions efficiently.
- Machine learning: Quantum algorithms potentially can train models more quickly and accurately than traditional computing.
- Simulation: Quantum computers can model complex systems at a deeper level than classical models, providing insights that are beyond the reach of current classical computational methods.
Building quantum computers
There are many ways to build a quantum computer. Each approach would bring different trade-offs, considerations, and potential benefits for a quantum-powered future. The primary approaches being explored today include superconducting qubits, silicon dots, trapped ions, neutral atoms, photonic systems, topological qubits and quantum annealers.
