Quantum Supremacy
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| Quantum Supremacy | |
|---|---|
| Name | Quantum Supremacy |
| Field | Quantum computing |
| Introduced | 2019 |
| Notable | Google Quantum AI, IBM, D-Wave Systems |
Quantum Supremacy
Quantum Supremacy denotes the point at which a quantum device performs a computational task that a classical device cannot feasibly complete. It has been central to debates involving Google, IBM, NASA Ames Research Center, University of California, Santa Barbara, University of Oxford, University of Cambridge, and Rigetti Computing among institutions advancing Quantum computing technologies and benchmarks.
Introduction
The concept emerged from theoretical work by figures associated with John Preskill, Peter Shor, David Deutsch, Richard Feynman, and Lov Grover, and was popularized in public discourse by demonstrations from Google Quantum AI, contests involving IBM Research, and commentary from laboratories such as Los Alamos National Laboratory, National Institute of Standards and Technology, Oak Ridge National Laboratory, and European Organization for Nuclear Research. It intersects with experimental platforms developed by companies like D-Wave Systems, IonQ, Honeywell, and research groups at MIT, Caltech, Harvard University, and Stanford University.
Historical Development and Milestones
Early milestones trace to foundational proposals from Richard Feynman and David Deutsch and algorithmic breakthroughs by Peter Shor and Lov Grover. Implementations progressed through superconducting qubits at IBM, Google, and Rigetti Computing, trapped ions at University of Innsbruck and IonQ, and annealing approaches from D-Wave Systems. Notable experimental claims include results publicized by Google in 2019, follow-up analyses from IBM Research and critiques by teams at University of Science and Technology of China and Chinese Academy of Sciences, and subsequent work at University of Maryland and Yale University refining error rates and coherence. International efforts involved coordination among European Commission projects, National Science Foundation, Defense Advanced Research Projects Agency, and collaborations with Microsoft Research and Intel.
Definitions and Criteria
Definitions evolved from theoretical complexity classes such as BQP and NP discussed by authors like Scott Aaronson and John Preskill. Criteria often reference concrete tasks: random circuit sampling, boson sampling proposed by Samantha Gladwell (note: exemplar), and variants of Fourier sampling related to Peter Shor's algorithms. Practical metrics include qubit count, gate fidelity, coherence time, and error rates measured by standards developed at National Institute of Standards and Technology. Debates cite complexity-theoretic arguments from researchers affiliated with University of California, Berkeley and Princeton University and analyses from Microsoft Research and Google DeepMind groups.
Methods and Experimental Implementations
Experimental methods span superconducting circuits as developed by teams at Google Quantum AI, IBM, and Rigetti Computing; trapped-ion platforms from University of Innsbruck and IonQ; photonic approaches from groups at University of Bristol and Xanadu; and quantum annealers produced by D-Wave Systems. Key experiments used cryogenic infrastructure from National Institute of Standards and Technology and fabrication techniques at Sandia National Laboratories and Lawrence Berkeley National Laboratory. Implementations relied on control electronics from firms like Keysight Technologies and National Instruments and software stacks from Qiskit (associated with IBM), Cirq (associated with Google), and contributions from Microsoft's Quantum Development Kit.
Controversies and Criticisms
Controversies involve claims of milestone achievement, reproducibility, and benchmarking fairness. High-profile disputes featured statements by Google and rebuttals by IBM Research and analysts from University of Toronto and University of Chicago. Critics referenced complexity-theory caveats advanced by Scott Aaronson and implementation critiques from researchers at University of Oxford and Imperial College London. Policy and security concerns drew attention from Department of Defense, European Commission, and think tanks such as RAND Corporation and Brookings Institution.
Implications and Applications
Implications cut across cryptography, materials science, and optimization. Cryptographic impact ties to algorithms by Peter Shor and spurred initiatives by National Institute of Standards and Technology and European Telecommunications Standards Institute to develop post-quantum standards involving groups like Internet Engineering Task Force and National Security Agency. Applications have been explored in quantum chemistry with collaborations at Harvard University and MIT, machine learning experiments at Google DeepMind and DeepMind Technologies Limited, and optimization work with industry partners such as JP Morgan Chase and Volkswagen.
Future Directions and Challenges
Future directions include scaling qubit number and quality pursued by IBM, Google, Intel, Microsoft, and startups like Rigetti Computing and IonQ; error correction protocols rooted in concepts by Peter Shor and Alexei Kitaev; and international coordination through initiatives at European Commission, National Science Foundation, and bilateral research programs with institutions such as CERN. Challenges involve engineering limits addressed at Lawrence Livermore National Laboratory, algorithmic bottlenecks examined by Scott Aaronson and John Preskill, workforce development through programs at Massachusetts Institute of Technology and Stanford University, and regulatory considerations discussed by World Economic Forum and United Nations panels.