A blueprint for demonstrating quantum supremacy with superconducting qubits
| Autor: | C. Neill, P. Roushan, K. Kechedzhi, S. Boixo, S. V. Isakov, V. Smelyanskiy, A. Megrant, B. Chiaro, A. Dunsworth, K. Arya, R. Barends, B. Burkett, Y. Chen, Z. Chen, A. Fowler, B. Foxen, M. Giustina, R. Graff, E. Jeffrey, T. Huang, J. Kelly, P. Klimov, E. Lucero, J. Mutus, M. Neeley, C. Quintana, D. Sank, A. Vainsencher, J. Wenner, T. C. White, H. Neven, J. M. Martinis |
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| Rok vydání: | 2018 |
| Předmět: |
Superconductivity
Quantum Physics Multidisciplinary Computer science Computation FOS: Physical sciences Macroscopic quantum phenomena 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Quantum technology Delocalized electron symbols.namesake Qubit 0103 physical sciences symbols Statistical physics Quantum Physics (quant-ph) 010306 general physics 0210 nano-technology Hamiltonian (quantum mechanics) Quantum |
| Zdroj: | Science. 360:195-199 |
| ISSN: | 1095-9203 0036-8075 |
| DOI: | 10.1126/science.aao4309 |
| Popis: | Fundamental questions in chemistry and physics may never be answered due to the exponential complexity of the underlying quantum phenomena. A desire to overcome this challenge has sparked a new industry of quantum technologies with the promise that engineered quantum systems can address these hard problems. A key step towards demonstrating such a system will be performing a computation beyond the capabilities of any classical computer, achieving so-called quantum supremacy. Here, using 9 superconducting qubits, we demonstrate an immediate path towards quantum supremacy. By individually tuning the qubit parameters, we are able to generate thousands of unique Hamiltonian evolutions and probe the output probabilities. The measured probabilities obey a universal distribution, consistent with uniformly sampling the full Hilbert-space. As the number of qubits in the algorithm is varied, the system continues to explore the exponentially growing number of states. Combining these large datasets with techniques from machine learning allows us to construct a model which accurately predicts the measured probabilities. We demonstrate an application of these algorithms by systematically increasing the disorder and observing a transition from delocalized states to localized states. By extending these results to a system of 50 qubits, we hope to address scientific questions that are beyond the capabilities of any classical computer. |
| Databáze: | OpenAIRE |
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