Benchmarking Neural Networks For Quantum Computations
The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. This means that we do not know for which calcu...
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| Published in: | IEEE transaction on neural networks and learning systems Vol. 31; no. 7; pp. 2522 - 2531 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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IEEE
01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2162-237X, 2162-2388, 2162-2388 |
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| Abstract | The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. This means that we do not know for which calculations there will be a "quantum advantage," once an algorithm is found. One way to answer the question is to find those algorithms, but finding truly quantum algorithms turns out to be very difficult. In previous work, over the past three decades, we have pursued the idea of using techniques of machine learning to develop algorithms for quantum computing. Here, we compare the performance of standard real- and complex-valued classical neural networks with that of one of our models for a quantum neural network, on both classical problems and on an archetypal quantum problem: the computation of an entanglement witness. The quantum network is shown to need far fewer epochs and a much smaller network to achieve comparable or better results. |
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| AbstractList | The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. This means that we do not know for which calculations there will be a “quantum advantage,” once an algorithm is found. One way to answer the question is to find those algorithms, but finding truly quantum algorithms turns out to be very difficult. In previous work, over the past three decades, we have pursued the idea of using techniques of machine learning to develop algorithms for quantum computing. Here, we compare the performance of standard real- and complex-valued classical neural networks with that of one of our models for a quantum neural network, on both classical problems and on an archetypal quantum problem: the computation of an entanglement witness. The quantum network is shown to need far fewer epochs and a much smaller network to achieve comparable or better results. The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. This means that we do not know for which calculations there will be a "quantum advantage," once an algorithm is found. One way to answer the question is to find those algorithms, but finding truly quantum algorithms turns out to be very difficult. In previous work, over the past three decades, we have pursued the idea of using techniques of machine learning to develop algorithms for quantum computing. Here, we compare the performance of standard real- and complex-valued classical neural networks with that of one of our models for a quantum neural network, on both classical problems and on an archetypal quantum problem: the computation of an entanglement witness. The quantum network is shown to need far fewer epochs and a much smaller network to achieve comparable or better results.The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. This means that we do not know for which calculations there will be a "quantum advantage," once an algorithm is found. One way to answer the question is to find those algorithms, but finding truly quantum algorithms turns out to be very difficult. In previous work, over the past three decades, we have pursued the idea of using techniques of machine learning to develop algorithms for quantum computing. Here, we compare the performance of standard real- and complex-valued classical neural networks with that of one of our models for a quantum neural network, on both classical problems and on an archetypal quantum problem: the computation of an entanglement witness. The quantum network is shown to need far fewer epochs and a much smaller network to achieve comparable or better results. |
| Author | Steck, J. E. Behrman, E. C. Nguyen, Nam H. Moustafa, Mohamed A. |
| Author_xml | – sequence: 1 givenname: Nam H. surname: Nguyen fullname: Nguyen, Nam H. organization: Department of Mathematics and Physics, Wichita State University, Wichita, KS, USA – sequence: 2 givenname: E. C. orcidid: 0000-0002-6195-9122 surname: Behrman fullname: Behrman, E. C. email: elizabeth.behrman@wichita.edu organization: Department of Mathematics and Physics, Wichita State University, Wichita, KS, USA – sequence: 3 givenname: Mohamed A. surname: Moustafa fullname: Moustafa, Mohamed A. organization: Department of Aerospace Engineering, Wichita State University, Wichita, KS, USA – sequence: 4 givenname: J. E. surname: Steck fullname: Steck, J. E. organization: Department of Aerospace Engineering, Wichita State University, Wichita, KS, USA |
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| Cites_doi | 10.1111/j.1469-1809.1936.tb02137.x 10.1103/PhysRevA.64.022319 10.1080/00107514.2014.964942 10.1109/SFCS.1994.365700 10.1007/BF02650179 10.1162/089976603321891846 10.1007/978-3-642-20353-4 10.1103/PhysRevLett.114.110504 10.1007/s10994-012-5316-5 10.1007/s11128-017-1692-x 10.1103/PhysRevLett.78.2275 10.1016/j.asoc.2016.04.024 10.1063/1.445732 10.1103/PhysRevX.8.021050 10.1016/S1076-5670(08)70147-2 10.1103/PhysRevA.94.022342 10.1145/237814.237866 10.1103/PhysRevLett.104.063603 10.1088/1367-2630/17/2/022005 10.1126/science.1252319 10.1103/PhysRevA.54.1098 10.1007/BF02551274 10.1137/S0097539796300921 10.1103/PhysRevLett.80.2245 10.1103/PhysRevA.81.052319 10.1016/j.swevo.2013.11.002 10.1109/ICASSP.2001.941159 |
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| SubjectTerms | Algorithms Benchmark testing Benchmarking Biological neural networks complex neural network complexity Complexity theory Computer simulation Computers entanglement Learning algorithms Machine learning Neural networks quantum computation Quantum computers Quantum computing Quantum entanglement quantum machine learning quantum neural network |
| Title | Benchmarking Neural Networks For Quantum Computations |
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