Quantum bootstrapping via compressed quantum Hamiltonian learning
A major problem facing the development of quantum computers or large scale quantum simulators is that general methods for characterizing and controlling are intractable. We provide a new approach to this problem that uses small quantum simulators to efficiently characterize and learn control models...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2015-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/17/2/022005 |
| _version_ | 1797751229185523712 |
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| author | Nathan Wiebe Christopher Granade D G Cory |
| author_facet | Nathan Wiebe Christopher Granade D G Cory |
| author_sort | Nathan Wiebe |
| collection | DOAJ |
| description | A major problem facing the development of quantum computers or large scale quantum simulators is that general methods for characterizing and controlling are intractable. We provide a new approach to this problem that uses small quantum simulators to efficiently characterize and learn control models for larger devices. Our protocol achieves this by using Bayesian inference in concert with Lieb–Robinson bounds and interactive quantum learning methods to achieve compressed simulations for characterization. We also show that the Lieb–Robinson velocity is epistemic for our protocol, meaning that information propagates at a rate that depends on the uncertainty in the system Hamiltonian. We illustrate the efficiency of our bootstrapping protocol by showing numerically that an 8 qubit Ising model simulator can be used to calibrate and control a 50 qubit Ising simulator while using only about 750 kilobits of experimental data. Finally, we provide upper bounds for the Fisher information that show that the number of experiments needed to characterize a system rapidly diverges as the duration of the experiments used in the characterization shrinks, which motivates the use of methods such as ours that do not require short evolution times. |
| first_indexed | 2024-03-12T16:44:30Z |
| format | Article |
| id | doaj.art-65797875e8884315b601ae219fea385f |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:44:30Z |
| publishDate | 2015-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-65797875e8884315b601ae219fea385f2023-08-08T14:17:04ZengIOP PublishingNew Journal of Physics1367-26302015-01-0117202200510.1088/1367-2630/17/2/022005Quantum bootstrapping via compressed quantum Hamiltonian learningNathan Wiebe0Christopher Granade1D G Cory2Quantum Architectures and Computation Group , Microsoft Research, Redmond, WA 98052, USADepartment of Physics, University of Waterloo , Ontario N2L 3G1, Canada; Institute for Quantum Computing, University of Waterloo , Ontario N2L 3G1, CanadaInstitute for Quantum Computing, University of Waterloo , Ontario N2L 3G1, Canada; Department of Chemistry, University of Waterloo , Ontario N2L 3G1, Canada; Perimeter Institute, University of Waterloo , Ontario N2L 2Y5, Canada; Canadian Institute for Advanced Research , Toronto, Ontario M5G 1Z8, CanadaA major problem facing the development of quantum computers or large scale quantum simulators is that general methods for characterizing and controlling are intractable. We provide a new approach to this problem that uses small quantum simulators to efficiently characterize and learn control models for larger devices. Our protocol achieves this by using Bayesian inference in concert with Lieb–Robinson bounds and interactive quantum learning methods to achieve compressed simulations for characterization. We also show that the Lieb–Robinson velocity is epistemic for our protocol, meaning that information propagates at a rate that depends on the uncertainty in the system Hamiltonian. We illustrate the efficiency of our bootstrapping protocol by showing numerically that an 8 qubit Ising model simulator can be used to calibrate and control a 50 qubit Ising simulator while using only about 750 kilobits of experimental data. Finally, we provide upper bounds for the Fisher information that show that the number of experiments needed to characterize a system rapidly diverges as the duration of the experiments used in the characterization shrinks, which motivates the use of methods such as ours that do not require short evolution times.https://doi.org/10.1088/1367-2630/17/2/022005quantum informationcharacterizationLieb–Robinson boundsmachine learningquantum simulation |
| spellingShingle | Nathan Wiebe Christopher Granade D G Cory Quantum bootstrapping via compressed quantum Hamiltonian learning New Journal of Physics quantum information characterization Lieb–Robinson bounds machine learning quantum simulation |
| title | Quantum bootstrapping via compressed quantum Hamiltonian learning |
| title_full | Quantum bootstrapping via compressed quantum Hamiltonian learning |
| title_fullStr | Quantum bootstrapping via compressed quantum Hamiltonian learning |
| title_full_unstemmed | Quantum bootstrapping via compressed quantum Hamiltonian learning |
| title_short | Quantum bootstrapping via compressed quantum Hamiltonian learning |
| title_sort | quantum bootstrapping via compressed quantum hamiltonian learning |
| topic | quantum information characterization Lieb–Robinson bounds machine learning quantum simulation |
| url | https://doi.org/10.1088/1367-2630/17/2/022005 |
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