Quantum classifier with tailored quantum kernel

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作者
Carsten Blank
Daniel K. Park
June-Koo Kevin Rhee
Francesco Petruccione
机构
[1] Data Cybernetics,School of Electrical Engineering
[2] KAIST,ITRC of Quantum Computing for AI
[3] KAIST,Quantum Research Group
[4] School of Chemistry and Physics,undefined
[5] University of KwaZulu-Natal,undefined
[6] National Institute for Theoretical Physics (NITheP),undefined
来源
npj Quantum Information | / 6卷
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摘要
Kernel methods have a wide spectrum of applications in machine learning. Recently, a link between quantum computing and kernel theory has been formally established, opening up opportunities for quantum techniques to enhance various existing machine-learning methods. We present a distance-based quantum classifier whose kernel is based on the quantum state fidelity between training and test data. The quantum kernel can be tailored systematically with a quantum circuit to raise the kernel to an arbitrary power and to assign arbitrary weights to each training data. Given a specific input state, our protocol calculates the weighted power sum of fidelities of quantum data in quantum parallel via a swap-test circuit followed by two single-qubit measurements, requiring only a constant number of repetitions regardless of the number of data. We also show that our classifier is equivalent to measuring the expectation value of a Helstrom operator, from which the well-known optimal quantum state discrimination can be derived. We demonstrate the performance of our classifier via classical simulations with a realistic noise model and proof-of-principle experiments using the IBM quantum cloud platform.
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共 72 条
[1]  
Schuld M(2015)An introduction to quantum machine learning Contemp. Phys. 56 172-185
[2]  
Sinayskiy I(2017)Quantum machine learning Nature 549 195 EP-1220
[3]  
Petruccione F(2018)Machine learning & artificial intelligence in the quantum domain: a review of recent progress Rep. Prog. Phys. 81 074001-212
[4]  
Biamonte J(2008)Kernel methods in machine learning Ann. Stat. 36 1171-252
[5]  
Dunjko V(2019)Quantum machine learning in feature Hilbert spaces Phys. Rev. Lett. 122 040504-14
[6]  
Briegel HJ(2019)Supervised learning with quantum-enhanced feature spaces Nature 567 209-296
[7]  
Hofmann T(2017)Implementing a distance-based classifier with a quantum interference circuit EPL (Europhys. Lett.) 119 60002-473
[8]  
Schölkopf B(2001)Quantum fingerprinting Phys. Rev. Lett. 87 167902-510
[9]  
Smola AJ(2019)Circuit-based quantum random access memory for classical data Sci. Rep. 9 083024-undefined
[10]  
Schuld M(2019)Parallel quantum trajectories via forking for sampling without redundancy N. J. Phys. 21 231-undefined