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+and+learning+if+they+are+related+to+one+another.+For+this%252C+we+develop+a+geometric+quantum+machine+learning+(GQML)+approach+with+embedded+symmetries+that+allows+for+the+classification+of+similar+and+dissimilar+pairs+based+on+global+correlations%252C+and+enables+generalization+from+just+a+few+samples.+Unlike+GQML+algorithms+developed+to+date%252C+we+propose+to+focus+on+symmetry-aware+measurement+adaptation+that+outperforms+unitary+parametrizations.+We+compare+GQML+for+similarity+testing+against+classical+deep+neural+networks+and+convolutional+neural+networks+with+Siamese+architectures.+We+show+that+quantum+networks+largely+outperform+their+classical+counterparts.+We+explain+this+difference+in+performance+by+analyzing+correlated+distributions+used+for+composing+our+dataset.+We+relate+the+similarity+testing+with+problems+that+showcase+a+proven+maximal+separation+between+the+BQP+complexity+class+and+the+polynomial+hierarchy.+While+the+ability+to+achieve+advantage+largely+depends+on+how+data+are+loaded%252C+we+discuss+how+similar+problems+can+benefit+from+quantum+machine+learning.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.fb9631e8dbab405fa595cfff02575446%3frik%3dNUYNrpX4o3Hwfg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Quantum+Machine+Learning+Classification&FORM=IRPRST&ck=B56BBADA04FFBA100B8E50ADA6A06575&selectedIndex=24&itb=0)
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