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Quantum K means Algorithm
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Quantum algorithms are being extensively researched nowadays seeing thepotential of providing exponential speed up when compared to classical algorithmexecution. This speed-up can play a big role in machine learning wheretraining a model is usually very slow. Training a machine learning model requiresmanipulating large vectors and quantum computers inherently are reallyfast at manipulating and computing large vectors and tensor products.However, current quantum computers have certain limitations with respect toqubit’s coherence times and noise. These barriers reduce their effectiveness insolving problems with high accuracy. In pursuit of having better results on currentterm noisy quantum computers, implementations of quantum algorithmswith simpler circuits are desired. To address this problem, three different novelmethods of the quantum version of the K-means clustering algorithm are presentedwith optimized shallow depth quantum circuit design. Experimentalresults on the quantum computer IBMQX2 are demonstrated which show asignificant improvement in the accuracy of the quantum K-means algorithm.

Abstract [sv]

Forskning inom kvantalgoritmer har ökat kraftig under de senaste åren p.g.a. en lovande exponentiell speed up för visa beräkningar som kräver en mängd stor data och tar en lång tida i en klassikt dator. Den här speed up:en kan spela en stor roll i maskininlärning där träningen av en modell är vanligtvis mycket långsam. Träning av en maskininlärningsmodell kräver att man manipulerar stora vektorer och kvantdatorer är i själva verket snabba att manipulera och beräkna stora vektorer och tensorprodukter. Nuvarande kvantdatorer har emellertid vissa begränsningar med avseende på qubit-koherens och brus. Dessa hindrar deras effektivitet vid att lösa problem med hög noggrannhet. I strävan efter att ha bättre resultat på de befintliga brus-påverkade kvantdatorer, är det önskvärt att implementera kvantalgoritmer med enklare kvantkretsar. Vi adresserar problematiken med tre olika nya metoder för kvant K-means algorithm som har en optimerad grunddjupkvantkretsdesign. Experimentella resultat på kvantmaskinen IBMQX2 visas och e visar en betydande förbättringav precision hos kvant K-means algoritmen.

Place, publisher, year, edition, pages
2019. , p. 64
Series
TRITA-EECS-EX ; 2019:618
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-266107OAI: oai:DiVA.org:kth-266107DiVA, id: diva2:1381305
External cooperation
Ericsson AB
Supervisors
Examiners
Available from: 2019-12-20 Created: 2019-12-20 Last updated: 2019-12-20Bibliographically approved

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