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Natural Disasters prediction with quantum algorithm
- 1. THE 8TH KVIS-ISF (2025) NATURALDISASTER PREDICTION WITH QUANTUM ALGORITHM
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- 4. CONTENTS : OBEJECTI VE FOUNDATIO N METHODOLO GY RESULT Ff 01 03 05 02 04 Backgroundand problems statement Quantum computing fundamental and Quantum Machine Learning (QML) The purpose of doing this research The Process of performing the prediction CONCLU SION 06 INTRODUC TION Accuracy and the lead time Future research
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- 6. Machine Learning (ML)is a part of AI that enables systems to learn and get better from experience on their own, by identifying patterns in data and making predictions. Machine Learning Figure: AI and ML Relation But LIke, I have shown you, that the data keeps increasing year by year! With so call, Big Data
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- 15. 2.OBJECT IVE • enhance naturaldisasters prediction with more accurate result with longer duration before the disaster occurs. • Human can be well prepared before the disaster. • It can save millions of lives one earth from tragical natural disasters.
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- 19. Quantum Computers: Fundamentals, Applicationsand Implementation Ben Feldman, Harvard University Big Techday Conference June 14, 2013 ˆ z = |0 > -z = |1 > x y θ φ |ψ > ˆ ˆ ˆ ˆ
- 20. Standard Computers Classical bits+ Logic and Memory = Computer 0 1 + =
- 21. Classical Bits • Canbe only 0 or 1 OR Qubits • Superposition of both 0 and 1 • Any quantum two-level system can act as a qubit, e.g. ⚬Atoms ⚬Spins Quantum Bits (qubits) AND
- 22. Quantum Bits (Qubits) z= |0 > -z = |1 > x y θ φ |ψ > ˆ ˆ ˆ ˆ Quantum Bit: |ψ > = cos(θ/2)|0> + e φsin(θ/2) ᶦ |1> = a|0> + b|1> Any vector pointing to the surface of the sphere is a valid quantum state! §This allows for superposition and interference to happen, providing enormous computational power.
- 23. Classical search Searching anunsorted list classically: no better way than guess and check (order N)
- 24. Quantum search: Grover’sAlgorithm Quantum search: order N1/2 Quantum Computer A ➢ computer that uses laws of quantum mechanics to perform massively parallel computing through superposition, entanglement, and decoherence.
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- 30. After encoding weput the encoded data into the model that we have chosen
- 33. Support Vector Machines:Slide <number> Linear Classifiers f x yᵉˢᵗ denotes +1 denotes -1 f(x,w,b) = sign(w. x - b) How would you classify this data?
- 34. Support Vector Machines:Slide <number> Linear Classifiers f x yᵉˢᵗ denotes +1 denotes -1 f(x,w,b) = sign(w. x - b) How would you classify this data?
- 35. Support Vector Machines:Slide <number> Linear Classifiers f x yᵉˢᵗ denotes +1 denotes -1 f(x,w,b) = sign(w. x - b) How would you classify this data?
- 36. Support Vector Machines:Slide <number> Linear Classifiers f x yᵉˢᵗ denotes +1 denotes -1 f(x,w,b) = sign(w. x - b) Any of these would be fine.. ..but which is best?
- 37. Support Vector Machines:Slide <number> Maximum Margin f x yᵉˢᵗ denotes +1 denotes -1 f(x,w,b) = sign(w. x - b) The maximum margin linear classifier is the linear classifier with the, um, maximum margin. This is the simplest kind of SVM (Called an LSVM) Support Vectors are those datapoints that the margin pushes up against Linear SVM
- 38. Support vector machines separating hyperplane.. …in higher- dimensional feature space Still(algebraic) optimization over hyperplane and feature function parameters…. 16
- 39. Lastly, by usinga technique known as kernel trick, SVM can separate data which is not linearly separable in its input space. This technique enables SVM to transform input data into higher-dimen- sional space, where a separating linear hyperplane can be found.
- 40. After encoding weput the encoded data into the model that we have chosen
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- 43. RESU LT This is theresults which is derived from earthquake prediction
- 44. CONCLUSION Further research inthe future: • This research can be fully applied once quantum computers are fully developed. • Update this research by making the prediction to process more faster and more accurate .
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