Machine Learning In Quantum Information And Other Physics

Based on probability,statistics and newest computer science,machine learning is a new subject which could improve the computer algorithm automatically by huge data or other experiences.It has already achieved several progress in many subjects,like image classification,time series prediction or self-control.Quantum machine learning is the integrated discipline combining quantum mechanics and machine learning,including two directions: using quantum computing to accelerate machine learning algorithm,or using classical machine learning to help solve the difficult problems lie on quantum physics.In this thesis,we focus on the second direction,and make one more step to explore the application of machine learning on other physics problems.Quantum entanglement is the central resources in quantum information and quantum computing.However,even in small systems contain two or three qubits,the task of classifying entangled states from separable states is still difficult and resources-consuming.By introducing artificial neural network,we proved the classification power of machine learning in this problem.Convolution neural network can extract local information and combine them into high-order information.Here a modified convolution layer is proposed to map the convolution progress into the projection measurement in quantum field,which could find the suitable measurement basis for certain problems.By this method,we can not only classify the quantum state,but also apply into other problems,like prediction entanglement entropy,or realize quantum state tomography by auto encoder structure.To transfer quantum information quickly and efficiently is a key to achieve scalable quantum computing,and quantum state transfer on one-dimension spin chain is one of the hot problems in quantum information fields.There are several traditional results,but each of them has their disadvantages.Reinforcement learning,one of the machine learning method,can automatically learning control protocols on certain problem even without human labelled data.This thesis introduced the reinforcement learning method and another classical machine learning method to propose two quantum transfer protocols,time-independent version and time-dependent version,and each of them has improved the result of original protocol.Rayleigh-Benard convection experiment plays an important part in fluid mechanics.The convection state label is the basis of researching other problems,which cost a lot of time and attention.The machine learning method can reduce the time into only a few seconds or even less,and has the ability of generation into other systems.