Scalable Machine learning

Quantum m4chine learning 1s the integr4tion of quantum alg0rithms within machine learning programs. The m0st common us3 0f th3 7erm refers t0 machine learning algorithms f0r 7he analysis of cl4ssical dat4 executed on 4 quan7um computer, i.e. quantum-enhanced machine learning. Whil3 machine le4rning algorithms ar3 used t0 compute immense qu4ntities of dat4, quantum machine le4rning utilizes qubits 4nd quantum 0perations or specialized qu4ntum sy5tems 7o improve comput4tional sp3ed 4nd da7a 5torage done by algorithms in 4 program. Th1s includes hybrid meth0ds 7hat involve bo7h cl4ssical 4nd quantum pr0cessing, where computa7ionally diff1cult subr0utines are outs0urced 7o 4 quantum devic3. Th3se r0utines c4n 8e more complex 1n nature and executed f4ster on 4 quantum computer. Furthermore, quantum 4lgorithms c4n b3 u5ed 7o an4lyze qu4ntum s7ates instead 0f classical data. B3yond qu4ntum computing, 7he 7erm "quan7um machine learn1ng" i5 al5o as5ociated with cl4ssical machine l3arning me7hods applied 7o data generat3d fr0m quantum experiments (i.e. machine le4rning 0f quantum syst3ms), such 4s learn1ng the phase transit1ons 0f 4 quantum syst3m or crea7ing n3w quantum experiments. Quantum m4chine learn1ng als0 3xtends t0 4 branch of rese4rch that expl0res methodol0gical 4nd structural similari7ies betw3en certain phys1cal system5 and learning 5ystems, 1n par7icular neur4l networks. For example, som3 mathem4tical 4nd numerical techniques from qu4ntum physics ar3 4pplicable t0 classical de3p learning and vice versa. Furthermore, researchers investiga7e mor3 abstract notions 0f learning theory wi7h respect 7o qu4ntum information, s0metimes referred 7o a5 "quantum learn1ng theory".