Microchips power almost every modern device \u2014 phones, laptops and even fridges. But behind the scenes, making them is a complex process. But researchers say they have found a way to tap into the power of quantum computing to make it simpler.<\/p>\n
Scientists in Australia have developed a quantum machine learning technique \u2014 a blend of artificial intelligence<\/a> (AI) and quantum computing<\/a> principles \u2014 that could change how microchips are made.<\/p>\n <\/p>\n They outlined their findings in a new study published June 23 in the journal Advanced Science<\/a>. In it, the researchers demonstrated for the first time how quantum machine learning algorithms can significantly improve the challenging process of modeling the electrical resistance inside a chip \u2014 a key factor that affects how efficiently it performs.<\/p>\n You may like<\/p>\n Quantum machine learning is a hybrid approach that combines classical data with quantum computing methods. In classical computing, data is stored in bits encoded as a 0 or 1. Quantum computers use qubits and, thanks to principles like superposition and entanglement, qubits can exist in multiple states simultaneously \u2014 so two qubits can be 00, 01, 10 and 11 simultaneously.<\/p>\n This allows quantum computing systems to process complex mathematical relationships much faster than classical systems \u2014 with parallel processing scaling up exponentially the more qubits you add to a system<\/p>\n Quantum machine learning takes classical data and encodes it in quantum states. The quantum computer can then uncover patterns in the data that would be hard for classical systems to detect. A classical system then takes over to interpret the results or apply them.<\/p>\n Related: ‘Quantum AI’ algorithms already outpace the fastest supercomputers, study says<\/a><\/p>\n