Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

• DOI: 10.1103/physrevapplied.8.024030
• Publication Date: 2017-08-30T16:33:47Z
• Authors: Keisuke Fujii; Kohei Nakajima
• Keywords: Learning rule

The authors describe an alternative to digital quantum computation that uses natural dynamics for information processing. $Q\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}m$ $r\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}s\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}v\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}r$ $c\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}m\phantom{\rule{0}{0ex}}p\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}g$ does not require fine tuning of parameters, is robust against noise, and based on existing devices. Simulations suggest with this approach, a system just 5 7 qubits as powerful recurrent neural network hundreds nodes. This framework artificial intelligence powered by physics enables $t\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}m\phantom{\rule{0}{0ex}}p\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}l$ machine-learning tasks, such language processing predicting the stock market.