An Approach to Interfacing the Brain with Quantum Computers: Practical Steps and Caveats

We report on the first proof-of-concept system demonstrating how one can control a qubit with mental activity. We developed a method to encode neural correlates of mental activity as instructions for a quantum computer. Brain signals are detected utilising electrodes placed on the scalp of a person, who learns how to produce the required mental activity to issue instructions to rotate and measure a qubit. Currently, our proof-of-concept runs on a software simulation of a quantum computer. At the time of writing, available quantum computing hardware and brain activity sensing technology are not sufficiently developed for real-time control of quantum states with the brain. But we are one step closer to interfacing the brain with real quantum machines, as improvements in hardware technology at both fronts become available in time to come. The paper ends with a discussion on some of the challenging problems that need to be addressed before we can interface the brain with quantum hardware.Ministerio de Ciencia, Innovación y Universidades PID2019-104002GB-C21Junta de Andalucía P20-00617Shanghai’s Municipality, China 2019SHZDZX01-ZX04 and 20DZ229090

Quantum Brain Networks: A Perspective

We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integrating knowledge and methods from neurotechnology, artificial intelligence, and quantum computing. The objective is to develop an enhanced connectivity between the human brain and quantum computers for a variety of disruptive applications. We foresee the emergence of hybrid classical-quantum networks of wetware and hardware nodes, mediated by machine learning techniques and brain– machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science, technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans, intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business

An approach to interfacing the brain with quantum computers: practical steps and caveats

We report on the first proof-of-concept system demonstrating how one can control a qubit with mental activity. We developed a method to encode neural correlates of mental activity as instructions for a quantum computer. Brain signals are detected utilizing electrodes placed on the scalp of a person, who learns how to produce the required mental activity to issue instructions to rotate and measure a qubit. Currently, our proof-of-concept runs on a software simulation of a quantum computer. At the time of writing, available quantum computing hardware and brain activity sensing technology are not sufficiently developed for real-time control of quantum states with the brain. But we are one step closer to interfacing the brain with real quantum machines, as improvements in hardware technology at both fronts become available in time to come. The paper ends with a discussion on some of the challenging problems that need to be addressed before we can interface the brain with quantum hardware

Quantum Brain Networks: A Perspective

We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integratingknowledge and methods from neurotechnology, artificial intelligence, and quantum computing. Theobjective is to develop an enhanced connectivity between the human brain and quantum computersfor a variety of disruptive applications. We foresee the emergence of hybrid classical-quantumnetworks of wetware and hardware nodes, mediated by machine learning techniques and brain–machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science,technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans,intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business.European Union (EU) QMiCS (820505) and OpenSuperQ (820363) projectsSpanish GovernmentPGC2018-095113-B-I00, PID2019-104002GB-C21, PID2019-104002GB-C22 (MCIU/AEI/FEDER, UE)Basque Government IT986-16Junta de Andalucía (P20-00617 andUS-1380840)National Natural Science Foundation of China (NSFC)(12075145), STCSM (2019SHZDZX01-ZX04, 18010500400 and 18ZR1415500