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Google Accelerates Quantum Computation with Classical Machine Learning

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Tech giant Google's recent claim regarding quantum supremacy created a buzz in the computer science community and got global mainstream media talking about quantum computing breakthroughs. Yesterday Google fed the public's growing interest in the topic with a blog post introducing a study on improving quantum computation using classical machine learning. The qubit is the most basic constituent of quantum computing, and also poses one of the most significant challenges for the realization of near-term quantum computers. Various characteristics of qubits have made it challenging to control them. Google AI explains that issues such as imperfections in the control electronics can "impact the fidelity of the computation and thus limit the applications of near-term quantum devices."


Artificial intelligence in quantum systems, too

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Quantum biomimetics consists of reproducing in quantum systems certain properties exclusive to living organisms. Researchers at University of the Basque Country have imitated natural selection, learning and memory in a new study. The mechanisms developed could give quantum computation a boost and facilitate the learning process in machines. Unai Alvarez-Rodriguez is a researcher in the Quantum Technologies for Information Science (QUTIS) research group attached to the UPV/EHU's Department of Physical Chemistry, and an expert in quantum information technologies. Quantum information technology uses quantum phenomena to encode computational tasks.


Artificial intelligence in quantum systems, too

#artificialintelligence

Quantum biomimetics consists of reproducing in quantum systems certain properties exclusive to living organisms. Researchers at University of the Basque Country have imitated natural selection, learning and memory in a new study. The mechanisms developed could give quantum computation a boost and facilitate the learning process in machines. Unai Alvarez-Rodriguez is a researcher in the Quantum Technologies for Information Science (QUTIS) research group attached to the UPV/EHU's Department of Physical Chemistry, and an expert in quantum information technologies. Quantum information technology uses quantum phenomena to encode computational tasks.


Improving quantum computation with classical machine learning

#artificialintelligence

Quantum computers aren't constrained to two states; they encode data as quantum bits, or qubits, which can exist in superposition. Qubits represent, particles, photons or electrons, and their respective control devices that are working together to act as computer memory and a processor. Qubits can interact with anything nearby that carries energy close to their own, for example, photons, phonons, or quantum defects, which can change the state of the qubits themselves. Manipulating and controlling out qubits is performed through old-style controls: pure signal as electromagnetic fields coupled to a physical substrate in which the qubit is implanted, e.g., superconducting circuits. Defects in these control electronics, from external sources of radiation, and variances in digital-to-analog converters, introduce even more stochastic errors that degrade the performance of quantum circuits.


Improving quantum computation with classical machine learning

#artificialintelligence

Quantum computers aren't constrained to two states; they encode data as quantum bits, or qubits, which can exist in superposition. Qubits represent, particles, photons or electrons, and their respective control devices that are working together to act as computer memory and a processor. Qubits can interact with anything nearby that carries energy close to their own, for example, photons, phonons, or quantum defects, which can change the state of the qubits themselves. Manipulating and controlling out qubits is performed through old-style controls: pure signal as electromagnetic fields coupled to a physical substrate in which the qubit is implanted, e.g., superconducting circuits. Defects in these control electronics, from external sources of radiation, and variances in digital-to-analog converters, introduce even more stochastic errors that degrade the performance of quantum circuits.