Improving quantum computation with classical machine learning

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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.


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."


Roadmap for 1000 Qubits Fault-tolerant Quantum Computers - Amit Ray

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How many qubits are needed to out-perform conventional computers, how to protect a quantum computer from the effects of decoherence and how to design more than 1000 qubits fault-tolerant large scale quantum computers, these are the three basic questions we want to deal in this article. Qubit technologies, qubit quality, qubit count, qubit connectivity and qubit architectures are the five key areas of quantum computing are discussed. Earlier we have discussed 7 Core Qubit Technologies for Quantum Computing, 7 Key Requirements for Quantum Computing. Spin-orbit Coupling Qubits for Quantum Computing and AI, Quantum Computing Algorithms for Artificial Intelligence, Quantum Computing and Artificial Intelligence, Quantum Computing with Many World Interpretation Scopes and Challenges and Quantum Computer with Superconductivity at Room Temperature. Here, we will focus on practical issues related to designing large-scale quantum computers.


About Google's Self-Proclaimed Quantum Supremacy and its Impact on Artificial Intelligence - KDnuggets

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Last week, Google sparked controversy in the scientific community by claiming that it has achieved the anticipated milestone known as quantum supremacy. In a paper published in Nature, Google described the experiments conducted on a new quantum machine, code named Sycamore, which proof the famous benchmark. It only took a few hours for IBM, Google's archrival in the race towards quantum dominance, to publish a paper refuting Google's claims sparking a passionate debate within the computer science community. Despite the controversy surrounding Google's claims, there is no doubt that the release of Sycamore represents a major milestone to demonstrate the viability of quantum systems and it has profound ramifications across other technology fields. In the case of artificial intelligence(AI), there has been a lot of speculation in terms of how the advent of quantum computing will affect AI programs.