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.
In a major breakthrough in quantum computing, scientists from Griffith University and the University of Queensland in Australia announced Monday that they had found a way to simplify a complicated logic operation by creating a quantum "Fredkin gate" for the first time ever. The development, detailed in the journal Science Advances, could bring fully functional quantum computers closer to reality. "Much like our everyday computer, the brains of a quantum computer consist of chains of logic gates, although quantum logic gates harness quantum phenomena," Raj Patel, from the Griffith University's Centre for Quantum Dynamics, said in a statement. "Similar to building a huge wall out lots of small bricks, large quantum circuits require very many logic gates to function. However, if larger bricks are used the same wall could be built with far fewer bricks."
Scientists have succeeded for the first time in entangling two separate qubits by connecting them via a cable, in a breakthrough that will likely accelerate the creation of quantum networks – which, by combining the capabilities of several quantum devices, could boost the potential of the technology even in its current limited state. The researchers, from the University of Chicago's Cleland Lab, created two quantum nodes, themselves containing three superconducting qubits each. Using a one-meter-long superconducting cable to connect the nodes, the scientists then chose one qubit in each node and entangled them together by sending so-called "entangled quantum states" through the cable. Taking the form of microwave photons, these entangled quantum states are extremely fragile, which makes the process particularly challenging; but the researchers nevertheless managed to transfer the entanglement from one node to the other, linking the qubits into a special quantum state that is still both fascinating and confounding to quantum scientists. Qubits, or quantum bits, are the basic unit of quantum information, and their properties can be exploited to create next-generation quantum technologies; one of those properties is entanglement.
This blog post is an overview of quantum machine learning written by the author of the paper Bayesian deep learning on a quantum computer. In it, we explore the application of machine learning in the quantum computing space. The authors of this paper hope that the results of the experiment help influence the future development of quantum machine learning. With no shortage of research problems, education programs, and demand for talent, machine learning is one of the hottest topics in technology today. Parallel to the success of learning algorithms, the development of quantum computing hardware has accelerated over the last few years.
In this superconducting quantum chip, each of the nine cross-shaped qubits is connected to its neighbors and individually controlled. Google engineers have found a way to make the company's D-Wave quantum computer more scalable and capable of solving problems in multiple fields. According to Nature, Google has created a device that blends analog and digital approaches to deliver enough quantum bits, or qubits, to create a scalable, multi-purpose quantum computer, capable of solving chemistry and physics problems by, for example, simulating molecules at the quantum level. The analog approach, or adiabatic quantum computing (AQC), underpins the D-Wave quantum computer Google bought a few years ago. But, as Nature notes, errors can't be corrected as systematically as they can on digital circuits.