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.
An AWS quantum hardware engineer works on a dilution refrigerator. The performance of superconducting quantum devices relies heavily on precise wiring configurations to minimise thermal fluctuations that contribute to noise. Cloud computing giant AWS is expanding its presence the quantum industry with the opening of a shiny new Center for Quantum Computing in California. Here, top academics and engineers will be working to build the company's very own superconducting quantum computer. Located northeast of Los Angeles in Pasadena, the two-storey, 21,000-square-foot facility was first announced by AWS in 2019 and has been built over the past two years in partnership with the neighbouring California Institute of Technology (Caltech).
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.
It is a law of physics that everything that is not prohibited is mandatory. They are everywhere: in language, cooking, communication, image processing and, of course, computation. Mitigating and correcting them keeps society running. You can scratch a DVD yet still play it. QR codes can be blurred or torn yet are still readable. Images from space probes can travel hundreds of millions of miles yet still look crisp. Error correction is one of the most fundamental concepts in information technology.
Google has unveiled its new Quantum AI campus in Santa Barbara, California, where engineers and scientists will be working on its first commercial quantum computer – but that will probably be a decade way. The new campus has a focus on both software and hardware. On the latter front, these include its first quantum data center, quantum hardware research labs, and Google's own quantum processor chip fabrication facilities, says Erik Lucero, lead engineer for Google Quantum AI in a blogpost. Quantum computers offer great promise for cryptography and optimization problems. ZDNet explores what quantum computers will and won't be able to do, and the challenges we still face.