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QC Ware Races Ahead With Breakthrough in Quantum Machine Learning Algorithms

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"QC Ware estimates that with Forge Data Loaders, the industry's 10-to-15-year timeline for practical applications of QML will be reduced significantly," said Yianni Gamvros, Head of Product and Business Development at QC Ware. "What our algorithms team has achieved for the quantum computing industry is equivalent to a quantum hardware manufacturer introducing a chip that is 10 to 100 times faster than their previous offering. This exciting development will require business analysts to update their quad charts and innovation scouts to adjust their technology timelines." Apart from the Forge Data Loaders, the latest release of Forge includes tools for GPU acceleration, which allows algorithms testing to be completed in seconds versus hours, and turnkey algorithms implementations on a choice of simulators and quantum hardware. Quantum hardware integrations include D-Wave Systems, and IonQ and Rigetti architectures through Amazon Braket.


QC Ware Touts Breakthrough in Quantum Machine Learning Algorithms

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PALO ALTO, Calif., July 22, 2020 – QC Ware, provider of enterprise software and services for quantum computing, announced a significant breakthrough in quantum machine learning (QML) that increases QML accuracy and speeds up the industry timeline for practical QML applications on near-term quantum computers. QC Ware's algorithms researchers have discovered how classical data can be loaded onto quantum hardware efficiently and how distance estimations can be performed quantumly. These new capabilities enabled by Data Loaders are now available in the latest release of QC Ware's Forgecloud services platform, an integrated environment to build, edit, and implement quantum algorithms on quantum hardware and simulators. "QC Ware estimates that with Forge Data Loaders, the industry's 10-to-15-year timeline for practical applications of QML will be reduced significantly," said Yianni Gamvros, Head of Product and Business Development at QC Ware. "What our algorithms team has achieved for the quantum computing industry is equivalent to a quantum hardware manufacturer introducing a chip that is 10 to 100 times faster than their previous offering. This exciting development will require business analysts to update their quad charts and innovation scouts to adjust their technology timelines."


QC Ware Announces Quantum Machine Learning Breakthrough

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QC Ware defines itself as a quantum computing-as-a-service company that builds enterprise solutions to run on quantum computing hardware. It recently announced several significant breakthroughs in quantum machine learning (QML). Machine learning is a subset of artificial intelligence. It has evolved from simple work in the 1950s to today's deep learning that uses sophisticated training and neural networks. Machine learning allows a computer to make decisions based on what it learns about the characteristics of large data sets.


Commercialize early quantum technologies

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Google's cryostats reach temperatures of 10 millikelvin to run its quantum processors. From aspects of quantum entanglement to chemical reactions with large molecules, many features of the world cannot be described efficiently with conventional computers based on binary logic. The solution, as physicist Richard Feynman realized three decades ago1, is to use quantum processors that adopt a blend of classical states simultaneously, as matter does. Many technical hurdles must be overcome for such quantum machines to be practical, however. These include noise control and improving the fidelity of operations acting on the quantum states that encode the information.


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.