Focused verticals: automotive, aerospace, chemistry, pharmaceuticals, defense.Near term applications research: quantum simulation, discrete optimization, quantum machine learning.Maximum qubits: 72 physical qubits ( announcement).The purpose of this system is to provide a testbed for research into system error rates and scalability of Google qubit technology, as well as applications in quantum simulation, optimization, and machine learning. Google’s research focuses mainly on Bristlecone, a quantum processor based on superconducting qubits. Specifically, it creates the Bell state, or EPR pair. The following series of image shows a very basic experiment that creates entanglement between two qubits so that the state of the system cannot be determined by looking at individual qubits. Then the algorithm can be simulated (3) or scheduled to run in the quantum computer (4).
#Microsoft win quantum error after all code#
Quantum algorithms can be designed with a graphical user interface (1) or through the Qasm code (2). IBM Q Experience is a cloud-hosted service to experiment with a 5-qubit quantum computer. Near term applications research: quantum simulation, quantum heuristics for optimization, quantum machine learning.Maximum qubits: 50 physical qubits ( announcement, pictures).Computation Model: Universal Gate Model.IBM’s research focuses on superconducting qubits, an approach that leverages IBM’s pre-existing experience with semiconductors and silicon wafer technology. Specifically, I will focus on tech leaders like IBM, Google, Intel, and Microsoft. This week I will review the players investing R&D in quantum computing to contrast their approach, current state and future goals. This is the third article following Quantum Computers: Optimism and Pessimism and Quantum Computers: Models of Computation.
It’s Quantum Wednesday! I decided to write a weekly article on quantum computing, such an exciting field in rapid R&D acceleration.
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