Ahoy pirates, entangle your seatbelts because this week we are simulating our quantum ship. Oak Ridge National Laboratory's Quantum Computing User Program (QCUP) launched a Request for Information (RFI) to enhance quantum research through stakeholder collaboration, aiming to optimize infrastructure and scale access. Knoxville trying to position itself as a quantum hub. Meanwhile, Nvidia's is pushing their GPUs to simulate efficiently quantum systems, and also working with Google Quantum AI to enhance quantum processor design, focusing on noise management.. Not in vain, AlphaQubit from Google DeepMind uses AI transformers architecture for reducing or removing quantum errors. Is this the true usage of ML into Quantum? A great paper gives an overview on where we are now. At the same time we ask ourselves, can AI improve ML algorithms? Inspired classical algorithms had reduced quantum advantages to polynomial with a lot of doubts in the community on existing exponential quantum speedups with classical data. Grønlund and Larsen's work specifically proves this separation for solving linear systems with well-conditioned (i.e. depending on the dataset and the matrix sparsity!). So it seems that QML for error correction and design and optimize the chips or estimating and tomography methods —> YES. Running a better support vector machine? Try harder classically. Dutch startup Fermioniq introduced Ava, a quantum emulation product on NVIDIA's CUDA-Q platform, enabling the design and testing of quantum algorithms on more qubits than current hardware allows. In company news, Microsoft and Atom Computing unveiled a commercial quantum machine with 24 entangled logical qubits, marking a significant step towards integrating quantum computing with AI. Their research demonstrated logical computation using a neutral atom quantum processor with 256 Ytterbium qubits. Quantinuum launched the Nexus platform for quantum computing solutions, while announcing a partnership with Infineon Technologies AG to develop next-generation ion traps.
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The Week in Quantum Computing - November 25th…
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Ahoy pirates, entangle your seatbelts because this week we are simulating our quantum ship. Oak Ridge National Laboratory's Quantum Computing User Program (QCUP) launched a Request for Information (RFI) to enhance quantum research through stakeholder collaboration, aiming to optimize infrastructure and scale access. Knoxville trying to position itself as a quantum hub. Meanwhile, Nvidia's is pushing their GPUs to simulate efficiently quantum systems, and also working with Google Quantum AI to enhance quantum processor design, focusing on noise management.. Not in vain, AlphaQubit from Google DeepMind uses AI transformers architecture for reducing or removing quantum errors. Is this the true usage of ML into Quantum? A great paper gives an overview on where we are now. At the same time we ask ourselves, can AI improve ML algorithms? Inspired classical algorithms had reduced quantum advantages to polynomial with a lot of doubts in the community on existing exponential quantum speedups with classical data. Grønlund and Larsen's work specifically proves this separation for solving linear systems with well-conditioned (i.e. depending on the dataset and the matrix sparsity!). So it seems that QML for error correction and design and optimize the chips or estimating and tomography methods —> YES. Running a better support vector machine? Try harder classically. Dutch startup Fermioniq introduced Ava, a quantum emulation product on NVIDIA's CUDA-Q platform, enabling the design and testing of quantum algorithms on more qubits than current hardware allows. In company news, Microsoft and Atom Computing unveiled a commercial quantum machine with 24 entangled logical qubits, marking a significant step towards integrating quantum computing with AI. Their research demonstrated logical computation using a neutral atom quantum processor with 256 Ytterbium qubits. Quantinuum launched the Nexus platform for quantum computing solutions, while announcing a partnership with Infineon Technologies AG to develop next-generation ion traps.