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- Quantum Machine Learning - IBM Research
We now know that quantum computers have the potential to boost the performance of machine learning systems, and may eventually power efforts in fields from drug discovery to fraud detection We’re doing foundational research in quantum ML to power tomorrow’s smart quantum algorithms
- Opening a new era in algorithmic development - IBM Research
Opening a new era in algorithmic development IBM Research is turbocharging algorithm development for a world with quantum computing and AI Algorithms have always been IBM Research’s superpower Since the dawn of modern computing, IBM scientists have time and again redefined how humanity computes
- Olivia Lanes - IBM Research
Olivia Lanes is the Global Lead for Quantum Education and Learning at IBM Quantum, where she drives strategy, content development, and advocacy to empower researchers and students worldwide
- Quantum Machine Learning: An Interplay Between Quantum Computing and . . .
This paper presents an overview of quantum computing for the machine learning paradigm, where variational quantum circuits (VQC) are used to develop QML architectures on noisy intermediate-scale quantum (NISQ) devices We discuss machine learning for the quantum computing paradigm, showcasing our recent theoretical and empirical findings
- When Machine Learning Meets Quantum Computers: A Case Study - IBM Research
When Machine Learning Meets Quantum Computers: A Case Study for ASP-DAC 2021 by Weiwen Jiang et al
- IBM researchers find mathematical proof of potential quantum advantage . . .
Quantum kernels can solve machine learning problems that are hard for all classical methods IBM researchers have found mathematical proof of a potential quantum advantage for quantum machine learning For a certain class of machine learning problems, a quantum computer can see patterns where a classical computer would only see random noise
- Entanglement-enhanced learning of quantum noise at scale
Learning unknown processes affecting a quantum system reveals underlying physical mechanisms and enables suppression, mitigation, and correction of unwanted effects Generally, learning quantum processes requires exponentially many measurements We show how entanglement with an ideal auxiliary quantum memory can provide an exponential advantage in learning certain quantum processes In
- The Qiskit Global Summer School is returning with a focus on Quantum . . .
Last year, the IBM Quantum team made history by hosting a free, virtual quantum computing crash course for over 4,000 learners This year, we’re hoping to host another 4,000 students — now with a focus on quantum machine learning (QML)
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