Quantum Concept Learning and Hypothesis Classes
Concept learning asks how a learner identifies a rule from examples. How quantum learning theory treats hypothesis classes and sample complexity.
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739 articlesConcept learning asks how a learner identifies a rule from examples. How quantum learning theory treats hypothesis classes and sample complexity.
How quantum algorithms such as quantum SVMs and Grover-based methods are proposed for anomaly detection in security and finance, and their limits.
Decision forests combine many decision trees. How quantum versions have been proposed, how they compare with random forests and possible uses.
Boosting combines weak learners into a strong one. How quantum boosting algorithms work, their claimed speedups and possible applications.
What Meta is doing about quantum computing: skepticism about near-term use, and its migration toward post-quantum cryptography.
IonQ builds quantum computers from trapped ions. How its hardware works, its fidelity and connectivity, cloud access and applications.
D-Wave Systems has built quantum annealers since 1999, from D-Wave One to Advantage2. Its hardware, software, customers and gate-model work.
Quantum computing companies listed on the NYSE and Nasdaq, such as IonQ, D-Wave, Rigetti and IBM, and the risks of investing in them.
Google’s quantum program: the 2019 Sycamore supremacy result, later processors, error correction milestones and what the claims mean.
IBM’s quantum roadmap compared with what it has delivered: Eagle, Heron, Condor, Quantum System Two, Qiskit and targets to 2033.
The quantum apocalypse is the day quantum computers break RSA and ECC. Realistic timelines, sector risks and the move to post-quantum cryptography.
In a store now, decrypt later attack, encrypted data is collected today to be decrypted once quantum computers can break it. Who is at risk.