Science

Researchers have developed an improved decoding algorithm for 3D color codes, pushing the boundaries of fault-tolerant quantum computation.

Researchers are exploring a radical departure from traditional cryptographic methods, building security on the principles of higher-arity operations and non-derived algebraic structures.

A new quantum machine learning approach dramatically improves the detection of credit card fraud, particularly in challenging, real-world scenarios.

A new analysis reveals the delicate balance between leveraging molecular symmetry for efficient quantum simulations and maintaining the flexibility needed for accurate ground-state energy calculations.

Researchers are exploring how machine learning can enhance the security and efficiency of quantum key distribution systems.

A new concept, QoeSiGN, proposes a collaborative approach to qualified electronic signatures, mitigating risks and enhancing user control.

Researchers have developed a new scheme allowing secure keyword searches and computations on encrypted data stored in mobile cloud environments, even in the face of quantum computing threats.

New research reveals that even modest machine learning models can effectively memorize cryptographic keys, creating a powerful new threat to widely-used encryption standards.

Researchers are demonstrating how parity codes, inherent in many quantum error correction schemes, can streamline multi-qubit gate operations and accelerate the development of practical quantum computers.

A new multi-agent AI system, Quantigence, offers a dynamic platform for organizations to assess and mitigate the evolving risks of the post-quantum cryptography transition.