New research demonstrates how efficiently solving lattice problems in specialized ‘Simultaneous Approximation’ lattices can improve the performance of cryptographic systems.
A new framework leverages the power of GPUs to accelerate authenticated encryption, enabling secure and high-throughput access to massive astronomical catalogs.
A new approach to disaster recovery leverages deterministic identifiers to overcome the performance bottlenecks of cryptographic hashing in large-scale storage systems.
![The framework defines a twist operator within a general space of operators [latex]G[/latex], specifically isolating those that preserve the scar subspace [latex]SGA[/latex] and those maintaining the commutants or individual scar wavefunctions [latex]C[/latex], thereby enabling detection of underlying symmetry types.](https://arxiv.org/html/2602.22397v1/2602.22397v1/Spt.png)
New research demonstrates that these unusual quantum states possess a surprising topological character, opening doors for advanced quantum technologies.
![A complete implementation of a hypercube-based quantum walk, utilizing the Qiskit framework, demonstrates a walk parameterized by [latex]P=3[/latex], a single time step [latex]t=1[/latex], an initial state of [latex]\ket{\psi\_{0}}=\ket{2}=\ket{(10)\_{2}}[/latex], a coin operator [latex]F=Y[/latex], and rotation angles [latex]\phi=0[/latex] and [latex]\theta=\pi/4[/latex].](https://arxiv.org/html/2602.23261v1/2602.23261v1/figures/qw_hypercube_sample_qiskit_c.png)
A new protocol leverages hypercube-based quantum walks to enhance the security and robustness of one-way quantum key distribution systems.
Researchers have developed a clustered quantum secure aggregation protocol to address the challenges of implementing privacy-enhancing technologies in near-term quantum devices.
This review examines how threshold homomorphic encryption is enabling privacy-preserving average aggregation in distributed learning environments.
Researchers have developed a novel framework that moves beyond traditional anomaly detection by incorporating explicitly defined security policies to improve the accuracy and interpretability of encrypted traffic analysis.
A new review explores the surprising mathematical foundations and emerging applications of codes designed for maximum data recovery, even in the face of significant errors.
Researchers are integrating uncertainty directly into machine learning models to achieve more reliable predictions of challenging physical phenomena like the critical heat flux.