Circuit-model blind quantum computation with key decoupling
Peer-Reviewed Publication
Updates every hour. Last Updated: 20-Oct-2026 02:16 ET (20-Oct-2026 06:16 GMT/UTC)
Researchers have developed a key-decoupled circuit-model blind quantum computation protocol that enhances privacy protection in delegated quantum computing while reducing the operational burden on the client. By independently generating encryption and decryption keys, the protocol avoids gate-by-gate propagation of decryption information and conceals the target operation within its Pauli equivalence class. A low-overhead statistical verification strategy based on randomly sampled Pauli observables is further introduced to detect deviations by the quantum server. Proof-of-principle experiments on an IBM superconducting quantum processor, together with multi-qubit numerical simulations, demonstrate the feasibility and scalability of the approach for privacy-preserving quantum cloud computing.
Hebrew University ranks 17th worldwide in Communication, 24th in Mathematics, 36th in Law and 44th in Public Administration; seven subjects place among the global top 100 and 13 among the top 150
Graph neural networks have been used in various graph-related tasks, where they have achieved excellent performance. However, incomplete graph structures, commonly found in real-life scenarios, can severely degrade performance. In a new study, researchers develop a novel graph completion network that reconstructs missing features and structure simultaneously while also maintaining performance with extremely weak supervision, which is difficult to achieve with conventional models.
Catalytic Applications of Carbon Nitride-Based Nanomaterials presents a focused overview of the synthesis, properties and catalytic applications of graphitic carbon nitride (g-C₃N₄)-based nanomaterials in modern chemistry. With their structural versatility, chemical stability and tunable electronic properties, carbon nitride-based materials have attracted growing interest as catalysts for organic synthesis, photocatalysis, electrocatalysis and environmental applications.
A review, published on September 11 in Cyborg and Bionic Systems by researchers at Tianjin University, summarizes recent advances in intracortical microstimulation (ICMS), a technique that uses tiny electrodes implanted in the brain to activate specific groups of neurons. The researchers discuss how ICMS has evolved from a tool for mapping brain function into a potential technology for sensory feedback, information delivery, closed-loop neuromodulation and biointegrated neural interfaces.
Brain–computer interfaces are increasingly being explored as a way to restore lost functions or provide new channels for communication between the nervous system and electronic devices. While much BCI research focuses on reading neural signals, ICMS provides the complementary ability to write information back into the brain.
“ICMS has emerged as a prominent invasive modality capable of activating intracortical neuronal populations with high precision,” the researchers write. The technology can potentially replace or supplement natural sensory inputs, probe
dysfunctional neural circuits and influence behavior.