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Oracle Launches Java 27 Featuring Hybrid Post-Quantum Cryptography for TLS 1.3
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Oracle Launches Java 27 Featuring Hybrid Post-Quantum Cryptography for TLS 1.3

Oracle Launches Java 27 Featuring Hybrid Post-Quantum Cryptography for TLS 1.3 Oracle (NYSE: ORCL) has announced the general availability of Java 27 (Oracle JDK 27), introducing significant security, runtime, and developer updates across the Java ecosystem. A central focus of the release is advancing enterprise post-quantum cryptography (PQC) readiness to protect global cloud, microservice, and enterprise applications against “harvest now, decrypt later” security threats. Heading the security updates is JEP 527: Post-Quantum Hybrid Key Exchange for TLS 1.3, which integrates hybrid key exchange algorithms natively into the javax.net.ssl APIs. By combining classical key exchange with quantum-resistant key encapsulation mechanisms, Java 27 enables default quantum protection for network communications without requiring application code rewrites. Additionally, Oracle announced Oracle Jipher 20, now included in the Oracle Java Verified Portfolio (JVP), which wraps a FIPS 140-3 validated OpenSSL cryptographic module supporting NIST-standardized ML-KEM (Kyber) and ML-DSA (Dilithium) algorithms. [ Java 27 PQC & Cryptographic Security Enhancements ]Enhancement / FeatureImplementation MechanismSecurity & Operational BenefitJEP 527 (Hybrid TLS 1.3)Native integration via javax.net.ssl standard APIsDefends TLS network streams against future quantum decryption without breaking legacy compatibility.Oracle Jipher 20FIPS 140-3 validated OpenSSL module in Java Verified PortfolioAdds native support for NIST ML-KEM and ML-DSA algorithms in regulated enterprise environments.JEP 538 (PEM Encodings)Third preview for encoding cryptographic objectsStreamlines management and interoperability of quantum-safe keys, certificates, and CRLs. Beyond security enhancements, JDK 27 incorporates performance and language optimizations, including JEP 534 (Compact Object Headers by Default) to reduce JVM heap footprints and JEP 523 (G1 Default Garbage Collector Across All Environments). The update

Sep 17, 2026

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QiT: Quantum-Inspired Transformer for Visual Recognition Taskquantum-computing

QiT: Quantum-Inspired Transformer for Visual Recognition Task

--> Quantum Physics arXiv:2609.17789 (quant-ph) [Submitted on 15 Sep 2026] Title:QiT: Quantum-Inspired Transformer for Visual Recognition Task Authors:Badri N. Patro, Vijay Agneeswaran View a PDF of the paper titled QiT: Quantum-Inspired Transformer for Visual Recognition Task, by Badri N. Patro and Vijay Agneeswaran View PDF HTML (experimental) Abstract:Quantum machine learning offers a compelling representational perspective: angle-encoded states inhabit Hilbert spaces in which periodic similarities and interactions can be expressed naturally. Realizing this perspective for visual recognition remains difficult, however, because present quantum neural networks are constrained by limited qubit counts, costly circuit simulation and measurement, noise, and unstable optimization on noisy intermediate-scale quantum devices. We investigate whether useful structural ideas from quantum models can instead be realized as scalable classical Transformer operations. We introduce QiT, a Quantum-inspired Transformer for vision tasks with three components: (i) angle-inspired encoding that maps image tokens to learned trigonometric Hilbert-space features analogous to quantum rotation-based state encoding; (ii) self-attention over these periodic features, inducing a classical cosine kernel approximated to quantum fidelity kernels; and (iii) gated multiplicative emulation, a trainable classical surrogate for interaction terms found in variational circuits. All components are differentiable tensor operations, so QiT claims neither quantum computation nor quantum speedup and retains the $\mathcal{O}(N^2D)$ attention complexity of a standard Vision Transformer. Across image-classification benchmarks, QiT is competitive with a matched classical Transformer while avoiding the severe runtime cost observed for a small simulated quantum Transformer. QiT-B reaches 78.3\% ImageNet-1K top-1 accuracy with 45.7M parameters and 11.5 GFLOPs. These results position QiT as a scalable baseline for iso

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Depth analysis of the Quantum Approximate Optimization Algorithm with a Grover mixerquantum-computing

Depth analysis of the Quantum Approximate Optimization Algorithm with a Grover mixer

--> Quantum Physics arXiv:2609.17835 (quant-ph) [Submitted on 15 Sep 2026] Title:Depth analysis of the Quantum Approximate Optimization Algorithm with a Grover mixer Authors:Bojko N. Bakalov, Dimitar Grantcharov View a PDF of the paper titled Depth analysis of the Quantum Approximate Optimization Algorithm with a Grover mixer, by Bojko N. Bakalov and Dimitar Grantcharov View PDF HTML (experimental) Abstract:We study the Quantum Approximate Optimization Algorithm with a Grover mixer and independently sampled cost and mixing angles. Under a lattice condition on the cost values carried by the initial state, we prove a depth-independent lower bound for the variance of the loss at every depth, together with the same bound for the derivative with respect to the final mixing angle. The estimate is instance-dependent and, for fixed locality, is inverse polynomial in the number of qubits for integer-valued local objective functions with uniformly bounded local terms, in particular for MaxCut. We also establish Grover-type reachability bounds showing that the depth required to approximate a prescribed carried eigenspace is bounded below by a constant multiple of the inverse square root of its initial probability. Comments: Subjects: Quantum Physics (quant-ph); Mathematical Physics (math-ph) Cite as: arXiv:2609.17835 [quant-ph]   (or arXiv:2609.17835v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2609.17835 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Bojko Bakalov [view email] [v1] Tue, 15 Sep 2026 20:53:43 UTC (34 KB) Full-text links: Access Paper: View a PDF of the paper titled Depth analysis of the Quantum Approximate Optimization Algorithm with a Grover mixer, by Bojko N. Bakalov and Dimitar GrantcharovView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-09 Change to browse by: math math-ph math.MP

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Explore India's ₹6,003 Crore quantum initiative: 4 thematic hubs, leading startups, and the latest developments in India's quantum ecosystem

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