Qubit layout selection determining which physical qubits a logical circuit occupies on a superconducting processor is fundamentally a noise-field sampling problem. On IBM Heron r1 and Heron r2 devices, two-qubit gate error varies by nearly one order of magnitude (7-9×) across qubit pairs on the same chip, causing measured fidelity differences up to 0.903 from layout choice alone. We introduce the Gate Noise Sensitivity Index (GNSI), a calibration-weighted linear combination of per-qubit readout error and per-edge two-qubit gate error, derived analytically as a first-order approximation of circuit fidelity under independent, heterogeneous noise channels. For 12 backend-circuit combinations on three IBM processors (133-qubit Heron r1 and 156-qubit Heron r2), GNSI scores are computed for the full pool of 1,320 computationally enumerated candidate layouts; the top-ranked (GNSI-BEST) and bottom-ranked (GNSI-WORST) layouts are then executed on hardware. GNSI-BEST ranks first by score in all 12 instances (Hit@1 = 100% within the enumerated candidate pool) and consistently achieves higher measured fidelity than GNSI-WORST, with Spearman rank correlation ρ = 0.926±0.016 and statistically significant fidelity separation in 9 of 12 paired tests. Initial hardware comparisons on ibm_fez (n = 5) show GNSI-BEST matching or exceeding SABRE-selected fidelity. We further identify a depth-dependent regime transition: gate-error heterogeneity appears to govern ranking in shallow circuits (depth ≤ 15), while qubit-specific T1 variation may dominate for deeper circuits (depth≥ 20). The analytic regime boundary d*(b), computable from calibration data alone, shows agreement within ±2 depth layers across all three backends in initial comparisons; GNSI-Extended incorporating the decoherence term receives an initial hardware comparison on two circuits, with full validation left to future work. These results suggest that layout-dependent fidelity variation may be governed by a calibration-predictable noise field, with direct implications for compiler design and noise-adaptive quantum circuit execution.

This paper presents STGA-Net (Spatio-Temporal Graph Attention Network), a deep learning framework designed for accurate short-term traffic flow prediction in complex Indian urban environments. Standard models such as ARIMA, CNN, and LSTM often fail to effectively represent dynamic spatial dependencies among interconnected road segments. The proposed model employs Graph Attention Networks (GAT) for adaptive spatial learning and Long Short-Term Memory (LSTM) units for modeling temporal sequences to address this issue. One big thing that makes this work is the use of correlation-based adaptive adjacency learning, which lets the model figure out how road segments are related to each other in real time, not just on static road maps. Embeddings of day-of-week and other time-based features can also help find traffic patterns that happen over and over again.The model was tested on a real-world traffic dataset from Bangalore and showed stable convergence and better predictive performance. The results of the experiment show that STGA-Net has a Mean Absolute Error (MAE) of 0.6536 and a Root Mean Squared Error (RMSE) of 0.8544, which means that it can reliably make short-term predictions. The model also got a (R2) score of 0.2390, which shows that it can capture important changes in space and time. These results show that STGA-Net works well for smart city and intelligent traffic management.

Blockchain-enabled telehealth systems with cryptographic protection for physiological sensor data, energy constraints of implant-class wearables, and GDPR compliance have revolutionized patient monitoring and data management. However, AES encryption through Ethereum's LSTM-based ECG prediction exhibits critical problems, such as prohibitive blockchain costs, single-patient generalization failure, invalidated wearable energy, absence of key recovery, lack of EHR interoperability, unmeasured end-to-end latency, no collusion resistance, and the conflict of the GDPR Article 17. This paper proposes a new Hyperledger Fabric RAFT consortium blockchain, FedAvg of federated learning, quantized TCN INT8 key generation, 3-of-5 Shamir Secret Sharing to protect sensitive data, HL7 FHIR R4 gateway, hash-based ZK commitment for authentication, six-stage pipeline benchmarking for throughput, and chameleon hash to erasure GDPR. Hyperledger Fabric RAFT achieves 63.3± 4.1 ms block commit latency at 0.00 per-transaction fee (98.4% improvement). FedAvg federated learning on the MIT-BIH ECG yields 71.8% reduction in MAE on three nodes without raw data transmission. A quantized TCN INT8 model reduces the scaled ARM Cortex-M4 inference energy from 22.50μ J (BeDaSH estimate) to 0.06μ J (MLPerf Tiny scaled estimate), a 99.7% reduction, with all 1000 generated keys confirmed unique on the measurement platform. A 3-of-5 Shamir scheme over F2127-1) enables key recovery in 37.7μs. HL7 FHIR R4 validation yields 10/10 HTTP 201 responses. Cryptographic pipeline benchmarking shows a P50 latency of 2.7 ms and 317.5 TPS at ten concurrent sessions; including the Fabric commit, full-path P50 is 66.0 ms. ZK-proof authentication blocks 100/100 collusion attacks. Chameleon hash erasure implements the recommended architectural pattern of EDPB Guidelines 02/2025 for GDPR Article 17 compliance in 100 patient records.

To investigate the process of cube attack in the world of wireless networks, Nikolaos Petrakos modeled E0 encryption function and implemented cube attack on E0 by performing linear testing which results the number of operations for computational process is 2 21.1. The output of the LFSRs has ended with 12 superpolys or linear coefficients based on the following unknown variables as x5, x6, x7, x8, x9, x10, x11, x12. This way 8 variables out of 16 variables are computed. In this paper, by using Nikolaos Petrakos modeled E0 encryption function and implemented the process of cube attack on E0 by performing quadraticity testing. The output of the LFSRs has ended with 23 superpolys or quadratic coefficients, based on the following unknown variables as x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16. Finally, we were able to compute all the 16 variables with the same time complexity of 2 21.1.

Lin Ding presented improved related cipher attack (IRCA) on 256-bit key Salsa20, by considering same secret key with two separate IVs used in Salsa20/12 and Salsa20/8 and recovered secret key with time complexity 2 193.58. Presented an algorithm to find unknown key words based on exhaustive search for 256-bit Salsa20. In this paper, similar to Lin Ding, IRCA on 128- bit key Salsa20 is applied by considering same secret key with one IV used in Salsa20/12 and Salsa20/10 and recovered secret key with time complexity 2 96. To achieve this, one forward round from (R10(m)) to (R11(m)) and one reverse round from (R12(m))-1 to (R11(m))-1 is applied. The same idea is utilized to find the unknown key words based on exhaustive search for 128-bit key Salsa20. Similarly, IRCA on 256-bit key ChaCha is applied by considering same secret key with two separate IVs used in ChaCha4 and ChaCha8 and secret key is recovered with time complexity 2 225. To achieve this, two forward round from (R4(m)) to (R6(m)) and two reverse round from (R8(m))-1 to (R6(m))-1 is applied. The same idea is utilized to find the unknown key words based on exhaustive search for 256-bit key ChaCha.

Maitra et al. (WCC-2015) proposed the characterisation of valid states by reversing the one round of Salsa20. When revisited, a mistake was found in the one bit change of eighth and ninth word while reversing the one round result to a valid initial state. It was mentioned in WCC-2015 that it would be an interesting combinatorial problem to characterise all such states. Thus, nine more values were characterised, leading to valid initial states. Aumasson et al. (FSE-2008) attacked 128-bit key Salsa20/7 with 2111 time and ChaCha6 with 2107 time. In this study, the attack was improved on 128-bit key Salsa20/7 with 2107 time and ChaCha6 with 2102 time. Maitra (DAM-2016) improved the attack on 256-bit key Salsa20/8 and ChaCha7 by choosing the proper initialisation vectors. In congruence with this, 128-bit key Salsa20/7 was attacked with 2104 time and ChaCha6 with 2101 time. Choudhuri and Maitra (FSE 2017) developed theoretical results on the differential-linear cryptanalysis and thus improved the biases on Salsa/ChaCha. Theoretical work had been extended with triple bits from m − 1 round to one bit m round of Salsa with the linear approximation holding the probability 1. In consideration of the linear approximation which holds the probability <1, linear approximation for three rounds from m to m + 3 for Salsa and ChaCha was exhibited.

Stream cipher is one of the basic cryptographic primitives that provide the confidentiality of communication through insecure channel. EU ECRYPT network has organized a project for identifying new stream suitable for widespread adoption where the ciphers can provide a more security levels. Finally the result of the project has identified new stream ciphers referred as eSTREAM. Salsa20 is one of the eSTREAM cipher built on a pseudorandom function. In this paper our contribution is two phases. First phase have two parts. In WCC 2015, Maitra et al. [9] explained characterization of valid states by reversing one round of Salsa20. In first part, we have revisited the Maitra et al. [9] characterization of valid states by reversing one round of Salsa20. We found there is a mistake in one bit change in 8th and 9th word in first round will result in valid initial state. In second part, Maitra et al. [9] as mentioned that it would be an interesting combinatorial problem to characterize all such states. We have characterized nine more values which lead to valid initial states. The combinations (s4, s7), (s2, s3), (s13, s14), (s1, s6), (s1, s11), (s1, s12), (s6, s11), (s6, s12) and (s11, s12) which characterized as valid states. In second phase, FSE 2008 Aumasson et al. [1] attacked 128-key bit of Salsa20/7 within 2111 time and ChaCha6 in within 2107 time. After this with best of our knowledge there does not exist any improvement on this attack. In this paper we have attacked 128-key bit of Salsa20/7 within 2107 time and ChaCha6 within 2102 time. Maitra [8] improved the attack on Salsa20/8 and ChaCha7 by choosing proper IVs corresponding to the 256-key bit. Applying the same concept we have attacked 128-key bit of Salsa20/7 within time 2104 and ChaCha7 within time 2101.