Hybrid Variational Quantum Algorithms (VQAs) present a highly viable pathway to near-term quantum utility; however, their performance is fundamentally bottlenecked by classical-quantum communication latency in Quantumas-a-Service (QaaS) environments.
This paper proposes an optimized classical-quantum orchestration architecture designed to minimize cloud-induced latency and maximize Quantum Processing Unit (QPU) active compute time. By implementing edge-colocated classical optimizers alongside batched parameter-shift gradient evaluations, the system circumvents stateless cloud API barriers.
Benchmarking across parameterized quantum circuits ranging from 15 to 50 qubits demonstrates an 84% reduction in network-induced QPU idle time. The framework yields a 3.2 × speedup in overall convergence time for the Quantum Approximate Optimization Algorithm (QAOA) and up to a 98% reduction in classical API call overhead compared to standard RESTful QaaS execution models.
These quantitative findings demonstrate that tightly coupled hybrid co-processing, physically adjacent to the control electronics, is critical for extending the computational bound of Noisy Intermediate-Scale Quantum (NISQ) devices.
Akshay Joseph, R. Delhibabu· Frontiers of Computer Scienc...· 0 citations
The rapid advancement of quantum computing presents an existential threat to the mathematical foundations of modern internet security. Fault-tolerant quantum computers are projected to reach the logical qubit scale necessary to execute Shor's algorithm by 2030–2035, threatening currently deployed public-key cryptography infrastructures.
We evaluate the performance metrics of integrating post-quantum cryptography (PQC), specifically the newly finalized NIST standards (FIPS 203, 204, and 205), with quantum key distribution (QKD) across communication networks.
Our analysis demonstrates that while hybrid PQC-QKD models reduce long-term key compromise probabilities to near 0%, they introduce a 15% to 40% increase in bandwidth overhead during initial cryptographic handshakes.
Given that enterprise-wide cryptographic migrations historically require 7–10 years, organizations face an immediate vulnerability window against “harvest now, decrypt later” adversaries. Ultimately, we propose a phased, cryptographically agile framework to achieve a Zero-Trust, Quantum-Safe network architecture within a 5-year implementation timeline.
R. Delhibabu· Frontiers of Computer Scienc...· 0 citations
The exponential parameter scaling of classical transformer models confronts severe physical and economic barriers. To sustain generative AI capabilities, alternative computational paradigms must be explored.
This paper projects the architecture and scaling laws of Quantum Generative AI Foundation Models by integrating Variational Quantum Algorithms (VQAs) with Fault-Tolerant Quantum Error Correction (QEC). We propose a hybrid quantum-classical framework utilizing isometric Tree Tensor Networks (TTNs) and a novel Quantum Self-Attention (QSA) subroutine, capable of compressing the latent space of a classical 10
11
-parameter Large Language Model (LLM) into a 10
6
parameter quantum neural network via amplitude encoding.
To circumvent Noise-Induced Barren Plateaus (NIBP), we map the training requirements onto a fault-tolerant regime. Assuming a surface code QEC overhead with a physical-to-logical qubit ratio of approximately 2,000:1, we establish the resource requirements for a VQA operating below the 10
−4
physical gate error threshold. Our numerical projections indicate an approximate 45% reduction in total energy expenditure for frontier model training and a per-query attention processing complexity of
O
(
L
log
d
)
.
The quantum framework fundamentally subverts the classical compute wall by substituting linear parameter scaling with logarithmic latent space compression, acknowledging that full attention matrix computation retains a dependency on measurement precision overheads.
R. Delhibabu· Frontiers of Computer Scienc...· 0 citations
TeleZK-FL establishes the feasibility of verifiable, trustless federated learning on commodity telehealth hardware by eliminating the computational bottlenecks of server-side proof generation while incurring only 0.1%–0.3% AUC degradation.
P. Jayaraman, R. Delhibabu· Frontiers in Digital Health· 0 citations
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