Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 508-512· 0 citations· 22 references
Abstract
This paper examines quantum algorithms and their computational complexity through a unified framework combining mathematical modeling, system architecture, and empirical evaluation. Key complexity measures circuit depth, gate count, and query complexity are analyzed under NISQ constraints. A hybrid quantum classical optimization framework is introduced to improve efficiency and stability. Results show the full model achieves 94.2% accuracy with baseline runtime, while removing optimization lowers accuracy to 85.6% and increases runtime by 30%. Reducing qubits decreases cost but drops accuracy to 78.3%, and disabling error mitigation causes unstable performance at 70.1%. Comparative analysis indicates strong advantages of quantum algorithms for structured problems, especially in scalability and asymptotic complexity. However, performance remains sensitive to noise, limited qubits, and circuit depth, emphasizing the need for hardware–algorithm co-design.
This work analyzes how circuit design constraints can systematically reduce the measurement overhead associated with repeated evaluations of the candidate gate pool in adaptive algorithms by focusing on the Hadamard test circuit architecture, hardware-aware qubit connectivity, and problem-specific adaptive framework.
Quantum machine learning (QML) faces practical limitations due to noisy intermediate-scale quantum (NISQ) constraints, including noise, restricted qubit availability, and unstable optimization. This paper proposes HyQNet, a resource-aware hybrid quantum–classical framework designed to address these challenges through efficient circuit execution and adaptive optimization. The framework integrates optimized quantum circuits with classical learning strategies to improve scalability and stability under NISQ conditions. Experimental results on Iris, Wine, and Breast Cancer datasets show that HyQNet achieves an accuracy of 95.1% and F1-score of 94.8%, outperforming variational QNN (92.6%) and quantum SVM (91.2%). It also reduces runtime to 16.9 s compared to 20.5 s for VQNN, while maintaining efficient utilization of 8 qubits. Statistical analysis confirms significance (p < 0.05), and ablation studies validate the contribution of each component. The results demonstrate improved convergence stability and resource efficiency in hybrid quantum learning systems.
Sudheer Reddy K., Hastimal Jangid, Usha Desai· 2026 International Conferenc...· 0 citations
We present a full-scale implementation and experimental evaluation of a quantum algorithm for the Longest Common Substring (LCS) problem in the circuit model, bridging the gap between recent theoretical advances and practical realization. Building upon a previously proposed \(\tilde{O}(\sqrt {n})\)-depth quantum circuit, we develop a modular implementation in Qiskit that supports non-binary alphabets and incorporates several key enhancements, including a deterministic BBHT-inspired Grover search, domain expansion via ancillary qubits to stabilize amplitude amplification, and circuit-level optimizations that reduce overhead. Our approach is validated through an extensive experimental campaign over a binary alphabet augmented with two termination symbols and length 16 demonstrating an overall accuracy of 98.4%. The results show that errors are both rare and small, with a consistent conservative bias toward underestimation, and that the algorithm maintains high performance across a wide range of input configurations. We further analyze the behavior of the algorithm under realistic noise models, showing a progressive degradation of accuracy and identifying a structural asymmetry in the error patterns induced by the oracle. These findings provide concrete evidence that circuit-based quantum algorithms for string processing can achieve reliable behavior in ideal settings, while highlighting key challenges for their deployment on noisy quantum devices.
R. Cantone, G. Falci, Simone Faro et al.· IEEE International Symposium...· 0 citations
Quantum computing algorithms are usually built by using gates and circuits to manipulate qubits. In this article, our analysis examines the impact of additive noise in a quantum circuit as an engineering simplification model. We evaluate these effects by computing the probability, fidelity, and signal-to-noise ratio (SNR). We study quantum noise from an amplitude-domain signal-processing perspective, bridging classical additive noise models with quantum state perturbations. We have analyzed the results of various quantum gates, such as X, Y, Z, and Hadamard gates. This study provides an approximation of the quantitative effect and qualitative analysis of the effect of additive noise on quantum gates, thereby contributing to a deeper understanding of the challenges and potential solutions in quantum computing. However, in some cases, the model does not represent the physical results due to approximation.
Rajnish Kumar, S. Arnon, Torben Larsen· Electronics· 0 citations
The theory of quantum error correction was established decades ago. Yet the limitation of the quantum computing platforms in terms of noise level and available physical qubit count persists, which greatly hinders the development of scalable quantum computing systems. In this paper, we present analytical estimates of logical error rates of advanced QEC codes across leading hardware platforms and distributed quantum computing systems using a simple but unified framework. The analysis captures two dominant contributors to logical error: code structure and two-qubit gate overhead. The framework provides a fast estimate of logical error rates and identification of dominating factors in different hardware platforms, such as circuit volume, routing overhead, inter-QPU operations, or asymmetric noise protection. We show that several qualitative trends observed in larger-scale simulations can be reproduced and interpreted analytically within this framework. We further demonstrate that the framework can be used to find the sweet spot design region of distributed QEC, which is critical for the design of distributed quantum computing systems.
Canonical quantum algorithms often achieve low execution fidelities on current Noisy Intermediate-Scale Quantum (NISQ) hardware. The standard implementation of Grover's search algorithm, designed for theoretical generality, produces deep, gate-heavy circuits that are susceptible to noise. This paper challenges the "one-size-fits-all" design paradigm by using Grammatical Evolution (GE) to automatically discover hardware-efficient, state-specific quantum circuits. We demonstrate this approach by evolving bespoke circuits for all eight 3-qubit computational basis states and executing them on a 133-qubit IBM Heron quantum processor. To our knowledge, this is the first hardware-validated application of GE for this task. The results indicate significant performance gains: evolved circuits achieve hardware-executed fidelities up to 96.9% (vs. 66.3% baseline) while reducing circuit depth by 82.5–96.6% and gate count by 77.4–94.6% compared to canonical implementations. These findings suggest that automated symbolic search is a viable approach to designing algorithms that can execute on today's NISQ devices.
Arinze Obidiegwu, Douglas Mota Dias, Emmanuel Obidiegwu et al.· Annual Conference on Genetic...· 0 citations