This repository contains the framework, data, and methodology for TOPAZ v2 (Topology OPtimization Ansatze with Zoning). TOPAZ is a high-performance research framework that introduces Quantum Topology Optimization as a proactive layer for designing noise-resilient quantum circuits. While conventional quantum error mitigation (QEM) operates post-execution on measurements, TOPAZ v2 enables intrinsic resilience by tuning the circuit’s physical parameters—specifically Intensity (ρ) and Timing (τ)—to navigate the parameter manifold toward regions of inherent robustness.
This repository contains the framework, data, and methodology for TOPAZ v2 (Topology OPtimization Ansatze with Zoning). TOPAZ is a high-performance research framework that introduces Quantum Topology Optimization as a proactive layer for designing noise-resilient quantum circuits. While conventional quantum error mitigation (QEM) operates post-execution on measurements, TOPAZ v2 enables intrinsic resilience by tuning the circuit’s physical parameters—specifically Intensity (ρ) and Timing (τ)—to navigate the parameter manifold toward regions of inherent robustness.
Near-term quantum processors operate in a regime fundamentally constrained by environ-mental noise. Current quantum devices experience substantial fidelity degradation from threeprimary noise channels—amplitude damping, phase damping, and depolarizing noise—whichscale catastrophically with circuit depth. Traditional quantum error correction frameworksimpose prohibitive logical-to-physical qubit overhead ratios, rendering them infeasible fornear-term quantum devices. A critical gap exists between raw hardware capabilities andfault-tolerant quantum computation. Addressing this gap requires practical methodologies that enhance circuit resilience with-out requiring hardware restructuring or deep error correction implementations. This researchintroduces TOPAZ (Topology OPtimization Ansatze with Zoning), a topology-preservingoptimization framework that systematically enhances quantum circuit resilience throughstrategic parameter tuning. Unlike structural redesign approaches, TOPAZ maintains cir-cuit connectivity unchanged, enabling passive enhancement that serves as a baseline forcomplementary error mitigation techniques. The novelty of TOPAZ encompasses three principal dimensions. First, it combines theParametric Trotter Expression (PTE) framework with specialized adaptation of the Method of Moving Asymptotes (MMA)—a classical optimization technique proven effective in struc-tural topology optimization—to the quantum parameter space. Second, it implements real-istic, spatially-correlated noise modeling across three independent channels, replacing sim-plified uncorrelated noise assumptions. Third, it demonstrates that passive parameter op-timization yields multiplicative fidelity improvements when combined with techniques likevirtual distillation. This paper presents the complete TOPAZ framework, detailing its mathematical founda-tions, algorithmic implementation, and experimental validation across multi-qubit systemsunder authentic NISQ noise conditions.
This paper explores the application of topological quantum circuit design, leveraging the inherent properties of topological structures to enhance the performance of quantum circuits. The research focuses on developing novel circuit architectures that are inherently more robust to noise and imperfections, offering improved fidelity and scalability. We investigate various topological circuit topologies and their impact on quantum computation, focusing on minimizing errors and maximizing coherence. The core mechanism involves the strategic placement and manipulation of topological features to achieve a more stable and reliable quantum operation. This work presents a framework for designing quantum circuits with enhanced resilience, potentially paving the way for more practical and efficient quantum devices.
Quantum Stealth Computing, a paradigm shift in computation, necessitates robust security measures. Traditional approaches often focus on algorithm optimization, neglecting the fundamental role of topology in enhancing security. This research investigates the utilization of topological structures as a core optimization strategy for quantum stealth computing, specifically leveraging their inherent anti-interference properties to bolster security and explore potential applications. We propose a novel framework that dynamically adjusts topological parameters to mitigate potential attacks and enhance the overall computational resilience. This work presents a comprehensive analysis of the impact of topological optimization on quantum computation's security, demonstrating significant improvements in resilience against known attack vectors.
Quantum computing, a revolutionary paradigm of computation, promises unprecedented computational power by leveraging the principles of quantum mechanics. However, designing and optimizing quantum algorithms and quantum hardware presents significant challenges, particularly in the realm of control and error mitigation. This paper explores the potential of topology, a field of mathematics concerned with shapes and spaces, as a powerful tool for guiding the design and optimization of quantum systems. We propose a novel heuristic modeling approach, drawing inspiration from topological properties, to enhance the efficiency of quantum computations. This approach aims to provide a more robust and adaptable framework for tackling the complexities inherent in quantum algorithm development. The core mechanism involves leveraging topological structures within quantum circuits to optimize gate sequences and reduce error rates.
This paper introduces a novel quantum circuit design framework, Topological Space-Adaptive Quantum Circuit Design (TS-QC), that dynamically adjusts quantum circuit parameters based on input data. Traditional quantum circuit design relies heavily on manual parameter tuning, limiting design flexibility and potentially hindering performance optimization. TS-QC leverages the power of topological structures to optimize quantum circuit parameters, resulting in improved circuit performance and reliability. This work presents a practical implementation of TS-QC and demonstrates its efficacy through a series of simulations and analysis. The core mechanism centers on the application of topological optimization techniques to enhance the robustness and efficiency of quantum circuits.
Quantum error mitigation (QEM) infers noiseless expectation values from noisy variants of a target quantum circuit. Unlike quantum error correction, QEM requires no additional hardware resources and is therefore routinely employed in experiments on contemporary quantum processors. QEM strategies based on agnostic noise amplification (ANA) are intrinsically resilient to temporal noise drift during the execution of the experiment, but their sampling cost (runtime overhead) remains a major practical bottleneck. In this work, we introduce the virtual noise scaling framework and combine it with layered mitigation to further enhance performance. While virtual noise scaling consistently reduces sampling overhead, we identify a specific noise threshold for the layered mitigation approach. When the noise level is above this threshold, layered mitigation decreases the sampling overhead; conversely, when below it, the overhead increases. Notably, this threshold is circuit-independent and depends solely on the number of layers. For strong noise, the combination of virtual noise scaling and layered mitigation yields several orders of magnitude reduction in sampling overhead compared with conventional zero-noise extrapolation post-processing. As a result, mitigation tasks that once seemed unrealistic are now challenging but achievable. The proposed approach is compatible with dynamic circuits and can be seamlessly integrated with error detection and quantum error correction schemes. In addition, it is also applicable to ANA-based mitigation of mid-circuit measurements and preparation errors. Our findings extend to any constant-step amplification factors and therefore also apply to probabilistic error amplification (PEA) QEM. We validate our post-processing approach by applying it to previously reported experimental data, where we observe a substantial improvement in mitigation efficiency and accuracy.