Digital twin technology -- the creation of a continuously updated virtual replica of a physical system synchronised with real-time sensor data -- has transformed performance analysis in aerospace, manufacturing, and energy sectors. Applying digital twin principles to quantum hardware offers a compelling paradigm: a quantum processor digital twin (QPDT) continuously ingests calibration data (T1/T2 times, gate error rates, crosstalk coefficients) and maintains an up-to-date computational noise model that accurately predicts circuit-level performance without running circuits on physical hardware. This paper proposes the Quantum Processor Digital Twin (QPDT) framework, a systematic methodology for constructing, calibrating, and validating digital twins for superconducting and trapped-ion quantum processors. QPDT is evaluated on 18 real quantum processors across IBM Quantum, Google Sycamore, and IonQ Aria platforms over a 90-day continuous operation period. Key results: QPDT achieves circuit fidelity prediction accuracy of 96.2% (mean absolute error 0.038 across 2,400 test circuits); twin synchronisation latency of 4.2 minutes from calibration update to model refresh; and a Quantum Twin Fidelity Score (QTFS) of 0.912. QPDT reduces hardware access requirements for algorithm benchmarking by 68% while maintaining prediction accuracy within 4% of physical execution.
Digital twin technology -- the creation of a continuously updated virtual replica of a physical system synchronised with real-time sensor data -- has transformed performance analysis in aerospace, manufacturing, and energy sectors. Applying digital twin principles to quantum hardware offers a compelling paradigm: a quantum processor digital twin (QPDT) continuously ingests calibration data (T1/T2 times, gate error rates, crosstalk coefficients) and maintains an up-to-date computational noise model that accurately predicts circuit-level performance without running circuits on physical hardware. This paper proposes the Quantum Processor Digital Twin (QPDT) framework, a systematic methodology for constructing, calibrating, and validating digital twins for superconducting and trapped-ion quantum processors. QPDT is evaluated on 18 real quantum processors across IBM Quantum, Google Sycamore, and IonQ Aria platforms over a 90-day continuous operation period. Key results: QPDT achieves circuit fidelity prediction accuracy of 96.2% (mean absolute error 0.038 across 2,400 test circuits); twin synchronisation latency of 4.2 minutes from calibration update to model refresh; and a Quantum Twin Fidelity Score (QTFS) of 0.912. QPDT reduces hardware access requirements for algorithm benchmarking by 68% while maintaining prediction accuracy within 4% of physical execution.
We evaluate calibration-based digital twins for IBM Quantum hardware, aiming to reproduce hardware measurement outcomes on classical simulators. We present a workflow that builds twins from downloadable calibration CSV files by mapping coherence times, gate and readout error rates, and operation durations to thermal-relaxation, depolarizing, and readout error channels, while reconstructing a directed coupling map to restore connectivity constraints during transpilation. We compare four twin variants (CSV-built, backend-derived simulator, backend-derived noise model, and fake-backend snapshots) under a common execution and validation protocol. Experiments on two IBM QPUs, ibm_brisbane and ibm_sherbrooke, use randomized five-qubit circuits of depths 10, 20, and 30 across four optimization levels. Weighted Jaccard similarity indicates that twins constructed from downloadable calibration CSV data often achieved the closest agreement with hardware, while backend-derived twins provided competitive and practical baselines. The results further show that agreement depends on both the target device and the transpilation settings, underscoring the need to validate digital twins for the specific execution setup rather than assuming transferability across devices.
The proliferation of Internet of Things (IoT) devices has created a critical demand for highly energy-efficient and timing-accurate Very Large-Scale Integration (VLSI) circuits. Traditional simulation and modeling techniques often fall short in capturing real-time performance metrics, particularly under dynamic operating conditions and process variations. Digital Twin (DT) technology has emerged as a transformative approach, enabling real-time mirroring and prediction of physical systems. However, classical DT frameworks are computationally limited when simulating complex VLSI behaviors involving multiple non-linear interactions and stringent power constraints. To address these challenges, this research proposes a novel quantum computing-enhanced digital twin framework designed for low-power VLSI circuits in IoT applications. The proposed system leverages the computational parallelism and probabilistic modeling capabilities of quantum algorithms to accelerate the simulation and optimization of power and timing metrics. A hybrid quantum-classical architecture is employed, where a quantum variational circuit models the probabilistic switching behavior of logic gates, while classical components handle data acquisition and control operations. Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) are integrated to minimize energy dissipation and optimize timing paths, respectively. Real-time telemetry from IoT hardware is used to continuously update the twin, ensuring fidelity with the physical circuit. Experimental evaluations on industry-standard benchmark circuits (e.g., ISCAS-85, ITC-99) demonstrate significant improvements in dynamic power estimation accuracy and critical path timing prediction when compared to classical simulation methods. Energy consumption was reduced by up to 17%, and timing deviations were minimized by 22% under varying operating conditions. Moreover, the quantum-DT framework proved robust in the presence of noise and variations, exhibiting scalability for large-scale circuit designs. This study highlights the potential of integrating quantum computing with digital twin technology to meet the stringent design goals of modern VLSI systems, especially in resource-constrained IoT environments. The proposed framework not only enhances modeling precision but also enables real-time adaptive optimization, paving the way for next-generation intelligent circuit design and autonomous system calibration.
This paper presents a predictive error suppression architecture for quantum computing that eliminates reliance on physical redundancy. By constructing a digital twin of the quantum processor, a classical system models and predicts noise as deviations from ideal quantum states in real time. The predicted noise is then used to correct measurement outcomes or apply compensatory operations, significantly improving computational accuracy on NISQ devices. This approach shifts the challenge of error correction from hardware scaling to computational modeling, enabling scalable, adaptive, and practical quantum computing systems.
This work presents a conceptual architecture and technological roadmap for a scalable superconducting quantum computing platform based on modular design principles. Rather than introducing a new qubit technology, the proposal focuses on addressing critical engineering bottlenecks that currently limit the large-scale deployment of quantum processors, including wiring complexity, electromagnetic crosstalk, fabrication yield, and latency in error-correction feedback loops.The proposed framework introduces a modular hybrid architecture (“Quantum Mesh”), where medium-scale quantum chiplets containing 25–50 superconducting transmon qubits are interconnected through advanced 3D packaging and cryogenic infrastructure. This approach aims to improve manufacturing yield, reduce wiring density, and enable rack-level integration of quantum processing units while maintaining high gate fidelities and manageable thermal loads.A second central element of the proposal is the integration of a high-fidelity digital twin environment capable of simulating qubit dynamics, noise sources, control latency, and thermal constraints at nanosecond resolution. This predictive framework is intended to support hardware iteration cycles prior to fabrication, potentially accelerating design optimization and system validation.The document outlines a three-year development pathway targeting 100–200 physical qubits and several logical qubits using surface code error correction, followed by a longer-term scaling strategy toward quantum fabrics exceeding one thousand qubits. By combining modular quantum chiplets, partial cryogenic electronics, and predictive digital twin modeling, the proposed architecture explores a plausible industrial pathway toward large-scale quantum computing systems.
Quantum noise and decoherence are the primary barriers to realising fault-tolerant quantum computation on near-term hardware. Accurately modelling noise processes -- amplitude damping, phase damping, depolarising errors, crosstalk, and leakage to non-computational states -- is essential for benchmarking quantum hardware, calibrating error mitigation strategies, and estimating quantum advantage thresholds. This paper proposes the Quantum Noise Computational Model (QNCM) framework, a systematic evaluation of seven noise modelling approaches -- Lindblad master equation, Kraus operator formalism, stochastic Schrodinger equation, randomised benchmarking models, process tomography, Pauli noise models, and coherent error models -- across 24 quantum hardware calibration datasets from IBM Quantum, Google Sycamore, and IonQ Aria processors. QNCM introduces the Noise Model Fidelity Index (NMFI) quantifying how accurately each model reproduces experimental gate error rates, T1/T2 decoherence times, and circuit-level fidelity. Key results: Lindblad master equation achieves NMFI = 0.924 (highest accuracy); Pauli noise models achieve NMFI = 0.882 with 840x lower computational cost; coherent error models are critical for superconducting qubit crosstalk (improving circuit fidelity prediction by 18.4%). The framework provides noise model selection guidance and calibrated noise datasets for 24 real quantum processors.
Cloud-accessible quantum processors enable direct execution of quantum algorithms on heterogeneous hardware platforms. Unlike classical systems, however, identical quantum circuits may exhibit substantially different behavior across devices due to architectural variations in qubit connectivity, gate fidelity, and coherence times.
In this work, we systematically benchmark five representative quantum algorithms - Bell state preparation, GHZ state generation, Quantum Fourier Transform (QFT), Grover's Search, and the Quantum Approximate Optimization Algorithm (QAOA) - across trapped-ion, superconducting, and simulator backends using Amazon Braket. Performance metrics including fidelity, CHSH violation, success probability, circuit depth, and gate counts are evaluated.
Our results demonstrate a strong dependence of algorithmic performance on hardware topology and noise characteristics. For example, 10-qubit GHZ states achieved fidelities above 0.8 on trapped-ion hardware, while superconducting platforms dropped below 0.15 due to routing overhead and accumulated two-qubit gate errors. These findings highlight the importance of hardware-aware algorithm selection and provide practical guidance for benchmarking in the NISQ era.