Abstract Perfect state transfer (PST) through a spin chain can be theoretically obtained via predesigned PST couplings. We perform the algorithmic PST simulations through short spin chains on IBM quantum computers, alongside Qiskit simulations. The results demonstrate low transmission success probability (SP) due to noise. To characterize the noise effects in system dynamics, we propose a comprehensive noise model incorporating Pauli errors, thermal relaxation, dephasing, and ZZ crosstalk. Simulations of the SP time evolution using this model in Qiskit show strong agreement with the experimental results. Building on the model, we further propose two noise mitigation strategies: rescaling technique and optimal coupling design via grid search or Bayesian optimization. Both approaches yield significant SP improvements in simulations and on hardware. Our work underscores the challenges in implementing algorithms on current quantum hardware and paves the way for developing noise-resistant protocols.
Abstract Perfect state transfer (PST) through a spin chain can be theoretically obtained via predesigned PST couplings. We perform the algorithmic PST simulations through short spin chains on IBM quantum computers, alongside Qiskit simulations. The results demonstrate low transmission success probability (SP) due to noise. To characterize the noise effects in system dynamics, we propose a comprehensive noise model incorporating Pauli errors, thermal relaxation, dephasing, and ZZ crosstalk. Simulations of the SP time evolution using this model in Qiskit show strong agreement with the experimental results. Building on the model, we further propose two noise mitigation strategies: rescaling technique and optimal coupling design via grid search or Bayesian optimization. Both approaches yield significant SP improvements in simulations and on hardware. Our work underscores the challenges in implementing algorithms on current quantum hardware and paves the way for developing noise-resistant protocols.
Achieving robust and scalable remote quantum entanglement is one of the fundamental challenges for the development of distributed quantum networks and modular quantum computing systems. In this context, perfect state transfer (PST) and fractional state transfer (FST) have emerged as promising schemes for quantum state transfer and remote entanglement generation using only nearest-neighbor couplings. In this work, we propose a quantum state transfer scheme based on a zig-zag configuration, which introduces a control parameter for PST and FST. We show that this new parameter can suppress the population in the intermediate qubits, thereby reducing losses and enhancing state transfer. In certain limiting cases, our new scheme reduces to the conventional PST scheme, revealing an elegant mathematical structure inherent in the design. We experimentally demonstrate our proposed scheme on a superconducting quantum processor, achieving an 18% reduction in error for remote Bell state generation in a 1D ( 1 × 5 ) qubit chain with enhanced robustness to specific noise channels. Furthermore, we extend our approach to a 2D ( 3 × 3 ) network and successfully generate a W state among the four corner qubits. These results highlight the potential of our new enhanced quantum state transfer scheme for scalable and noise-resilient quantum communication and computing.
Hamiltonian inverse engineering enables the design of protocols for specific quantum evolutions or target state preparation. Perfect state transfer (PST) and remote entanglement generation are notable examples, as they serve as key primitives in quantum information processing. However, Hamiltonians obtained through conventional methods often lack robustness against noise. Assisted by inverse engineering, we begin with a noise-resilient energy spectrum and construct a class of Hamiltonians, referred to as the dome model, that significantly improves the system's robustness against noise, as confirmed by numerical simulations. This model introduces a tunable parameter m that modifies the energy-level spacing and gives rise to a well-structured Hamiltonian. It reduces to the conventional PST model at m=0 and simplifies to a SWAP model involving only two end qubits in the large-m regime. To address the challenge of scalability, we propose a cascaded strategy that divides long-distance PST into multiple consecutive PST steps. Our work is particularly suited for demonstration on superconducting qubits with tunable couplers, which enable rapid and flexible Hamiltonian engineering, thereby advancing the experimental potential of robust and scalable quantum information processing.
Abstract Continuous-time quantum walks (CTQW) becoming more significant for quantum information and quantum computation. The perfect state transfer (PST) on graphs is a specific phenomena of CTQW which is widely used in quantum communication of small system, and be found critical for quantum algorithm design recently, such as quantum approximate optimization algorithms and quantum search. If a network G contains 2N vertices, and PST occurs between N pairs of vertices at the same time T, then we say G admits symmetric PST (SPST). In this work, we will study the SPST on bipartite graphs via spectra. And then, we will present how to set couplings of special bipartite graphs such as cycles, grids and chains to admit SPST is given. For cycles with 6, 8, 10, 12 vertices, we give the analytical method to obtain the couplings to admit SPST. Analogously, the couplings for chains and grids to admit SPST are analytical obtained. We also present a typical approach by using the graph tensor product to construct new quantum networks that admit SPST, in which the new quantum networks are derived from special kinds of integral graphs, such as complete graphs, strongly regular graphs and distance regular graphs.
Abstract This practical guide presents methods for constructing noise models using the Qiskit Aer noise module within the IBM Q ecosystem. The objective is to simulate quantum circuits under realistic error conditions, enabling accurate emulation of noisy quantum processors. The guide demonstrates how to: Use the Qiskit Aer noise module to define custom noise models. Incorporate gate errors, readout errors, and decoherence effects into simulations. Apply noise channels (e.g., depolarizing, amplitude damping, phase damping). Attach noise models to quantum circuit simulations. Analyze how noise affects algorithm performance and output distributions. Building noise models is essential for studying the behavior of quantum algorithms in the NISQ (Noisy Intermediate-Scale Quantum) era. It allows researchers to evaluate algorithm robustness, optimize circuit depth, and test error mitigation strategies before execution on real IBM Quantum hardware. This approach provides a bridge between ideal theoretical circuits and physically realistic quantum devices, enhancing the reliability and applicability of quantum computing experiments.
Abstract Quantum computing has recently emerged as a novel paradigm for solving problems otherwise intractable on classical computers, yet the practical progress of quantum computers is severely impacted by noise, decoherence, and imperfect gate operations in Noisy Intermediate-Scale Quantum (NISQ) devices. Specific to IBM Quantum hardware, imperfections due to readout error, gate errors, and device drift, which depends on time, resolves limits for the accuracy of outputs from our algorithms. The mitigation strategies currently used, for example, Measurement Error Mitigation (MEM), Zero Noise Extrapolation (ZNE), and Clifford Data Regression (CDR), offer improvement over raw, unmitigated outputs, but they are static and not dynamic; the reduction of noise in within an NISQ device is temporal and can vary in patterns that are non-zero. In addition to this, using the conventional methods provides inconsistent and suboptimal results.This study will provide enhanced reliability with respect to performing quantum computation on IBM Quantum hardware with a unique mitigation framework that is adaptive and computationally efficient for a high-fidelity output. The problem taken on here is the failure of existing error mitigation strategies to provide adaptive noise compensation, as they assume a statically-distributed error, despite the performance-related characteristics already being documented in regards to calibration drift and variation over time.To achieve this, we propose a Hybrid Adaptive Error Mitigation (HAEM) approach that includes baseline measurement error mitigation and an additional correction layer that utilizes realtime calibration information, along with lightweight machine learning models. The approach can be broken down into three steps: (i) perform standard measurement error mitigation to reduce classical readout noise, (ii) execute compact calibration circuits, such as Bell states, GHZ states and Clifford benchmarks to capture then current error profile of the device, and (iii) use a lightweight machine learning model (having been trained on historical and newest calibration data) to dynamically change the mitigation weights applied for the target quantum algorithm. All implementation is done using Qiskit Runtime to ensure low-latency execution, and we completed experiments on both Qiskit Aer noisy simulators and IBM Quantum devices.Initial results suggest HAEM improved the outputs of quantum algorithms' fidelity compared to the unmitigated runs and the baseline measurement error mitigation (MEM) runs. On the noisy simulators, HAEM increased fidelity from a raw performance of 0.65 to 0.87, nearly a 34% improvement. In fairer hardware-like scenarios, fidelity-on-average was persistently 12% better than MEM, with nearly equal time duration for the additional adaptive measurements. The adaptive aspect of HAEM consistently supported subsequent calibration cycles of the device and was effective to continue providing fidelity improvements despite the static mitigation performance degrading.To sum up, the proposed HAEM framework is a modern and viable alternative for error mitigation which brings together the benefits of baseline approaches with corrections that are driven by fixed-function error-corrective, adaptive learning. As both learning-based and standard baseline approaches can only achieve fixed levels of performance, HAEM, through its use of noise mitigation both at execution time and through correlating it with standard data, provides a route towards reliable quantum computations on current IBM hardware. HAEM also presents a contribution to the NISQ error-resilient landscape, and however it is intended that it will continue to improve future applications of hybrid workflows of quantum and classical computation where error mitigation and resilience is important.
Quantum state transfer through spin chains has been extensively investigated. Two schemes, the coupling set for perfect state transfer (PST) or adding a leakage elimination operator (LEO) Hamiltonian have been proposed to boost the transmission fidelity. However, these ideal schemes are only suitable for closed systems and will lose their effectiveness in open ones. In this work, we invoke a well-explored optimization algorithm, Adam, to expand the applicable range of PST couplings and LEO to the open systems. Our results show that, although the transmission fidelity decreases with increasing system-bath coupling strength, Markovianity and temperature for both ideal and optimized cases, the fidelities obtained by the optimized schemes always outweigh the ideal cases. The enhancement becomes bigger for a stronger bath, indicating a stronger bath provides more space for the Adam to optimize. This method will be useful for the realization of high-fidelity information transfer in the presence of environment.