arXiv subjects

Zhao-Ming Wang

Publications and source records attributed to Zhao-Ming Wang.

At least 19 recordsLinked to original sources

arXiv2026

Exact dynamics of first-order system-bath coherence in bilinear bosonic models

We investigate the exact dynamics of first-order system--bath coherence induced by excitation exchange in bilinear bosonic models. By solving the linear Heisenberg equations, we obtain the exact evolution of system and bath operators and evaluate the first-order coherence between the system mode and the collective bath mode directly coupled to it. We analyze how this coherence depends on initial occupations, coupling strength, spectral width, and detuning, showing that its buildup and oscillatory behavior are closely related to excitation exchange and reservoir memory. We further examine controlled coherence dynamics under leakage-elimination-operator inspired modulation of the system frequency. The results show that random modulation can suppress excitation leakage and maintain finite and relatively stable coherence fluctuations at long times. These results clarify the evolution and control of first-order system--bath coherence in settings where a localized bosonic mode is coupled to a structured bosonic reservoir.

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arXiv2026

Forked Physics-Informed Neural Networks for Non-Markovian Open Quantum Dynamics and Control

Physics-informed neural networks (PINNs) provide a pathway to reunify the simulation and control of quantum systems, in which these two tasks are typically decoupled in traditional strategies. However, most work remains confined to Markovian environments. When applied to non-Markovian systems, standard PINN architectures fail to converge reliably due to multi-objective optimization conflicts arising from the coupled differential equations. To address this fundamental limitation, we extend our previously proposed forked PINN (FPINN) by incorporating a dedicated control branch. By decoupling the optimization objectives at the gradient level via selective gradient flow, our method turns a previously intractable multi-task optimization into a well-conditioned one, allowing simulation and control to be optimized jointly without compromise. Numerical simulations on a two-qubit Heisenberg XXX model confirm that our framework faithfully reproduces the features of non-Markovian dynamics, including decoherence and information backflow. Taking a state-preparation task on the same model as an example, our FPINN achieves higher fidelity than gradient ascent pulse engineering, chopped random basis, and standard PINNs, with the advantage becoming more pronounced as the environment becomes more dissipative and more Markovian. The generated pulses are also noticeably smoother, which is advantageous for experimental implementation. Our framework thus provides a unified, end-to-end differentiable paradigm for simulation and control of open quantum systems, with potential implications for quantum computing, simulation, and control.

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arXiv2026

Nonperturbative Leakage Elimination Operator-Based Quantum Control Pulse Design Beyond the High Frequency Driving Regime

Precise quantum pulse design is central to achieving high precision quantum control, while level leakage induced by system environment coupling is the bottleneck limiting control precision. The leakage elimination operator (LEO) approach is highly effective at suppressing leakage from target subspace to other leakage spaces. The analytical control conditions under the high frequency driving limit have been derived via the Feshbach PQ partitioning technique. However, low frequency driving is experimentally more feasible, and the driving strength is subject to a fundamental physical bound. In this work, we overcome the high frequency driving limit in the pulse design by recasting the LEO protocol within the nonperturbative Floquet-Magnus framework. Applying the Magnus expansion to Floquet dynamical localization, we establish a generalized optimal control formalism that is applicable to the low frequency regime. We prove that the analytical control conditions derived via the Feshbach PQ partitioning technique are equivalent to the zero order Magnus expansion, and that higher order Magnus terms must be taken into account in the low frequency driving regime. We validate our nonperturbative framework using two examples: near perfect quantum state transfer in a one dimensional spin chain and adiabatic speedup in a two level system, corresponding to time independent and time dependent system Hamiltonians, respectively. Our results provide an effective route for designing control pulses in the low frequency regime, which is promising for practical quantum information processing tasks across diverse experimental platforms, including superconducting qubits and ion traps.

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arXiv2026

End-to-End Learning of Quantum Control on Latent Dynamical Manifold

Traditional quantum control relies on an iterative "simulate-then-optimize" paradigm, where dynamics simulation and control design are decoupled, leading to substantial computational overhead and limited scalability, particularly in noisy environments. Here, we propose an end-to-end quantum control framework based on long short-term memory, in which system dynamics and control strategies are learned jointly in a low dimensional latent manifold. The model directly maps initial states and environmental parameters to both dynamical trajectories and optimized control pulse in a single forward pass. The framework is validated on adiabatic speedup in a two-level system and state transfer in a one-dimensional spin chain under noise, achieving accurate dynamical prediction and control optimization. It improves the fidelity for both tasks and significantly reduces the optimization cost by three orders of magnitude compared with conventional iterative methods, while exhibiting strong generalization to multi-parameter, time-varying noise, as well as to different initial states and driving fields. Our work introduces a data-driven control paradigm based on latent manifold learning, reducing the computational bottleneck of iterative optimization and enabling real-time adaptive control of complex open quantum systems.

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arXiv2026

Optimal control of open-quantum-system dynamics predicted by long short-term memory

The realization of high-fidelity quantum control is crucial for quantum information processing, particularly in noisy environments where control strategies must simultaneously achieve precise manipulation and effective noise suppression. Conventional optimal control designs typically require numerical calculations of the system dynamics. Recent studies have demonstrated that long short-term memory neural networks (LSTM) can accurately predict the time evolution of open quantum systems. Based on LSTM predicted dynamics, we propose an optimal control framework for rapid and efficient optimal control design in open quantum systems. As illustrative examples, we apply the proposed framework to design optimal control for adiabatic speedup in a two-level system and for quantum state transfer in a spin chain, both under non-Markovian environments. For adiabatic speedup, our optimization procedure involves two steps: driving trajectory optimization and zero-area pulse optimization. Fidelity improvements for both steps have been obtained, demonstrating the effectiveness of the scheme. Furthermore, this effectiveness is validated for quantum state transfer in a spin chain, which is a high dimensional control problem. Our optimal control design scheme utilizes predicted dynamics to generate optimized controls, offering broad application potential in quantum computing, communication, and sensing.

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arXiv2026

Realizing leakage elimination operator-based adiabatic speedup on a superconducting quantum processor

The slow evolution required for adiabaticity in adiabatic quantum computation renders the system vulnerable to environmental noise. Leakage elimination operator (LEO) control provides an effective strategy to realize adiabatic speedup over a short timescale, thus mitigating the noise impact. Despite extensive theoretical investigations, the realization of LEO-based adiabatic speedup on realistic superconducting quantum processors remains absent. In this work, we present such a realization on IBM superconducting quantum processors. We first characterize the trade-off between adiabaticity and noise accumulation by varying the total evolution time on both the Qiskit simulator and the ibm_marrakesh processor, employing a comprehensive noise model that closely reproduces the experimental results. We then implement ideal LEO pulse derived for a closed system and achieve a significant enhancement of adiabatic fidelity within a short evolution time. To further improve the adiabatic fidelity, we refine the ideal LEO pulse via Bayesian optimization based on the comprehensive noise model. The optimized pulse yields a modest fidelity gain in simulation, yet on hardware it falls short of the ideal pulse under the present experimental conditions. Our work validates the feasibility of LEO-based adiabatic speedup on a superconducting quantum processor and highlights the potential of LEO for noise-aware adiabatic dynamics.

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arXiv2026

Noise-resilient Universal Quantum Computing in the Presence of Anisotropic Noise

We propose a universal gate set for quantum computing that operates in the presence of decoherence without the overhead of active error correction. We show that a broad class of anisotropic system--bath couplings can be effectively decoupled by preparing an appropriate system--bath entangled initial state. The initially established entanglement serves as a resource to cancel out the dominant decoherence during evolution, enabling quantum computation to proceed as if the system were effective decoupled from its environment.

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arXiv2026

Steady-State Coherences under Partial Collective non-Markovian Decoherence

Steady-state coherence in open quantum systems is crucial for quantum technologies, yet its behavior is not fully understood due to the interplay between collective and individual decoherence. While collective decoherence is thought to induce steady-state coherence, experiments often fail to observe this because of individual decoherence. We study a system of two harmonic oscillators coupled to both individual and collective environments, introducing a tunable parameter to adjust the decoherence proportions. By analytically solving the exact dynamical equations, we find that steady-state coherence depends on the initial state under collective decoherence, but not under partial decoherence. Interestingly, non-Markovianity induces rich and complex steady-state coherence behaviors. Our results offer new insights into the role of non-Markovian decoherence in quantum systems and serve as a benchmark for evaluating approximate methods in modelling quantum processes.

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arXiv2026

Forked Physics Informed Neural Networks for Coupled Systems of Differential equations

Solving coupled systems of differential equations (DEs) is a central problem across scientific computing. While Physics Informed Neural Networks (PINNs) offer a promising, mesh-free approach, their standard architectures struggle with the multi-objective optimization conflicts and local optima traps inherent in coupled problems. To address the first issue, we propose a Forked PINN (FPINN) framework designed for coupled systems of DEs. FPINN employs a shared base network with independent branches, isolating gradient pathways to stabilize training. We demonstrate the effectiveness of FPINN in simulating non-Markovian open quantum dynamics governed by coupled DEs, where multi-objective conflicts and local optima traps often cause evolutionary stagnation. To overcome this second challenge, we incorporate an evolution regularization loss that guides the model away from trivial solutions and ensures physically meaningful evolution. We demonstrate the effectiveness of FPINN in simulating non-Markovian open quantum dynamics governed by coupled DEs, where multi-objective conflicts and local optima traps often cause evolutionary stagnation. For the spin-boson and XXZ models, FPINN accurately captures hallmark non-Markovian features, such as quantum coherence revival and information backflow, significantly outperforming standard PINNs. The proposed FPINN architecture offers a general and effective framework for solving coupled systems of equations, which arise across a broad spectrum from classical physics to modern artificial intelligence, including applications in multi-body rotational dynamics, multi-asset portfolio optimization, chemical reaction kinetics, and deep representation learning.

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arXiv2025

Machine-Learning-Assisted Pulse Design for State Preparation in a Noisy Environment

High-precision quantum control is essential for quantum computing and quantum information processing. However, its practical implementation is challenged by environmental noise, which affects the stability and accuracy of quantum systems. In this paper, using machine learning techniques we propose a quantum control approach that incorporates environmental factors into the design of control schemes, improving the control fidelity in noisy environments. Specifically, we investigate arbitrary quantum state preparation in a two-level system coupled to a bosonic bath. We use both Deep Reinforcement Learning (DRL) and Supervised Learning (SL) algorithms to design specific control pulses that mitigate the noise. These two neural network (NN) based algorithm both have the advantage that the well trained NN can output the optimal pulse sequence for any environmental parameters. Comparing the performance of these two algorithms, our results show that DRL is more effective in low-noise environments due to its strong optimization capabilities, while SL provides greater stability and performs better in high-noise conditions. These findings highlight the potential of machine learning techniques to enhance the quantum control fidelity in practical applications.

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arXiv2025

Enhanced Algorithmic Perfect State Transfer on IBM Quantum Computers

Perfect state transfer (PST) through a spin chain can be theoretically obtained via predesigned PST couplings. However, the corresponding experiment on IBM quantum computers demonstrates low transmission success probability (SP) due to noises. Using few qubits of their 127-qubit Eagle processors, we perform the simulation of algorithmic PST through an XY spin chain with PST couplings on ibm_sherbrooke and ibm_brisbane processors, alongside Qiskit simulations. The peak SP cannot reach 1 ($\sim$0.725 peak SP for N=4). We then propose a comprehensive noise model including Pauli errors, thermal relaxation ($T_1$) and dephasing ($T_2$), and ZZ crosstalk. Based on the experimental parameters provided by the IBM superconducting quantum computing platform, we perform the Qiskit simulation with the comprehensive noise model, and find that the time evolution of the SP is highly consistent with the experimental results. This simulation yields a peak SP of 0.761 at $\textstyle t\approxπ/4$, closely matching the results on hardware. To mitigate the impact of noise, we use rescaling techniques to correct noise-induced time shifts and SP decay, achieving an SP improvement of 0.210 (27.60%) in simulators and 0.263 (38.23%) on hardware, aligning hitting times closer to ideal values. Additionally, optimal couplings designed via grid search and refined by Bayesian optimization under the comprehensive noise model achieve an SP improvement of 0.190 (26.21%) in simulators and 0.056 (7.72%) on hardware. Our work highlights challenges in implementing algorithmic PST on current quantum computers, proposes a comprehensive noise model to effectively describe the system dynamics, and provides insights for developing noise-robust quantum communication protocols.

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arXiv2024

Going beyond quantum Markovianity and back to reality: An exact master equation study

The precise characterization of dynamics in open quantum systems often presents significant challenges, leading to the introduction of various approximations to simplify a model. One commonly used strategy involves Markovian approximations, assuming a memoryless environment. In this study, such approximations are not used and an analytical dynamical depiction of an open quantum system is provided. The system under consideration is an oscillator that is surrounded by a bath of oscillators. The resulting dynamics are characterized by a second-order complex coefficient linear differential equation, which may be either homogeneous or inhomogeneous. Moreover, distinct dynamical regions emerge, depending on certain parameter values. Notably, the steady-state average excitation number (AEN) of the system shows rapid escalation with increasing non-Markovianity, reflecting the intricacies of real-world dynamics. In cases where there is detuning between the system frequency and the environmental central frequency within a non-Markovian regime, the AEN maintains its initial value for an extended period. Furthermore, the application of pulse control can effectively protect the quantum system from decoherence effects without using approximations. The pulse control can not only prolong the relaxation time of the oscillator, but can also be used to speed up the relaxation process, depending on the specifications of the pulse. By employing a kick pulse, the Mpemba effect can be observed in the non-Markovian regime in a surprisingly super-cooling-like effect.

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arXiv2024

Arbitrary quantum states preparation aided by deep reinforcement learning

The preparation of quantum states is essential in the realm of quantum information processing, and the development of efficient methodologies can significantly alleviate the strain on quantum resources. Within the framework of deep reinforcement learning (DRL), we integrate the initial and the target state information within the state preparation task together, so as to realize the control trajectory design between two arbitrary quantum states. Utilizing a semiconductor double quantum dots (DQDs) model, our results demonstrate that the resulting control trajectories can effectively achieve arbitrary quantum state preparation (AQSP) for both single-qubit and two-qubit systems, with average fidelities of 0.9868 and 0.9556 for the test sets, respectively. Furthermore, we consider the noise around the system and the control trajectories exhibit commendable robustness against charge and nuclear noise. Our study not only substantiates the efficacy of DRL in QSP, but also provides a new solution for quantum control tasks of multi-initial and multi-objective states, and is expected to be extended to a wider range of quantum control problems.

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arXiv2024

Non-Markovian environment induced anomaly in steady state quantum coherence

Environment induced steady state quantum coherence (SSQC) is a captivating phenomenon that challenges conventional understandings of decoherence. In this letter, we delve into the foundational aspects of environment-induced SSQC, shedding light on its emergence within the framework of system-bath interactions. Starting from a microscopic system-bath coupled model, we investigate the dependence of SSQC on environmental memory effects, bath temperature, system-bath coupling strength, and squeezing parameters. Our findings reveal that the environment not only acts as a generator but also as a disruptor of SSQC. A peak will exist for a non-Markovian bath, which is a result of competition between these two mechanisms. Interestingly, the peak disappears in Markovian case. Additionally, we observe that the generated SSQC can be further amplified through environment squeezing.

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arXiv2024

Time series prediction of open quantum system dynamics

Time series prediction (TSP) has been widely used in various fields, such as life sciences and finance, to forecast future trends based on historical data. However, to date, there has been relatively little research conducted on the TSP for quantum physics. In this paper, we explore the potential application of TSP in forecasting the dynamical evolution of open quantum systems. We employ deep learning techniques to train a TSP model and evaluate its performance by comparison with exact solution. We use the ratio of the prediction step length and the sequence length to define short and long-term forecasting. Our results show that the trained model has the ability to effectively capture the inherent characteristics of time series for both short-term and long-term forecasting. Accurate predictions for different coupling intensities and initial states are obtained. Furthermore, we use our method to train another model and find that it can successfully predict the steady state of the system. These findings suggests that TSP is a valuable tool for the prediction of the dynamics in open quantum systems.

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arXiv2023

Hybrid noise protection of logical qubits for universal quantum computation

Quantum computers now show the promise of surpassing any possible classical machine. However, errors limit this ability and current machines do not have the ability to implement error correcting codes due to the limited number of qubits and limited control. Therefore, dynamical decoupling (DD) and encodings that limit noise with fewer qubits are more promising. For these reasons, we put forth a model of universal quantum computation that has many advantages over strategies that require a large overhead such as the standard quantum error correcting codes. First, we separate collective noise from individual noises on physical qubits and use a decoherence-free subspace (DFS) that uses just two qubits for its encoding to eliminate collective noise. Second, our bath model is very general as it uses a spin-boson type bath but without any Markovian assumption. Third, we are able to either use a steady global magnetic field or to devise a set of DD pulses that remove much of the remaining noise and commute with the logical operations on the encoded qubit. This allows removal of noise while implementing gate operations. Numerical support is given for this hybrid protection strategy which provides an efficient approach to deal with the decoherence problems in quantum computation and is experimentally viable for several current quantum computing systems. This is emphasized by a recent experiment on superconducting qubits which shows promise for increasing the number of gates that can be implemented reliably with some realistic parameter assumptions.

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arXiv2023

Enhanced quantum state preparation via stochastic prediction of neural network

In pursuit of enhancing the predication capabilities of the neural network, it has been a longstanding objective to create dataset encompassing a diverse array of samples. The purpose is to broaden the horizons of neural network and continually strive for improved prediction accuracy during training process, which serves as the ultimate evaluation metric. In this paper, we explore an intriguing avenue for enhancing algorithm effectiveness through exploiting the knowledge blindness of neural network. Our approach centers around a machine learning algorithm utilized for preparing arbitrary quantum states in a semiconductor double quantum dot system, a system characterized by highly constrained control degrees of freedom. By leveraging stochastic prediction generated by the neural network, we are able to guide the optimization process to escape local optima. Notably, unlike previous methodologies that employ reinforcement learning to identify pulse patterns, we adopt a training approach akin to supervised learning, ultimately using it to dynamically design the pulse sequence. This approach not only streamlines the learning process but also constrains the size of neural network, thereby improving the efficiency of algorithm.

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arXiv2023

Optimized control for high-fidelity state transmission in open systems

Quantum state transfer (QST) 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 more 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.

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