Abstract Despite their theoretical importance, dynamic Bayesian networks associated with quantum processes are currently not accessible experimentally. We here describe a general scheme to determine the multi-time path probability of a Bayesian network based on local measurements on independent copies of a composite quantum system combined with postselection. We further show that this protocol corresponds to a nonprojective measurement. It thus allows the investigation of the multi-time properties of a given local observable while fully preserving all its quantum features.
Abstract Despite their theoretical importance, dynamic Bayesian networks associated with quantum processes are currently not accessible experimentally. We here describe a general scheme to determine the multi-time path probability of a Bayesian network based on local measurements on independent copies of a composite quantum system combined with postselection. We further show that this protocol corresponds to a nonprojective measurement. It thus allows the investigation of the multi-time properties of a given local observable while fully preserving all its quantum features.
Despite their theoretical importance, dynamic Bayesian networks associated with quantum processes are currently not accessible experimentally. We here describe a general scheme to determine the multi-time path probability of a Bayesian network based on local measurements on independent copies of a composite quantum system combined with postselection. We further show that this protocol corresponds to a non-projective measurement. It thus allows the investigation of the multi-time properties of a given local observable while fully preserving all its quantum features.
Abstract In multi-agent systems, Bayesian networks are pivotal for inference and decision-making under uncertainty, yet they face significant challenges, such as high computational complexity and slow decision speed. Quantum computing, leveraging superposition and entanglement, offers potential advantages for solving certain complex problems. Therefore, exploring Bayesian networks within the quantum multi-agent framework promises enhanced inference and decision-making capabilities. While quantum Bayesian networks for inference have been extensively studied, quantum dynamic Bayesian networks for inference and quantum decision networks for decision-making remain underexplored. In the noisy intermediate-scale quantum (NISQ) era, quantum computers struggle to process the long-term temporal structures of dynamic Bayesian networks due to limited resources. To address this, we propose a recursive quantum-classical hybrid Bayesian network inference method, which decomposes dynamic Bayesian networks into smaller subnetworks along the time series. Each subnetwork is represented by a reduced-scale quantum circuit, and the forward and backward operators are computed recursively, enabling efficient filtering and smoothing inference. In addition, we propose an optimal decision-making method based on quantum decision networks, which maps utility values to quantum probabilities and identifies the optimal action by determining the quantum state corresponding to the maximum expected probability. We validate the effectiveness of these algorithms using the IonQ quantum simulator and compare their performance with classical methods. The results demonstrate that our proposed quantum algorithms can effectively perform inference and decision-making tasks well on NISQ devices. The proposed methods provide a foundation for collaborative inference and decision-making within the quantum multi-agent framework.
This paper investigates the application of Bayesian inference for optimizing quantum measurement schemes. Traditional quantum measurement strategies often rely on heuristic approaches or specific system constraints, potentially leading to suboptimal outcomes. We propose a framework where the quantum measurement process is modeled as a Bayesian inference problem. This allows us to systematically explore the measurement landscape, incorporating prior knowledge and iteratively refining the measurement strategy based on observed outcomes. The core of this approach lies in formulating the measurement problem as a probabilistic model and utilizing Bayesian updating to determine the optimal measurement basis or parameters. We demonstrate, through theoretical analysis and conceptual design, that Bayesian optimization can lead to improved measurement accuracy and efficiency compared to conventional methods. The key benefits include reduced measurement errors, enhanced signal-to-noise ratios, and potentially the ability to extract more information from quantum systems. This work presents a novel methodology for quantum measurement, bridging the gap between classical Bayesian optimization and the unique properties of quantum mechanics.
Quantum states over time were introduced, in part, to extend Bayesian probability to quantum theory and have since found applications across diverse areas, ranging from quantum causality and open-system dynamics to tests of macroscopic realism. While axiomatic characterizations of these states exist for Markovian dynamics, a general framework accommodating non-Markovian dynamics is still lacking. Moreover, extending this framework beyond quantum mechanics to general physical theories also remains elusive. In this work, we develop an axiomatic framework for multi-time states within a class of operational probabilistic theories (OPTs) that includes quantum mechanics as a special case. Based on three assumptions---compositionality, reconstructibility, and spatio-temporal marginal consistency---the framework accommodates both Markovian and non-Markovian dynamics, and provides sufficient conditions to certify signaling and temporal memory within OPT. As a concrete application of our OPT framework, we fully characterize the unique multi-time state in boxworld and develop a method for Bayesian retrodiction of physical transformations there. Our general framework is consistent with quantum theory, reproducing several existing results. In the non-Markovian quantum dynamics, we establish a correspondence between multi-time states and the process-matrix framework, and fully characterize the multi-time states compatible with general dynamics. This correspondence also lets us infer quantum causal models from multi-time states, and as a proof of principle, we propose a semidefinite program to infer causal order from quantum multi-time states. These results provide a unified framework for studying spatio-temporal states, causal relations, and Bayesian reasoning within OPTs, with implications for both fundamental and applied research.
Quantum-enhanced Bayesian Network Propagation for Complex Systems Abstract: Quantum-enhanced Bayesian Network Propagation for Complex Systems addresses the limitations of traditional Bayesian Network methods in handling complex, non-linear systems. This paper proposes a novel approach utilizing quantum-inspired algorithms to significantly improve the accuracy and efficiency of probabilistic updates within Bayesian Networks. The core claim is that this method, by combining quantum speed with Bayesian techniques, can overcome current bottlenecks in complex system modeling and analysis. This work investigates the potential of quantum phenomena to enhance Bayesian inference, offering a pathway toward more robust and accurate modeling of dynamic systems.
Quantum state tomography is a crucial technique for characterizing the state of a quantum system. However, traditional tomography methods often suffer from high measurement complexity and require a large number of measurements to achieve high accuracy. This paper proposes a novel approach utilizing Dynamic Bayesian Networks (DBNs) to iteratively refine quantum state tomography measurements. The DBN learns the relationship between the quantum state estimate and the expected measurement outcomes, allowing it to intelligently guide the measurement process. This approach significantly reduces the number of measurements needed while simultaneously improving the accuracy of the state reconstruction. We present a theoretical framework for the DBN-based tomography and demonstrate its potential through a mathematical analysis of the iterative estimation process. The core claim of this work is that DBNs offer a more efficient and accurate method for quantum state tomography compared to conventional techniques.