Parameterized Hamiltonian Learning With Quantum Circuit

Shi, Jinjing; Wang, Wenxuan; Lou, Xiaoping; Zhang, Shichao; Li, Xuelong

doi:10.1109/tpami.2022.3203157

Cited by 24 publications

(10 citation statements)

References 34 publications

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“…Therefore, its time complexity is O(n 2 ), while the time complexity of classical fourier transform is O(n2 n ). It can be seen that the quantum fourier transform has an exponential acceleration compared with the classical fourier transform [18].…”

Section: Correlated Quantum Technologymentioning

confidence: 99%

Robust Quantum Feature Selection Algorithm

Zhang¹,

Li²,

Xu³

et al. 2022

Preprint

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<p>High dimensional data has been a notoriously challenging issue. Existing quantum dimension reduction technology mainly focuses on quantum principal component analysis. There are only a few works on the direction of quantum feature selection algorithm which they are not robust. Also, there are few quantum circuits designed for feature selection, in which some steps are not quantized yet. For example, existing quantum circuits cannot solve the objective function based on sparse learning. To deal with these issues, this paper proposes a robust quantum feature selection by designing a new quantum circuit. Specifically, the sparse regularization term and least squares loss are first applied to construct the proposed objective function. And then, six kinds of quantum registers and their initial states are prepared. In addition, quantum techniques, such as quantum phase estimation and controlled rotation, are employed to construct an alternating iterative quantum circuit to obtain the final quantum state of the feature selection variable. Finally, a series of experiments are conducted to verify that the proposed algorithm can accurately select important features and has good robustness. <br> </p>

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Section: Correlated Quantum Technologymentioning

confidence: 99%

Robust Quantum Feature Selection Algorithm

Zhang¹,

Li²,

Xu³

et al. 2022

Preprint

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show abstract

“…This information is then passed to a classical optimization routine, which suggests new values for the parameters γ and β in order to minimize the expectation value F γ , β . Similar approaches, where optimized parameters of a quantum circuit are found in a hybrid quantum classical process, are also used in other fields such as quantum circuit learning [19] or Hamiltonian learning [20]. As explained in [1], this particular choice, Eq.…”

Section: Qaoa Algorithmmentioning

confidence: 99%

Benchmarking the performance of portfolio optimization with QAOA

Brandhofer

Braun

Dehn

et al. 2022

Quantum Inf Process

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We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA). For a given list of assets, the portfolio optimization problem is formulated as quadratic binary optimization constrained on the number of assets contained in the portfolio. QAOA has been suggested as a possible candidate for solving this problem (and similar combinatorial optimization problems) more efficiently than classical computers in the case of a sufficiently large number of assets. However, the practical implementation of this algorithm requires a careful consideration of several technical issues, not all of which are discussed in the present literature. The present article intends to fill this gap and thereby provides the reader with a useful guide for applying QAOA to the portfolio optimization problem (and similar problems). In particular, we will discuss several possible choices of the variational form and of different classical algorithms for finding the corresponding optimized parameters. Viewing at the application of QAOA on error-prone NISQ hardware, we also analyse the influence of statistical sampling errors (due to a finite number of shots) and gate and readout errors (due to imperfect quantum hardware). Finally, we define a criterion for distinguishing between ‘easy’ and ‘hard’ instances of the portfolio optimization problem.

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“…Quantum computation has attracted attention for factoring integers or finding unordered sets of data (see Shi et al [ 90 ]).…”

Section: Introductionmentioning

confidence: 99%

A Quantum-like Model of Interdependence for Embodied Human–Machine Teams: Reviewing the Path to Autonomy Facing Complexity and Uncertainty

Lawless,

Moskowitz,

Doctor

2023

Entropy

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In this review, our goal is to design and test quantum-like algorithms for Artificial Intelligence (AI) in open systems to structure a human–machine team to be able to reach its maximum performance. Unlike the laboratory, in open systems, teams face complexity, uncertainty and conflict. All task domains have complexity levels—some low, and others high. Complexity in this new domain is affected by the environment and the task, which are both affected by uncertainty and conflict. We contrast individual and interdependence approaches to teams. The traditional and individual approach focuses on building teams and systems by aggregating the best available information for individuals, their thoughts, behaviors and skills. Its concepts are characterized chiefly by one-to-one relations between mind and body, a summation of disembodied individual mental and physical attributes, and degrees of freedom corresponding to the number of members in a team; however, this approach is characterized by the many researchers who have invested in it for almost a century with few results that can be generalized to human–machine interactions; by the replication crisis of today (e.g., the invalid scale for self-esteem); and by its many disembodied concepts. In contrast, our approach is based on the quantum-like nature of interdependence. It allows us theorization about the bistability of mind and body, but it poses a measurement problem and a non-factorable nature. Bistability addresses team structure and performance; the measurement problem solves the replication crisis; and the non-factorable aspect of teams reduces the degrees of freedom and the information derivable from teammates to match findings by the National Academies of Science. We review the science of teams and human–machine team research in the laboratory versus in the open field; justifications for rejecting traditional social science while supporting our approach; a fuller understanding of the complexity of teams and tasks; the mathematics involved; a review of results from our quantum-like model in the open field (e.g., tradeoffs between team structure and performance); and the path forward to advance the science of interdependence and autonomy.

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Parameterized Hamiltonian Learning With Quantum Circuit

Cited by 24 publications

References 34 publications

Robust Quantum Feature Selection Algorithm

Robust Quantum Feature Selection Algorithm

Benchmarking the performance of portfolio optimization with QAOA

A Quantum-like Model of Interdependence for Embodied Human–Machine Teams: Reviewing the Path to Autonomy Facing Complexity and Uncertainty

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