Ensembles and Effects

Xue Zhang, Tai-Yu Zheng, Tian Tian +1 more

The Dynamical Casimir effect and collective excitations in atom ensemble are investigated in a non-stationary cavity. The results show that the dynamical Casimir effect leads to the collective excitations of atom ensemble due to absorption of the created photons. This remarkable feature indicates that the atom ensemble can serve as a detector for the process of photon production in a non-stationary cavity. Therefore by measuring the collective excitations of the atom ensemble, the dynamical Casimir effect can indirectly be verified.

Jani Lukkarinen

We develop a regularization of the quantum microcanonical ensemble, called a Gaussian ensemble, which can be used for derivation of the canonical ensemble from microcanonical principles. The derivation differs from the usual methods by giving an explanation for the, at the first sight unreasonable, effectiveness of the canonical ensemble when applied to certain small, isolated, systems. This method also allows a direct identification between the parameters of the microcanonical and the canonical ensemble and it yields simple indicators and rigorous bounds for the effectiveness of the approximation. Finally, we derive an asymptotic expansion of the microcanonical corrections to the canonical ensemble for those systems, which are near, but not quite, at the thermodynamical limit and show how and why the canonical ensemble can be applied also for systems with an exponentially increasing density of states. The aim throughout the paper is to keep mathematical rigour intact while attempting to produce results which are both physically and practically interesting.

Kahn Rhrissorrakrai, Kathleen E Hamilton, Prerana Bangalore Parthasarathy +5 more

Learning on sample-limited data is a challenge frequently encountered in many real-world applications. In this work we study how effective quantum ensemble models are when trained on a sample-limited data problem in healthcare and life sciences. We constructed multiple types of quantum ensembles for binary classification using up to 26 qubits in simulation and 56 qubits on quantum hardware. The ensembles include both variational and non-variational methods as well as introducing a new quantum ensemble cosine classifier with randomly sampled unitaries. Our ensemble designs use minimal trainable parameters but require long-range connections between qubits. We tested these quantum ensembles on synthetic datasets and gene expression data from renal cell carcinoma (RCC) patients with the task of predicting patient response to immunotherapy. From the performance observed in simulation and quantum hardware experiments using up to 56 qubits, we demonstrate how quantum embedding structure affects performance and discuss how to extract informative features and build models that can learn and generalize effectively. We also find that quantum ensemble cosine classifiers were effective in learning from few training data, and all quantum ensembles performed comparatively to classical ensembles while using significantly fewer learners. We confirmed this performance characteristic in a separate RCC validation cohort. We present these exploratory results in order to assist other researchers in the design of effective learning using ensembles, particularly for similarly size constrained problems. Incorporating quantum computing in these data constrained problems offers hope for a wide range of studies in healthcare and life sciences where biological samples are relatively scarce given the feature space to be explored.