Quantum Memory: A Missing Piece in Quantum Computing Units

Memory is an indispensable component in classical computing systems. While the development of quantum computing is still in its early stages, current quantum processing units mainly function as quantum registers. Consequently, the actual role of quantum memory in future advanced quantum computing architectures remains unclear. With the rapid scaling of qubits, it is opportune to explore the potential and feasibility of quantum memory across different substrate device technologies and application scenarios. In this paper, we provide a full design stack view of quantum memory. We start from the elementary component of a quantum memory device, quantum memory cells. We provide an abstraction to a quantum memory cell and define metrics to measure the performance of physical platforms. Combined with addressing functionality, we then review two types of quantum memory devices: random access quantum memory (RAQM) and quantum random access memory (QRAM). Building on top of these devices, quantum memory units in the computing architecture, including building a quantum memory unit, quantum cache, quantum buffer, and using QRAM for the quantum input-output module, are discussed. We further propose the programming model for the quantum memory units and discuss their possible applications. By presenting this work, we aim to attract more researchers from both the Quantum Information Science (QIS) and classical memory communities to enter this emerging and exciting area.Comment: 41 pages, 11 figures, 7 table

Altered cognitive response to serotonin challenge as a candidate endophenotype for obsessive-compulsive disorder.

RATIONALE: Obsessive-compulsive disorder (OCD) implicates dysfunction of orbitofrontal and insula-related circuitry and of the serotonin system. There is an on-going search in psychiatry for intermediate biological markers, termed 'endophenotypes', that exist not only in patients with a given disorder but also in their clinically unaffected first-degree relatives. OBJECTIVE: Pharmacological challenge is recognized as a means of eliciting an endophenotype, but this strategy has yet to be used in OCD. METHODS: Twenty-three OCD patients without comorbidities (12 [52.2 %] female), 13 clinically asymptomatic first-degree relatives of OCD patients (11 [84.6 %] female) and 27 healthy controls (16 [59.3 %] female) received single-dose escitalopram (20 mg) and placebo in a randomized double-blind crossover design. Effects of treatment on decision-making were quantified using the Cambridge Gamble Task (CGT) in conjunction with a mixed model analysis of covariance (ANCOVA). RESULTS: There was a significant interaction between serotonergic challenge and group for risk adjustment on the CGT (F = 4.1406; p = 0.02). Only controls showed a significant placebo-drug change in risk adjustment (p = 0.02; versus p > 0.10). Numerically, escitalopram was associated with increase in risk adjustment in controls and reductions in the other groups. Change in risk adjustment was similar in OCD patients and relatives (p = 0.806) and differed significantly from controls (p = 0.007; p = 0.041, respectively). CONCLUSIONS: Individuals with OCD, and first-degree relatives, showed an altered cognitive response to serotonin challenge. This is the first demonstration of a candidate pharmacological challenge endophenotype for the disorder. Future work should confirm these findings in a larger sample size and ideally extend them to other cognitive paradigms, utilizing functional neuroimaging.This work was supported by the Medical Research Council of South Africa, the Obsessive-Compulsive Foundation (Prof Stein), the National Research Foundation of South Africa (Prof Lochner), an unrestricted grant from Lundbeck H/S and by a Starter Grant for Clinical Lecturers from the Academy of Medical Sciences UK (Dr Chamberlain).This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s00213-015-4172-

QuGAN: A Quantum State Fidelity based Generative Adversarial Network

Tremendous progress has been witnessed in artificial intelligence where neural network backed deep learning systems have been used, with applications in almost every domain. As a representative deep learning framework, Generative Adversarial Network (GAN) has been widely used for generating artificial images, text-to-image or image augmentation across areas of science, arts and video games. However, GANs are computationally expensive, sometimes computationally prohibitive. Furthermore, training GANs may suffer from convergence failure and modal collapse. Aiming at the acceleration of use cases for practical quantum computers, we propose QuGAN, a quantum GAN architecture that provides stable convergence, quantum-state based gradients and significantly reduced parameter sets. The QuGAN architecture runs both the discriminator and the generator purely on quantum state fidelity and utilizes the swap test on qubits to calculate the values of quantum-based loss functions. Built on quantum layers, QuGAN achieves similar performance with a 94.98% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms state-of-the-art quantum based GANs in the literature providing a 48.33% improvement in system performance compared to others attaining less than 0.5% in terms of similarity between generated distributions and original data sets. QuGAN code is released at https://github.com/yingmao/Quantum-Generative-Adversarial-NetworkComment: 2021 IEEE International Conference on Quantum Computing and Engineering (QCE

Striatal abnormalities in trichotillomania: a multi-site MRI analysis.

Trichotillomania (hair-pulling disorder) is characterized by the repetitive pulling out of one's own hair, and is classified as an Obsessive-Compulsive Related Disorder. Abnormalities of the ventral and dorsal striatum have been implicated in disease models of trichotillomania, based on translational research, but direct evidence is lacking. The aim of this study was to elucidate subcortical morphometric abnormalities, including localized curvature changes, in trichotillomania. De-identified MRI scans were pooled by contacting authors of previous peer-reviewed studies that examined brain structure in adult patients with trichotillomania, following an extensive literature search. Group differences on subcortical volumes of interest were explored (t-tests) and localized differences in subcortical structure morphology were quantified using permutation testing. The pooled sample comprised N=68 individuals with trichotillomania and N=41 healthy controls. Groups were well-matched in terms of age, gender, and educational levels. Significant volumetric reductions were found in trichotillomania patients versus controls in right amygdala and left putamen. Localized shape deformities were found in bilateral nucleus accumbens, bilateral amygdala, right caudate and right putamen. Structural abnormalities of subcortical regions involved in affect regulation, inhibitory control, and habit generation, play a key role in the pathophysiology of trichotillomania. Trichotillomania may constitute a useful model through which to better understand other compulsive symptoms. These findings may account for why certain medications appear effective for trichotillomania, namely those modulating subcortical dopamine and glutamatergic function. Future work should study the state versus trait nature of these changes, and the impact of treatment

Renormalization-Scheme Dependence of Pade Summation in QCD

We study the renormalization-scheme (RS) dependence of Pade Approximants (PA's), and compare them with the Principle of Minimal Sensitivity (PMS) and the Effective Charge (ECH) approaches. Although the formulae provided by the PA, PMS and ECH predictions for higher-order terms in a QCD perturbation expansion differ in general, their predictions can be very close numerically for a wide range of renormalization schemes. Using the Bjorken sum rule as a test case, we find that Pade Summation (PS) reduces drastically the RS dependence of the Bjorken effective charge. We use these results to estimate the theoretical error due to the choice of RS in the extraction of $\alpha_s$ from the Bjorken sum rule, and use the available data at $Q^2=3 GeV^2$ to estimate $\alpha_s(M_Z) = 0.117^{+0.004}_{-0.007} \pm 0.002$, where the first error is experimental, and the second is theoretical.Comment: 12 pages (latex), including 6 embedded figures; uses epsfig.st

Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry.

Problematic internet use is common, functionally impairing, and in need of further study. Its relationship with obsessive-compulsive and impulsive disorders is unclear. Our objective was to evaluate whether problematic internet use can be predicted from recognised forms of impulsive and compulsive traits and symptomatology. We recruited volunteers aged 18 and older using media advertisements at two sites (Chicago USA, and Stellenbosch, South Africa) to complete an extensive online survey. State-of-the-art out-of-sample evaluation of machine learning predictive models was used, which included Logistic Regression, Random Forests and Naïve Bayes. Problematic internet use was identified using the Internet Addiction Test (IAT). 2006 complete cases were analysed, of whom 181 (9.0%) had moderate/severe problematic internet use. Using Logistic Regression and Naïve Bayes we produced a classification prediction with a receiver operating characteristic area under the curve (ROC-AUC) of 0.83 (SD 0.03) whereas using a Random Forests algorithm the prediction ROC-AUC was 0.84 (SD 0.03) [all three models superior to baseline models p < 0.0001]. The models showed robust transfer between the study sites in all validation sets [p < 0.0001]. Prediction of problematic internet use was possible using specific measures of impulsivity and compulsivity in a population of volunteers. Moreover, this study offers proof-of-concept in support of using machine learning in psychiatry to demonstrate replicability of results across geographically and culturally distinct settings.This research received internal departmental funds of the Department of Psychiatry at the University of Chicago.This is the final version of the article. It first appeared from Elsevier at http://dx.doi.org/10.1016/j.jpsychires.2016.08.010