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Background: Intrinsic resistance to immunotherapy observed in breast cancer is attributed to low neoantigen levels, defective antigen presentation, low mutational burden, reduced programmed death ligand (PD-L1) expression and the presence of immunosuppressive signals like transforming growth factor-beta (TGFβ) in the tumor microenvironment (TME). These collectively attenuate the effector functions of T cells and natural killer cells (NK cells). NK cells are an important component of innate immunity. While generally TGFβ dampens the NK cell response in the breast cancer TME, chronic stimulation in vivo with TGFβ during the expansion process of donor NK cells produces TGFβ imprinted NK cells which exhibit high cytotoxicity and resistance to suppression by TGFβ due to down regulation of SMAD3 and hypersecretion of interferon gamma and tumor necrosis factor alpha. NK cell activation also promotes antibody dependent cellular cytotoxicity (ADCC) and augments monoclonal antibody directed killing. GD2 is a disialoganglioside and a tumor-associated antigen with limited expression in healthy tissues and overexpression in a variety of cancers, including breast cancer. Up to 60% of high grade breast cancers have GD2 expression with higher expression linked to worse overall survival. Given GD2 expression in aggressive BC subtypes and the role of NK cells in ADCC and antigen presentation, there is a strong rationale for evaluating an anti-GD2 antibody along with NK cells in the immunosuppressive breast TME. Based upon established preclinical data, we hypothesized that combining (1) an IgG1 anti-GD2 monoclonal antibody (anti-GD2), (2) TGFβ imprinted, IL-21 expanded allogenic universal donor NK cells (TGFβiNK) and (3) the standard chemotherapy agent gemcitabine (gem) which has immunomodulatory effects including improved NK cell function, will improve tumor control and survival in a TNBC mouse model. Methods: First, we evaluated if anti-GD2 treatment would increase ADCC against MD-MBA-231 tumor cells. To do this, tumor cells with or without anti-GD2 were cocultured with TGFβiNK + anti-GD2 and evaluated by real time cell analysis (RTCA). Next we evaluated the effect of gem on both tumor and immune cells. Finally to evaluate the effect of our novel combination therapy, female NSG mice were injected with 1 × 10e6 MDA-MB-231 cells into the mammary fat on day 0 and tumor burden was followed for 83 days. On day 21, mice were divided into 4 groups: control, gem only (standard of care), TGFβiNK+anti-GD2 (NK-GD2), and gem+TGFβiNK+anti-GD2 (G-NK-GD2) and tumor growth was measured weekly. Mice were observed for toxicity and bioluminescence evaluation for NK cells were performed prior to each cycle. Results: RTCA data confirmed anti-GD2 treatment increased ADCC mediated cell lysis by TGFβiNK. Furthermore, at clinically relevant doses, gem did not alter GD2 expression by flow cytometry on MDA-MB-231 tumor cells but induced MICA expression which suggests potential improved NK cell activity. In the mouse model, monitoring of mouse weights showed greater weight stability in the G-NK-GD2 group, suggesting a tolerable regimen. Serial measurement of tumors showed a significant reduction in tumor growth in NK-GD2 and G-NK-GD2 arms when compared to Gem or control. Additionally, mice who received G-NK-GD2 or NK-GD2 had significantly improved survival. Histological evaluation and immune assessment of the mice are ongoing. Conclusion: Our data showed no significant toxicities in the murine model and improved efficacy of G-NK-GD2 over the standard of care gemcitabine. Our preclinical data supports moving forward with a phase 1b/2 Bench-To-Bedside clinical trial, that will evaluate this combination in patients with aggressive subtypes of metastatic breast cancer where there is a great clinical need for effective treatments. Citation Format: Marcelo Pereira, Prashant Trikha, Sumithira Vasu, Zihai Li, Daniel Stover, Dean Lee, Margaret Gatti-Mays. Combination Therapy with an Anti-GD2 Antibody, Transforming Growth Factor Beta Imprinted Natural Killer Cells and Gemcitabine Improve Tumor Control and Survival in a Triple Negative Breast Cancer Model [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-20-03.

Este artículo se basó en la recreación de experiencias “Acompañamiento Comunitario Intercultural de URACCAN en la elaboración de los estatutos de gobernanza comunitaria de las comunidades de Kamla, Sumubila y Tuapi, Región Autónoma de la Costa Caribe Norte de Nicaragua”. Presenta los aportes de la 8 como Universidad Comunitaria Intercultural en los procesos de vinculación Universidad - Comunidad en el fortalecimiento de la gobernanza comunitaria en tres comunidades del municipio de Puerto Cabezas, mediante la elaboración de los estatutos de gobernanza comunal y planes de vida intercultural como documentos de referencia para la consolidación de su gobernanza, reglamentar sus derechos y obligaciones, sobre todo, establecer algunas pautas para el uso sostenible de los recursos naturales, entre otros aspectos para alcanzar el buen vivir de estas comunidades

During infection, animals exhibit adaptive changes in physiology and behavior aimed at increasing survival. Although many causes of infection exist, they trigger similar stereotyped symptoms such as fever, warmth seeking, loss of appetite and fatigue(1,2). Yet exactly how the nervous system alters body temperature and triggers sickness behaviors to coordinate responses to infection remains unknown. Here we identify a previously uncharacterized population of neurons in the ventral medial preoptic area (VMPO) of the hypothalamus that are activated after lipopolysaccharide (LPS)- and Poly (I:C)- induced sickness and are critical for generating a fever response and other sickness symptoms such as warmth-seeking and loss of appetite. Single-nucleus RNA-sequencing and multiplexed error-robust fluorescent in situ hybridization (MERFISH) uncovered the identity and distribution of LPS-activated VMPO (VMPO(LPS)) neurons and non-neuronal cells. Gene expression and electrophysiological measurements suggest a paracrine mechanism in which the release of immune signals by non-neuronal cells during infection activates nearby VMPO(LPS) neurons. Finally, we show that VMPO(LPS) neurons exert a broad influence on the activity of brain areas associated with behavioral and homeostatic functions and are synaptically and functionally connected to circuit nodes controlling body temperature and appetite. Together these results uncover VMPO(LPS) neurons as a control hub that integrates immune signals to orchestrate multiple sickness symptoms in response to infection.

Este artículo titulado El may pole en la memoria de los portadores culturales creoles de Puerto Cabezas, Región Autónoma de la Costa Caribe Norte de Nicaragua, alude a la danza caribeña palo de mayo y se han enfatizado los ritos asociados a la celebración de esta festividad que, representa un reencuentro con nuestras raíces históricas y culturales. La metodología es cualitativa con enfoque hermenéutico al basarse fundamentalmente en bibliografía y las entrevistas realizadas, el nivel de profundidad es descriptiva-explicativa ya que permitió reconstruir la memoria sobre lo andado y vivido y extraer las lecciones aprendidas o la experiencia de los portadores culturales afrodescendientes en la promoción y preservación del may pole en Puerto Cabezas. Al compartir sus conocimientos, saberes y prácticas los portadores culturales nos brindan la posibilidad de rescate del verdadero significado de la festividad y la revitalización de la misma en espacios privados y públicos.

We introduce a new approach for solving quantum many-body systems called wave function matching. Wave function matching transforms the interaction between particles so that the wave functions at short distances match that of an easily computable interaction. This allows for calculations of systems that would otherwise be impossible due to problems such as Monte Carlo sign cancellations. We apply the method to lattice Monte Carlo simulations of light nuclei, medium-mass nuclei, neutron matter, and nuclear matter. We use interactions at next-to-next-to-next-to-leading order in the framework of chiral effective field theory and find good agreement with empirical data. These results are accompanied by new insights on the nuclear interactions that may help to resolve long-standing challenges in accurately reproducing nuclear binding energies, charge radii, and nuclear matter saturation in ab initio calculations., 24 pages, 10 figues, 13 tables

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Colloquium provides a snapshot of nuclear physics research, which has been transformed by machine learning techniques.

Solving the generalized eigenvalue problem is a useful method for finding energy eigenstates of large quantum systems. It uses projection onto a set of basis states which are typically not orthogonal. One needs to invert a matrix whose entries are inner products of the basis states, and the process is unfortunately susceptible to even small errors. The problem is especially bad when matrix elements are evaluated using stochastic methods and have significant error bars. In this work, we introduce the trimmed sampling algorithm in order to solve this problem. Using the framework of Bayesian inference, we sample prior probability distributions determined by uncertainty estimates of the various matrix elements and likelihood functions composed of physics-informed constraints. The result is a probability distribution for the eigenvectors and observables which automatically comes with a reliable estimate of the error and performs far better than standard regularization methods. The method should have immediate use for a wide range of applications involving classical and quantum computing calculations of large quantum systems., Final version to appear in Physical Review Research. 6 pages and 3 figures (main), 5 pages and 8 pages (supplemental)

Lyme disease (LD) due to Borrelia burgdorferi is the most prevalent vector-borne disease in the United States. There is a poor understanding of how immunity contributes to bacterial control, pathology, or both during LD. Dogs in an area of endemicity were screened for B. burgdorferi and Anaplasma exposure and stratified according to seropositivity, presence of LD symptoms, and doxycycline treatment. Significantly elevated serum interleukin-21 (IL-21) and increased circulating CD3(+) CD94(+) lymphocytes with an NK-like CD8(+) T cell phenotype were predominant in asymptomatic dogs exposed to B. burgdorferi. Both CD94(+) T cells and CD3(−) CD94(+) lymphocytes, corresponding to NK cells, from symptomatic dogs expressed gamma interferon (IFN-γ) at a 3-fold-higher frequency upon stimulation with B. burgdorferi than the same subset among endemic controls. Surface expression of activating receptor NKp46 was reduced on CD94(+) T cells from LD, compared to cells after doxycycline treatment. A higher frequency of NKp46-expressing CD94(+) T cells correlated with significantly increased peripheral blood mononuclear cell (PBMC) cytotoxic activity via calcein release assay. PBMCs from dogs with symptomatic LD showed significantly reduced killing ability compared with endemic control PBMCs. An elevated NK-like CD8(+) T cell response was associated with protection against development of clinical LD, while excess IFN-γ was associated with clinical disease.

Variants in the AUTS2 gene are associated with a broad spectrum of neurological conditions characterized by intellectual disability, microcephaly, and congenital brain malformations. Here, we use a human cerebral organoid (CO) model to investigate the pathophysiology of a heterozygous de novo missense AUTS2 variant identified in a patient with multiple neurological impairments including primary microcephaly and profound intellectual disability. Proband COs exhibit reduced growth, deficits in neural progenitor cell (NPC) proliferation and disrupted NPC polarity within ventricular zone-like regions compared to control COs. We used CRISPR-Cas9-mediated gene editing to correct this variant and demonstrate rescue of impaired organoid growth and NPC proliferative deficits. Single-cell RNA sequencing revealed a marked reduction of G1/S transition gene expression and alterations in WNT-β-Catenin signaling within proband NPCs, uncovering a novel role for AUTS2 in NPCs during human cortical development. Collectively, these results underscore the value of COs to uncover molecular mechanisms underlying AUTS2 syndrome.

We present the reduced basis method as a tool for developing emulators for equations with tunable parameters within the context of the nuclear many-body problem. The method uses a basis expansion informed by a set of solutions for a few values of the model parameters and then projects the equations over a well-chosen low-dimensional subspace. We connect some of the results in the eigenvector continuation literature to the formalism of reduced basis methods and show how these methods can be applied to a broad set of problems. As we illustrate, the possible success of the formalism on such problems can be diagnosed beforehand by a principal component analysis. We apply the reduced basis method to the one-dimensional Gross-Pitaevskii equation with a harmonic trapping potential and to nuclear density functional theory for $^{48}$Ca, achieving speed-ups of more than x150 in both cases when compared to traditional solvers. The outstanding performance of the approach, together with its straightforward implementation, show promise for its application to the emulation of computationally demanding calculations, including uncertainty quantification.

This is a collection of perspective pieces contributed by the participants of the Institute of Nuclear Theory's Program on Nuclear Physics for Precision Nuclear Physics which was held virtually from April 19 to May 7, 2021. The collection represents the reflections of a vibrant and engaged community of researchers on the status of theoretical research in low-energy nuclear physics, the challenges ahead, and new ideas and strategies to make progress in nuclear structure and reaction physics, effective field theory, lattice QCD, quantum information, and quantum computing. The contributed pieces solely reflect the perspectives of the respective authors and do not represent the viewpoints of the Institute for Nuclear theory or the organizers of the program., Perspective pieces of the virtual INT program 21-1b "Nuclear Forces for Precision Nuclear Physics", 107 pages

We develop the impurity lattice Monte Carlo formalism, for the case of two distinguishable impurities in a bath of polarized fermions. The majority particles are treated as explicit degrees of freedom, while the impurities are described by worldlines. The latter serve as localized auxiliary fields, which affect the majority particles. We apply the method to non-relativistic three-dimensional systems of two impurities and a number of majority particles where both the impurity-impurity interaction and the impurity-majority interaction have zero range. We consider the case of an attractive impurity-majority interaction, and we study the formation and disintegration of bound states as a function of the impurity-impurity interaction strength. We also discuss the potential applications of this formalism to other quantum many-body systems., Comment: 7 pages, 4 figures

The introduction of chiral effective field theory by Steven Weinberg three decades ago has had a profound and lasting impact on nuclear physics. This brief review explores the impact of Weinberg's work on the field of nuclear lattice simulations. Rather than a summary of technical details, an effort is made to present the conceptual advances that made much of the recent progress possible., 12 pages, 2 figures, invited contribution to the special issue in Few-Body Systems "Celebrating 30 years of Steven Weinberg's papers on Nuclear Forces from Chiral Lagrangians"

Collisions of . light and heavy nuclei in relativistic heavy-ion collisions have been shown to be sensitive to nuclear structure. With a proposed $^{16}\mathrm{O}^{16}\mathrm{O}$ run at the CERN Large Hadron Collider (LHC) and at the BNL Relativistic Heavy Ion Collider (RHIC) we study the potential for finding $\ensuremath{\alpha}$ clustering in $^{16}\mathrm{O}$. Here we use the state-of-the-art iEBE-VISHNU package with $^{16}\mathrm{O}$ nucleonic configurations from ab initio nuclear lattice simulations. This setup was tuned using a Bayesian analysis on $p\mathrm{Pb}$ and PbPb systems. We find that the $^{16}\mathrm{O}^{16}\mathrm{O}$ system always begins far from equilibrium and that at LHC and RHIC it approaches the regime of hydrodynamic applicability only at very late times. Finally, by taking ratios of flow harmonics we are able to find measurable differences between $\ensuremath{\alpha}$-clustering, nucleonic, and subnucleonic degrees of freedom in the initial state.

X inactivation presents two longstanding puzzles: the counting and choice of X chromosomes. Here, we consider counting and choice in the context of pairing, both of the X and of the autosomes.

It is often computationally advantageous to model space as a discrete set of points forming a lattice grid. This technique is particularly useful for computationally difficult problems such as quantum many-body systems. For reasons of simplicity and familiarity, nearly all quantum many-body calculations have been performed on simple cubic lattices. Since the removal of lattice artifacts is often an important concern, it would be useful to perform calculations using more than one lattice geometry. In this work we show how to perform quantum many-body calculations using auxiliary-field Monte Carlo simulations on a three-dimensional body-centered cubic (BCC) lattice. As a benchmark test we compute the ground state energy of 33 spin-up and 33 spin-down fermions in the unitary limit, which is an idealized limit where the interaction range is zero and scattering length is infinite. As a fraction of the free Fermi gas energy $E_{\rm FG}$, we find that the ground state energy is $E_0/E_{\rm FG}= 0.369(2), 0.371(2),$ using two different definitions of the finite-system energy ratio. This is in excellent agreement with recent results obtained on a cubic lattice \cite{He:2019ipt}. We find that the computational effort and performance on a BCC lattice is approximately the same as that for a cubic lattice with the same number of lattice points. We discuss how the lattice simulations with different geometries can be used to constrain the size lattice artifacts in simulations of continuum quantum many-body systems., 13 pages, 7 figures

Primary tumors generate metastases by shedding tumor cells into the circulation; these circulating tumor cells (CTCs) implant at distant sites to develop into metastatic lesions. CTCs can travel either as clusters or as single CTCs. Previous studies revealed that the frequency of CTC clusters in a cancer patient positively correlates with the likelihood of developing metastatic lesions. Three key characteristics of CTC clusters - chemoresistance, reduced apoptosis, and epigenetically programmed stemness - enhance their metastatic potential relative to single CTCs: CTC clusters seem to be more resistant to chemotherapy due to their quiescent and necrotic cores, making drug penetration difficult. Their chemoresistance also correlates with specific molecular components of the extracellular matrix. CTC clusters suffer lower rates of apoptosis. This might be attributed to autocrine factors that protect against immune attack and the epithelial-mesenchymal transition. The DNA methylation landscape of CTC clusters closely resembles that of embryonic stem cells. It features hypomethylation of four critical transcription factors associated with stemness and hypermethylation of a set of pro-differentiation genes. Further research might focus on the interdependence of these three characteristics and whether they precede or follow the clustering of CTCs. The answers to these research questions will help drug developers define specific mechanisms that can curb the metastatic potential of CTC clusters.

The discovery of predictive biomarkers of response is critical for forecasting patient benefit from novel immune-modulatory therapeutics. However, their discovery is hindered by the complexity of the drug modes of action and the lack of adequate biological models. We designed an approach to define biomarkers of response by subjecting dissociated tissue from human tumors to T cell-targeting biologics or their combinations, followed by single-cell transcriptomic profiling and TCR clonotype characterization. Responding cells are identified as those showing treatment-specific shifts in gene expression profiles. Specifically for T cells, TCR clonotypes can be used to match responding T cells in treatment conditions to their sister clones in the baseline state. Comparing the baseline gene expression profiles between T cell clonotypes that responded to the treatment and those that did not allows the discovery of gene expression signatures that can predict response. We processed over 20 tumor samples obtained from cancer patients and treated them ex vivo with two novel biologics currently under clinical development - HFB301001, a potentially best-in-class 2nd generation OX40 agonist, and HFB200301, a potentially first-in-class TNFR2 agonist. We applied our biomarker discovery strategy to the pooled scRNA-seq and scTCR-seq data from these samples to define predictive signatures of response to these drugs. To further validate this strategy, we also generated single-cell data in our ex vivo system to characterize response to anti-PD-1 treatment. In the anti-PD-1 predictive response signature, we identified genes involved in inflammatory response and genes in the pathway of other co-inhibitory checkpoints. Application of the anti-PD-1 predictive response signature to bulk transcriptomic data from clinical studies with checkpoint inhibitors successfully stratified patients into two groups with significantly different risk of progression. The predictive response signatures for the two novel agonistic antibodies shared genes involved in inflammatory pathway with the anti-PD-1 signature, but also contained other distinct gene sets. Application of these predictive response signatures to bulk transcriptomic data from TCGA was able to stratify patients across cancer indications by predicted likelihood of response to individual treatments or combinations. The predictive biomarkers of response for the novel OX40 and TNFR2 agonist antibodies will be validated in our Phase I clinical trials. Ex vivo single-cell profiling coupled with TCR clonotype characterization enabled the discovery of predictive response signatures that informed patient selection strategies for the early clinical development of novel therapeutics. Citation Format: Dean Lee, Monika Manne, Roshan Kumar, Rebecca Silver, Alexandra Staskus, Julianna Crivello, Zhiyuan Wang, Dohyun Lee, Ross Fulton, Zhizhan Gu, Christos Hatzis, Francisco Adrian, Andreas Raue, Liang Schweizer. Discovery of predictive biomarkers of response to T cell-targeting biologics using ex vivo single-cell profiling coupled with TCR clonotype characterization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 618.

Eigenvector continuation is a computational method that finds the extremal eigenvalues and eigenvectors of a Hamiltonian matrix with one or more control parameters. It does this by projection onto a subspace of eigenvectors corresponding to selected training values of the control parameters. The method has proven to be very efficient and accurate for interpolating and extrapolating eigenvectors. However, almost nothing is known about how the method converges, and its rapid convergence properties have remained mysterious. In this letter we present the first study of the convergence of eigenvector continuation. In order to perform the mathematical analysis, we introduce a new variant of eigenvector continuation that we call vector continuation. We first prove that eigenvector continuation and vector continuation have identical convergence properties and then analyze the convergence of vector continuation. Our analysis shows that, in general, eigenvector continuation converges more rapidly than perturbation theory. The faster convergence is achieved by eliminating a phenomenon that we call differential folding, the interference between non-orthogonal vectors appearing at different orders in perturbation theory. From our analysis we can predict how eigenvector continuation converges both inside and outside the radius of convergence of perturbation theory. While eigenvector continuation is a non-perturbative method, we show that its rate of convergence can be deduced from power series expansions of the eigenvectors. Our results also yield new insights into the nature of divergences in perturbation theory., Comment: 5 pages and 4 figures (main text), 4 pages and 8 figures (supplemental), new analysis of the multi-parameter case, new application to BCS-BEC crossover and the unitary limit

It is not possible, using standard lattice techniques in Euclidean space, to calculate the complete fermionic spectrum of a quantum field theory. Algorithms running on quantum computers have the potential to access the theory with real-time evolution, enabling a direct computation. As a testing ground we consider the 1 + 1-dimensional Schwinger model with the presence of a {\theta} term using a staggered fermions discretization. We study the convergence properties of two different algorithms - adiabatic evolution and the Quantum Approximate Optimization Algorithm - with an emphasis on their cost in terms of CNOT gates. This is crucial to understand the feasibility of these algorithms, because calculations on near-term quantum devices depend on their rapid convergence. We also propose a blocked algorithm that has the first indications of a better scaling behavior with the dimensionality of the problem., Comment: 9 pages, 2 figures, 38th International Symposium on Lattice Field Theory, LATTICE2021 26th-30th July, 2021 Zoom/Gather@Massachusetts Institute of Technology

The strong interactions among nucleons have an approximate spin-isospin exchange symmetry that arises from the properties of quantum chromodynamics in the limit of many colors, $N_c$. However this large-$N_c$ symmetry is well hidden and reveals itself only when averaging over intrinsic spin orientations. Furthermore, the symmetry is obscured unless the momentum resolution scale is close to an optimal scale that we call $\Lambda_{{\rm large-}N_c}$. We show that the large-$N_c$ derivation requires a momentum resolution scale of $\Lambda_{{\rm large-}N_c} \sim 500$ MeV. We derive a set of spin-isospin exchange sum rules and discuss implications for the spectrum of $^{30}$P and applications to nuclear forces, nuclear structure calculations, and three-nucleon interactions., Comment: 5 pages (main) + 3 pages (supplemental materials), 1 figure (main) + 4 figures (supplemental materials), final version to appear in Phys. Rev. Lett

We present lattice calculations of the low-lying spectrum of $^{12}$C using a simple nucleon-nucleon interaction that is independent of spin and isospin and therefore invariant under Wigner's SU(4) symmetry. We find strong signals for all excited states up to $\sim 15$~MeV above the ground state, and explore the structure of each state using a large variety of $\alpha$ cluster and harmonic oscillator trial states, projected onto given irreducible representations of the cubic group. We are able to verify earlier findings for the $\alpha$ clustering in the Hoyle state and the second $2^+$ state of $^{12}$C. The success of these calculations to describe the full low-lying energy spectrum using spin-independent interactions suggest that either the spin-orbit interactions are somewhat weak in the $^{12}$C system, or the effects of $\alpha$ clustering are diminishing their influence. This is in agreement with previous findings from {\it ab initio} shell model calculations., Comment: 9 pages, 9 figures, discussion extended, references added

We show that the {\it ab initio} calculations of nuclear thermodynamics can be performed efficiently using lattice effective field theory. The simulations use a new approach called the pinhole trace algorithm to calculate thermodynamic observables for a fixed number of protons and neutrons enclosed in a finite box. In this framework, we calculate the equation of state, the liquid-vapor coexistence line and the critical point of neutral symmetric nuclear matter with high precision. Since the algorithm uses a canonical ensemble with a fixed number of particles, it provides a sizable computational advantage over grand canonical ensemble simulations that can be a factor of several thousands to as much as several millions for large volume simulations., Comment: 17 pages, plenary talk at the 38th International Symposium on Lattice Field Theory (Lattice 2021), 26-30 July 2021, Zoom/Gather @ MIT, USA

Emulators that can bypass computationally expensive scientific calculations with high accuracy and speed can enable new studies of fundamental science as well as more potential applications. In this work we discuss solving a system of constraint equations efficiently using a self-learning emulator. A self-learning emulator is an active learning protocol that can be used with any emulator that faithfully reproduces the exact solution at selected training points. The key ingredient is a fast estimate of the emulator error that becomes progressively more accurate as the emulator is improved, and the accuracy of the error estimate can be corrected using machine learning. We illustrate with three examples. The first uses cubic spline interpolation to find the solution of a transcendental equation with variable coefficients. The second example compares a spline emulator and a reduced basis method emulator to find solutions of a parameterized differential equation. The third example uses eigenvector continuation to find the eigenvectors and eigenvalues of a large Hamiltonian matrix that depends on several control parameters., Comment: 6 + 5 pages (main + supplemental), 5 + 7 figures (main + supplemental), additional discussion, references, and examples added

Quantum state preparation by adiabatic evolution is currently rendered ineffective by the long implementation times of the underlying quantum circuits, comparable to the decoherence time of present and near-term quantum devices. These implementation times can be significantly reduced by realizing the evolution with a minimal number of customized gates. Employing a realistic model of a two-qubit processor, we carried out classical device-level simulations of the adiabatic evolution of a two-spin system implemented with customized two-qubit gates. These device-level simulations were compared with (experimental) ones solving the same problem on IBMQ systems. When used to emulate the IBMQ quantum circuit, our device-level simulations reached state fidelities ranging from 65% to 85%, similar to the actual performance of a diverse set of IBMQ devices.When we reduced the implementation time by using a minimal number of customized gates, however, the loss of fidelity was reduced by at least a factor of four, allowing us to accurately extract the energy of the target state. This improvement is enough to render adiabatic evolution useful for quantum state preparation for small systems or as a preconditioner for other state preparation methods., Comment: 12 pages, 10 figures