arXiv subjects

Peter Mueller

Publications and source records attributed to Peter Mueller.

At least 37 records · Page 2Linked to original sources

arXiv2022

A system design approach toward integrated cryogenic quantum control systems

In this paper, we provide a system level perspective on the design of control electronics for large scale quantum systems. Quantum computing systems with high-fidelity control and readout, coherent coupling, calibrated gates, and reconfigurable circuits with low error rates are expected to have superior quantum volumes. Cryogenic CMOS plays a crucial role in the realization of scalable quantum computers, by minimizing the feature size, lowering the cost, power consumption, and implementing low latency error correction. Our approach toward achieving scalable feed-back based control systems includes the design of memory based arbitrary waveform generators (AWG's), wide band radio frequency analog to digital converters, integrated amplifier chain, and state discriminators that can be synchronized with gate sequences. Digitally assisted designs, when implemented in an advanced CMOS node such as 7 nm can reap the benefits of low power due to scaling. A qubit readout chain demands several amplification stages before the digitizer. We propose the co-integration of our in-house developed InP HEMT LNAs with CMOS LNA stages to achieve the required gain at the digitizer input with minimal area. Our approach using high impedance matching between the HEMT LNA and the cryogenic CMOS receiver can relax the design constraints of an inverter-based CMOS LNA, paving the way toward a fully integrated qubit readout chain. The qubit state discriminator consists of a digital signal processor that computes the qubit state from the digitizer output and a pre-determined threshold. The proposed system realizes feedback-based optimal control for error mitigation and reduction of the required data rate through the serial interface to room temperature electronics.

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arXiv2022

Bayesian Scalable Precision Factor Analysis for Massive Sparse Gaussian Graphical Models

We propose a novel approach to estimating the precision matrix of multivariate Gaussian data that relies on decomposing them into a low-rank and a diagonal component. Such decompositions are very popular for modeling large covariance matrices as they admit a latent factor based representation that allows easy inference. The same is however not true for precision matrices due to the lack of computationally convenient representations which restricts inference to low-to-moderate dimensional problems. We address this remarkable gap in the literature by building on a latent variable representation for such decomposition for precision matrices. The construction leads to an efficient Gibbs sampler that scales very well to high-dimensional problems far beyond the limits of the current state-of-the-art. The ability to efficiently explore the full posterior space also allows the model uncertainty to be easily assessed. The decomposition crucially additionally allows us to adapt sparsity inducing priors to shrink the insignificant entries of the precision matrix toward zero, making the approach adaptable to high-dimensional small-sample-size sparse settings. Exact zeros in the matrix encoding the underlying conditional independence graph are then determined via a novel posterior false discovery rate control procedure. A near minimax optimal posterior concentration rate for estimating precision matrices is attained by our method under mild regularity assumptions. We evaluate the method's empirical performance through synthetic experiments and illustrate its practical utility in data sets from two different application domains.

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arXiv2022

A Recommender System Based on a Double Feature Allocation Model

A collaborative filtering recommender system predicts user preferences by discovering common features among users and items. We implement such inference using a Bayesian double feature allocation model, that is, a model for random pairs of subsets. We use an Indian buffet process (IBP) to link users and items to features. Here a feature is a subset of users and a matching subset of items. By training feature-specific rating effects, we predict ratings. We use MovieLens Data to demonstrate posterior inference in the model and prediction of user preferences for unseen items compared to items they have previously rated. Part of the implementation is a novel semi-consensus Monte Carlo method to accomodate large numbers of users and items, as is typical for related applications. The proposed approach implements parallel posterior sampling in multiple shards of users while sharing item-related global parameters across shards.

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arXiv2021

A Unified Decision Framework for Phase I Dose-Finding Designs

The purpose of a phase I dose-finding clinical trial is to investigate the toxicity profiles of various doses for a new drug and identify the maximum tolerated dose. Over the past three decades, various dose-finding designs have been proposed and discussed, including conventional model-based designs, new model-based designs using toxicity probability intervals, and rule-based designs. We present a simple decision framework that can generate several popular designs as special cases. We show that these designs share common elements under the framework, such as the same likelihood function, the use of loss functions, and the nature of the optimal decisions as Bayes rules. They differ mostly in the choice of the prior distributions. We present theoretical results on the decision framework and its link to specific and popular designs like mTPI, BOIN, and CRM. These results provide useful insights into the designs and their underlying assumptions, and convey information to help practitioners select an appropriate design.

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arXiv2021

Surface Processing and Discharge-Conditioning of High Voltage Electrodes for the Ra EDM Experiment

The Ra EDM experiment uses a pair of high voltage electrodes to search for the atomic electric dipole moment of $^{225}$Ra. We use identical, plane-parallel electrodes with a primary high gradient surface of 200 mm$^2$ to generate reversible DC electric fields. Our statistical sensitivity is linearly proportional to the electric field strength in the electrode gap. We adapted surface decontamination and processing techniques from accelerator physics literature to chemical polish and clean a suite of newly fabricated large-grain niobium and grade-2 titanium electrodes. Three pairs of niobium electrodes and one pair of titanium electrodes were discharge-conditioned with a custom high voltage test station at electric field strengths as high as $+52.5$ kV/mm and $-51.5$ kV/mm over electrode gap sizes ranging from 0.4 mm to 2.5 mm. One pair of large-grain niobium electrodes was discharge-conditioned and validated to operate at $\pm 20$ kV/mm with steady-state leakage current $\leq 25$ pA ($1σ$) and a polarity-averaged $98 \pm 19$ discharges per hour. These electrodes were installed in the Ra EDM experimental apparatus, replacing a copper electrode pair, and were revalidated to $\pm 20$ kV/mm. The niobium electrodes perform at an electric field strength 3.1 times larger than the legacy copper electrodes and are ultimately limited by the maximum output of our 30 kV bipolar power supply.

physics.ins-det↗

arXiv2021

Search Algorithms and Loss Functions for Bayesian Clustering

We propose a randomized greedy search algorithm to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. Given the large size and awkward discrete nature of the search space, the minimization of the posterior expected loss is challenging. Our approach is a stochastic search based on a series of greedy optimizations performed in a random order and is embarrassingly parallel. We consider several loss functions, including Binder loss and variation of information. We note that criticisms of Binder loss are the result of using equal penalties of misclassification and we show an efficient means to compute Binder loss with potentially unequal penalties. Furthermore, we extend the original variation of information to allow for unequal penalties and show no increased computational costs. We provide a reference implementation of our algorithm. Using a variety of examples, we show that our method produces clustering estimates that better minimize the expected loss and are obtained faster than existing methods.

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arXiv2020

The Dependent Dirichlet Process and Related Models

Standard regression approaches assume that some finite number of the response distribution characteristics, such as location and scale, change as a (parametric or nonparametric) function of predictors. However, it is not always appropriate to assume a location/scale representation, where the error distribution has unchanging shape over the predictor space. In fact, it often happens in applied research that the distribution of responses under study changes with predictors in ways that cannot be reasonably represented by a finite dimensional functional form. This can seriously affect the answers to the scientific questions of interest, and therefore more general approaches are indeed needed. This gives rise to the study of fully nonparametric regression models. We review some of the main Bayesian approaches that have been employed to define probability models where the complete response distribution may vary flexibly with predictors. We focus on developments based on modifications of the Dirichlet process, historically termed dependent Dirichlet processes, and some of the extensions that have been proposed to tackle this general problem using nonparametric approaches.

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arXiv2020

Consensus Monte Carlo for Random Subsets using Shared Anchors

We present a consensus Monte Carlo algorithm that scales existing Bayesian nonparametric models for clustering and feature allocation to big data. The algorithm is valid for any prior on random subsets such as partitions and latent feature allocation, under essentially any sampling model. Motivated by three case studies, we focus on clustering induced by a Dirichlet process mixture sampling model, inference under an Indian buffet process prior with a binomial sampling model, and with a categorical sampling model. We assess the proposed algorithm with simulation studies and show results for inference with three datasets: an MNIST image dataset, a dataset of pancreatic cancer mutations, and a large set of electronic health records (EHR). Supplementary materials for this article are available online.

stat.CO↗

arXiv2019

Opportunities for Nuclear Physics & Quantum Information Science

This whitepaper is an outcome of the workshop Intersections between Nuclear Physics and Quantum Information held at Argonne National Laboratory on 28-30 March 2018 [www.phy.anl.gov/npqi2018/]. The workshop brought together 116 national and international experts in nuclear physics and quantum information science to explore opportunities for the two fields to collaborate on topics of interest to the U.S. Department of Energy (DOE) Office of Science, Office of Nuclear Physics, and more broadly to U.S. society and industry. The workshop consisted of 22 invited and 10 contributed talks, as well as three panel discussion sessions. Topics discussed included quantum computation, quantum simulation, quantum sensing, nuclear physics detectors, nuclear many-body problem, entanglement at collider energies, and lattice gauge theories.

nucl-th↗

arXiv2019

Pulsed reset protocol for fixed-frequency superconducting qubits

Improving coherence times of quantum bits is a fundamental challenge in the field of quantum computing. With long-lived qubits it becomes, however, inefficient to wait until the qubits have relaxed to their ground state after completion of an experiment. Moreover, for error-correction schemes it is import to rapidly re-initialize ancilla parity-check qubits. We present a simple pulsed qubit reset protocol based on a two-pulse sequence. A first pulse transfers the excited state population to a higher excited qubit state and a second pulse into a lossy environment provided by a low-Q transmission line resonator, which is also used for qubit readout. We show that the remaining excited state population can be suppressed to $2.2\pm0.8\%$ and utilize the pulsed reset protocol to carry out experiments at enhanced rates.

quant-ph↗

arXiv2019

Bayesian Double Feature Allocation for Phenotyping with Electronic Health Records

We propose a categorical matrix factorization method to infer latent diseases from electronic health records (EHR) data in an unsupervised manner. A latent disease is defined as an unknown biological aberration that causes a set of common symptoms for a group of patients. The proposed approach is based on a novel double feature allocation model which simultaneously allocates features to the rows and the columns of a categorical matrix. Using a Bayesian approach, available prior information on known diseases greatly improves identifiability and interpretability of latent diseases. This includes known diagnoses for patients and known association of diseases with symptoms. We validate the proposed approach by simulation studies including mis-specified models and comparison with sparse latent factor models. In the application to Chinese EHR data, we find interesting results, some of which agree with related clinical and medical knowledge.

stat.AP↗

arXiv2019

The Low-Radioactivity Underground Argon Workshop: A workshop synopsis

In response to the growing need for low-radioactivity argon, community experts and interested parties came together for a 2-day workshop to discuss the worldwide low-radioactivity argon needs and the challenges associated with its production and characterization. Several topics were covered: experimental needs and requirements for low-radioactivity argon, the sources of low-radioactivity argon and its production, how long-lived argon radionuclides are created in nature, measuring argon radionuclides, and other applicable topics. The Low-Radioactivity Underground Argon (LRUA) workshop took place on March 19-20, 2018 at Pacific Northwest National Laboratory in Richland Washington, USA. This paper is a synopsis of the workshop with the associated abstracts from the talks.

physics.ins-det↗

arXiv2018

Gate-efficient simulation of molecular eigenstates on a quantum computer

A key requirement to perform simulations of large quantum systems on near-term quantum hardware is the design of quantum algorithms with short circuit depth that finish within the available coherence time. A way to stay within the limits of coherence is to reduce the number of gates by implementing a gate set that matches the requirements of the specific algorithm of interest directly in hardware. Here, we show that exchange-type gates are a promising choice for simulating molecular eigenstates on near-term quantum devices since these gates preserve the number of excitations in the system. Complementing the theoretical work by Barkoutsos et al. [PRA 98, 022322 (2018)], we report on the experimental implementation of a variational algorithm on a superconducting qubit platform to compute the eigenstate energies of molecular hydrogen. We utilize a parametrically driven tunable coupler to realize exchange-type gates that are configurable in amplitude and phase on two fixed-frequency superconducting qubits. With gate fidelities around 95% we are able to compute the eigenstates within an accuracy of 50 mHartree on average, a limit set by the coherence time of the tunable coupler.

quant-ph↗

arXiv2018

A Nonparametric Bayesian Basket Trial Design

Targeted therapies on the basis of genomic aberrations analysis of the tumor have shown promising results in cancer prognosis and treatment. Regardless of tumor type, trials that match patients to targeted therapies for their particular genomic aberrations have become a mainstream direction of therapeutic management of patients with cancer. Therefore, finding the subpopulation of patients who can most benefit from an aberration-specific targeted therapy across multiple cancer types is important. We propose an adaptive Bayesian clinical trial design for patient allocation and subpopulation identification. We start with a decision theoretic approach, including a utility function and a probability model across all possible subpopulation models. The main features of the proposed design and population finding methods are that we allow for variable sets of covariates to be recorded by different patients, adjust for missing data, allow high order interactions of covariates, and the adaptive allocation of each patient to treatment arms using the posterior predictive probability of which arm is best for each patient. The new method is demonstrated via extensive simulation studies.

stat.AP↗

arXiv2018

Entanglement generation in superconducting qubits using holonomic operations

We investigate a non-adiabatic holonomic operation that enables us to entangle two fixed-frequency superconducting transmon qubits attached to a common bus resonator. Two coherent microwave tones are applied simultaneously to the two qubits and drive transitions between the first excited resonator state and the second excited state of each qubit. The cyclic evolution within this effective 3-level $Λ$-system gives rise to a holonomic operation entangling the two qubits. Two-qubit states with 95\% fidelity, limited mainly by charge-noise of the current device, are created within $213~\rm{ns}$. This scheme is a step toward implementing a SWAP-type gate directly in an all-microwave controlled hardware platform. By extending the available set of two-qubit operations in the fixed-frequency qubit architecture, the proposed scheme may find applications in near-term quantum applications using variational algorithms to efficiently create problem-specific trial states.

quant-ph↗

arXiv2018

Heterogeneous Reciprocal Graphical Models

We develop novel hierarchical reciprocal graphical models to infer gene networks from heterogeneous data. In the case of data that can be naturally divided into known groups, we propose to connect graphs by introducing a hierarchical prior across group-specific graphs, including a correlation on edge strengths across graphs. Thresholding priors are applied to induce sparsity of the estimated networks. In the case of unknown groups, we cluster subjects into subpopulations and jointly estimate cluster-specific gene networks, again using similar hierarchical priors across clusters. We illustrate the proposed approach by simulation studies and two applications with multiplatform genomic data for multiple cancers.

stat.ME↗

arXiv2017

TreeClone: Reconstruction of Tumor Subclone Phylogeny Based on Mutation Pairs using Next Generation Sequencing Data

We present TreeClone, a latent feature allocation model to reconstruct tumor subclones subject to phylogenetic evolution that mimics tumor evolution. Similar to most current methods, we consider data from next-generation sequencing of tumor DNA. Unlike most methods that use information in short reads mapped to single nucleotide variants (SNVs), we consider subclone phylogeny reconstruction using pairs of two proximal SNVs that can be mapped by the same short reads. As part of the Bayesian inference model, we construct a phylogenetic tree prior. The use of the tree structure in the prior greatly strengthens inference. Only subclones that can be explained by a phylogenetic tree are assigned non-negligible probabilities. The proposed Bayesian framework implies posterior distributions on the number of subclones, their genotypes, cellular proportions, and the phylogenetic tree spanned by the inferred subclones. The proposed method is validated against different sets of simulated and real-world data using single and multiple tumor samples. An open source software package is available at http://www.compgenome.org/treeclone.

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arXiv2017

Fermionic Hamiltonians for quantum simulations: a general reduction scheme

Many-body fermionic quantum calculations performed on analog quantum computers are restricted by the presence of k-local terms, which represent interactions among more than two qubits. These originate from the fermion-to-qubit mapping applied to the electronic Hamiltonians. Current solutions to this problem rely on perturbation theory in an enlarged Hilbert space. The main challenge associated with this technique is that it relies on coupling constants with very different magnitudes. This prevents its implementation in currently available architectures. In order to resolve this issue, we present an optimization scheme that unfolds the k-local terms into a linear combination of 2-local terms, while ensuring the conservation of all relevant physical properties of the original Hamiltonian, with several orders of magnitude smaller variation of the coupling constants.

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