Implicit neural representations (INRs) have achieved impressive results for scene reconstruction and computer graphics, where their performance has primarily been assessed on reconstruction accuracy. As INRs make their way into other domains, where model predictions inform high-stakes decision-making, uncertainty quantification of INR inference is becoming critical. To that end, we study a Bayesian reformulation of INRs, UncertaINR, in the context of computed tomography, and evaluate several Bayesian deep learning implementations in terms of accuracy and calibration. We find that they achieve well-calibrated uncertainty, while retaining accuracy competitive with other classical, INR-based, and CNN-based reconstruction techniques. Contrary to common intuition in the Bayesian deep learning literature, we find that INRs obtain the best calibration with computationally efficient Monte Carlo dropout, outperforming Hamiltonian Monte Carlo and deep ensembles. Moreover, in contrast to the best-performing prior approaches, UncertaINR does not require a large training dataset, but only a handful of validation images.

Impact of ionizing radiation on superconducting qubit coherence

Antti P Vepsäläinen, Amir H Karamlou, John L Orrell, Akshunna S Dogra, Ben Loer, Francisca Vasconcelos, and
7 more authors

Technologies that rely on quantum bits (qubits) require long coherence times and high-fidelity operations. Superconducting qubits are one of the leading platforms for achieving these objectives. However, the coherence of superconducting qubits is affected by the breaking of Cooper pairs of electrons. The experimentally observed density of the broken Cooper pairs, referred to as quasiparticles, is orders of magnitude higher than the value predicted at equilibrium by the Bardeen-Cooper-Schrieffer theory of superconductivity. Previous work has shown that infrared photons considerably increase the quasiparticle density, yet even in the best-isolated systems, it remains much higher than expected, suggesting that another generation mechanism exists. Here we provide evidence that ionizing radiation from environmental radioactive materials and cosmic rays contributes to this observed difference. The effect of ionizing radiation leads to an elevated quasiparticle density, which we predict would ultimately limit the coherence times of superconducting qubits of the type measured here to milliseconds. We further demonstrate that radiation shielding reduces the flux of ionizing radiation and thereby increases the energy-relaxation time. Albeit a small effect for today’s qubits, reducing or mitigating the impact of ionizing radiation will be critical for realizing fault-tolerant superconducting quantum computers.

Generating spatially entangled itinerant photons with waveguide quantum electrodynamics

Bharath Kannan, Daniel L Campbell, Francisca Vasconcelos, Roni Winik, David K Kim, Morten Kjaergaard, and
7 more authors

Realizing a fully connected network of quantum processors requires the ability to distribute quantum entanglement. For distant processing nodes, this can be achieved by generating, routing, and capturing spatially entangled itinerant photons. In this work, we demonstrate the deterministic generation of such photons using superconducting transmon qubits that are directly coupled to a waveguide. In particular, we generate two-photon N00N states and show that the state and spatial entanglement of the emitted photons are tunable via the qubit frequencies. Using quadrature amplitude detection, we reconstruct the moments and correlations of the photonic modes and demonstrate state preparation fidelities of 84%. Our results provide a path toward realizing quantum communication and teleportation protocols using itinerant photons generated by quantum interference within a waveguide quantum electrodynamics architecture.

conference

Extending Quantum State Tomography for Superconducting Quantum Processors

Francisca Vasconcelos, Morten Kjaergaard, Tim Menke, Simon Gustavsson, Terry P Orlando, and William D Oliver

Quantum state tomography (QST), or the reconstruction of the density matrix of a quantum state via measurements, is critcal to ensure the proper functionality of qubits and quantum operations in a quantum computer. In this work, we extend existing QST code based on Maximum Likelihood Estimation from two to an arbitrary number of qubits and from one to arbitrarily many energy levels. A 100x algorithmic speedup is achieved over the original implementation. However, the exponential scaling of the density matrix makes this MLE-based algorithm infeasible for analysis over 6-qubits on a standard computer. To mitigate this limitation, we propose a novel deep-learning based approach to QST. Utilizing the CycleGAN architecture from the field of computer vision, we aim to address the issues of scalability and bias that plague current QST implementations.

Person-Following UAVs

Francisca Vasconcelos, and Nuno Vasconcelos

In IEEE Winter Conference on Applications of Computer Vision (WACV), 2016

We consider the design of vision-based control algorithms for unmanned aerial vehicles (UAVs), so as to enable a UAV to autonomously follow a person. A new vision-based control architecture is proposed with the goals of 1) robustly following the user and 2) implementing following behaviors programmed by manipulation of visual patterns. This is achieved within a detection/tracking paradigm, where the target is a programmable badge worn by the user. This badge contains a visual pattern with two components. The first is fixed and used to locate the user. The second is variable and implements a code used to program the UAV behavior. A biologically inspired tracking/recognition architecture, combining bottom-up and top-down saliency mechanisms, a novel image similarity measure, and an affine validation procedure, is proposed to detect the badge in the scene. The badge location is used by a control algorithm to adjust the UAV flight parameters so as to maintain the user in the center of the field of view. The detected badge is further analyzed to extract the visual code that commands the UAV behavior This is used to control the height and distance of the UAV relative to the user.

This article provides an overview of the history, theoretical basis, and different implementations of quantum computers. In Fall 2018, four MIT faculty – Isaac Chuang, Dirk Englund, Aram Harrow, and William Oliver – at the forefront of quantum computation and information research were interviewed. They provided personal perspectives on the development of the field, as well as insight to its near-term trajectory. There has been a lot of recent media hype surrounding quantum computation, so in this article we present an academic view of the matter, specifically highlighting progress being made at MIT.

thesis

Uncertainty in Implicit Neural Representations for Medical Imaging

Francisca Vasconcelos

University of Oxford, Sep 2021

Submitted in completion of an MSc in Statistical Sciences.