Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
2,709
result(s) for
"Quintana, Chris"
Sort by:
Deadly fable
\"The Might Beyond the Mirror, the great threat that Batman assembled the Justice League of America to stop, has finally arrived in the form of the Queen of Fables. With her reality-warping power, her intent is to take over the real world and the imaginary and rule over everything--and without Batman, does the Justice League stand a chance against her? Can their mysetrious new member, Promethea, help them? Or will reality crumble at the Queen's whim?\"-- Provided by publisher.
Book
Optimizing quantum gates towards the scale of logical qubits
A foundational assumption of quantum error correction theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Two major challenges that could become fundamental roadblocks are manufacturing high-performance quantum hardware and engineering a control system that can reach its performance limits. The control challenge of scaling quantum gates from small to large processors without degrading performance often maps to non-convex, high-constraint, and time-dynamic control optimization over an exponentially expanding configuration space. Here we report on a control optimization strategy that can scalably overcome the complexity of such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunable superconducting qubits to execute single- and two-qubit gates while mitigating computational errors. When combined with a comprehensive model of physical errors across our processor, the strategy suppresses physical error rates by ~3.7× compared with the case of no optimization. Furthermore, it is projected to achieve a similar performance advantage on a distance-23 surface code logical qubit with 1057 physical qubits. Our control optimization strategy solves a generic scaling challenge in a way that can be adapted to a variety of quantum operations, algorithms, and computing architectures.
Ensuring high-fidelity quantum gates while increasing the number of qubits poses a great challenge. Here the authors present a scalable strategy for optimizing frequency trajectories as a form of error mitigation on a 68-qubit superconducting quantum processor, demonstrating high single- and two-qubit gate fidelities.
Journal Article
When classroom interactions have to go online: the move to specifications grading in a project-based design course
Purpose
The events surrounding the COVID-19 crisis had a profound effect on higher education, forcing students and instructors to face a sudden transition to wholly online learning contexts. This paper aims to examine how the design of a residential course was adapted to an online context and how this adaptation may prove beneficial to future iterations of the course.
Design/methodology/approach
This analysis centers on a master’s-level course in which students design software to support learning. One of the major changes to the course revolves around the transition from a traditional rubric-based grading scheme to a specifications grading system. This latter approach provides a series of binary (pass/fail) requirements (specifications) that students must meet to pass. Various forms of interactions were also altered during the transition; the authors investigate these in the paper.
Findings
This study found that the move to specifications grading helped students and the instructor to focus on the important work of meeting course learning goals. The approach also aligned well with authentic scenarios in which software projects are tested against certain specifications. Finally, this study concludes that thinking about specifications grading in the future can help us to develop more resilient pedagogical design approaches that respond to various forms of disruptions and changes.
Originality/value
The course design insights described in this paper illustrate alternative ways of instruction that can be especially useful during times of emergency, but which may also provide an added level of authenticity and learner motivation during times of stability.
Journal Article
Quantum supremacy using a programmable superconducting processor
The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor
1
. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits
2
–
7
to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 2
53
(about 10
16
). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy
8
–
14
for this specific computational task, heralding a much-anticipated computing paradigm.
Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.
Journal Article
Quantum approximate optimization of non-planar graph problems on a planar superconducting processor
Faster algorithms for combinatorial optimization could prove transformative for diverse areas such as logistics, finance and machine learning. Accordingly, the possibility of quantum enhanced optimization has driven much interest in quantum technologies. Here we demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA). Like past QAOA experiments, we study performance for problems defined on the planar connectivity graph native to our hardware; however, we also apply the QAOA to the Sherrington–Kirkpatrick model and MaxCut, non-native problems that require extensive compilation to implement. For hardware-native problems, which are classically efficient to solve on average, we obtain an approximation ratio that is independent of problem size and observe that performance increases with circuit depth. For problems requiring compilation, performance decreases with problem size. Circuits involving several thousand gates still present an advantage over random guessing but not over some efficient classical algorithms. Our results suggest that it will be challenging to scale near-term implementations of the QAOA for problems on non-native graphs. As these graphs are closer to real-world instances, we suggest more emphasis should be placed on such problems when using the QAOA to benchmark quantum processors.It is hoped that quantum computers may be faster than classical ones at solving optimization problems. Here the authors implement a quantum optimization algorithm over 23 qubits but find more limited performance when an optimization problem structure does not match the underlying hardware.
Journal Article
Exponential suppression of bit or phase errors with cyclic error correction
Realizing the potential of quantum computing requires sufficiently low logical error rates
1
. Many applications call for error rates as low as 10
−15
(refs.
2
–
9
), but state-of-the-art quantum platforms typically have physical error rates near 10
−3
(refs.
10
–
14
). Quantum error correction
15
–
17
promises to bridge this divide by distributing quantum logical information across many physical qubits in such a way that errors can be detected and corrected. Errors on the encoded logical qubit state can be exponentially suppressed as the number of physical qubits grows, provided that the physical error rates are below a certain threshold and stable over the course of a computation. Here we implement one-dimensional repetition codes embedded in a two-dimensional grid of superconducting qubits that demonstrate exponential suppression of bit-flip or phase-flip errors, reducing logical error per round more than 100-fold when increasing the number of qubits from 5 to 21. Crucially, this error suppression is stable over 50 rounds of error correction. We also introduce a method for analysing error correlations with high precision, allowing us to characterize error locality while performing quantum error correction. Finally, we perform error detection with a small logical qubit using the 2D surface code on the same device
18
,
19
and show that the results from both one- and two-dimensional codes agree with numerical simulations that use a simple depolarizing error model. These experimental demonstrations provide a foundation for building a scalable fault-tolerant quantum computer with superconducting qubits.
Repetition codes running many cycles of quantum error correction achieve exponential suppression of errors with increasing numbers of qubits.
Journal Article
Delayed presentation: negative pressure pulmonary hemorrhage
Negative pressure pulmonary hemorrhage (NPPH) is a rare, life-threatening complication that develops after an acute upper airway obstruction. A 26 year old, healthy African-American man with no underlying lung disease developed negative pressure pulmonary edema and subsequently NPPH during recovery from general anesthesia for elective spine surgery. Diagnostic bronchoscopy confirmed an alveolar source of the bleeding. Clinical improvement was quick with supportive care in the medical intensive care unit.
Journal Article
Towards Culturally Relevant Personalization at Scale: Experiments with Data Science Learners
In this article we describe our experiences building a large-scale data science program aimed at supporting diversity in online data science learning. This program was built to support a set of introductory skills-based, higher education courses. We are motivated by work done in project-based learning contexts and culturally responsive pedagogies and are particularly keen to understand how we can scale such kinds of approaches to large and diverse global classrooms. Specifically, we consider the country from which a learner is accessing the course as the key context for our work and discuss two interrelated investigations we have undertaken to understand how this feature interacts with learners’ motivation and learning. Our findings provide insights on how learners respond to location-specific problem-based personalizations in data science education and provides an initial exploration as to how this form of personalization differs depending on the geo-political context of the learners.
Journal Article
A Scaffolding Design Framework for Software to Support Science Inquiry
The notion of scaffolding learners to help them succeed in solving problems otherwise too difficult for them is an important idea that has extended into the design of scaffolded software tools for learners. However, although there is a growing body of work on scaffolded tools, scaffold design, and the impact of scaffolding, the field has not yet converged on a common theoretical framework that defines rationales and approaches to guide the design of scaffolded tools. In this article, we present a scaffolding design framework addressing scaffolded software tools for science inquiry. Developed through iterative cycles of inductive and theory-based analysis, the framework synthesizes the work of prior design efforts, theoretical arguments, and empirical work in a set of guidelines that are organized around science inquiry practices and the challenges learners face in those practices. The framework can provide a basis for developing a theory of pedagogical support and a mechanism to describe successful scaffolding approaches. It can also guide design, not in a prescriptive manner but by providing designers with heuristics and examples of possible ways to address the challenges learners face.
Journal Article