53,524 research outputs found
Fast quantum gate design with deep reinforcement learning using real-time feedback on readout signals
The design of high-fidelity quantum gates is difficult because it requires
the optimization of two competing effects, namely maximizing gate speed and
minimizing leakage out of the qubit subspace. We propose a deep reinforcement
learning algorithm that uses two agents to address the speed and leakage
challenges simultaneously. The first agent constructs the qubit in-phase
control pulse using a policy learned from rewards that compensate short gate
times. The rewards are obtained at intermediate time steps throughout the
construction of a full-length pulse, allowing the agent to explore the
landscape of shorter pulses. The second agent determines an out-of-phase pulse
to target leakage. Both agents are trained on real-time data from noisy
hardware, thus providing model-free gate design that adapts to unpredictable
hardware noise. To reduce the effect of measurement classification errors, the
agents are trained directly on the readout signal from probing the qubit. We
present proof-of-concept experiments by designing X and square root of X gates
of various durations on IBM hardware. After just 200 training iterations, our
algorithm is able to construct novel control pulses up to two times faster than
the default IBM gates, while matching their performance in terms of state
fidelity and leakage rate. As the length of our custom control pulses
increases, they begin to outperform the default gates. Improvements to the
speed and fidelity of gate operations open the way for higher circuit depth in
quantum simulation, quantum chemistry and other algorithms on near-term and
future quantum devices.Comment: 9 pages, 3 figures, submitted to QCE23: 2023 IEEE International
Conference on Quantum Computing & Engineerin
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI
Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging
techniques available for clinical applications. However, the rather slow speed
of MRI acquisitions limits the patient throughput and potential indi cations.
Compressive Sensing (CS) has proven to be an efficient technique for
accelerating MRI acquisition. The most widely used CS-MRI model, founded on the
premise of reconstructing an image from an incompletely filled k-space, leads
to an ill-posed inverse problem. In the past years, lots of efforts have been
made to efficiently optimize the CS-MRI model. Inspired by deep learning
techniques, some preliminary works have tried to incorporate deep architectures
into CS-MRI process. Unfortunately, the convergence issues (due to the
experience-based networks) and the robustness (i.e., lack real-world noise
modeling) of these deeply trained optimization methods are still missing. In
this work, we develop a new paradigm to integrate designed numerical solvers
and the data-driven architectures for CS-MRI. By introducing an optimal
condition checking mechanism, we can successfully prove the convergence of our
established deep CS-MRI optimization scheme. Furthermore, we explicitly
formulate the Rician noise distributions within our framework and obtain an
extended CS-MRI network to handle the real-world nosies in the MRI process.
Extensive experimental results verify that the proposed paradigm outperforms
the existing state-of-the-art techniques both in reconstruction accuracy and
efficiency as well as robustness to noises in real scene
The prospect of using LES and DES in engineering design, and the research required to get there
In this paper we try to look into the future to divine how large eddy and
detached eddy simulations (LES and DES, respectively) will be used in the
engineering design process about 20-30 years from now. Some key challenges
specific to the engineering design process are identified, and some of the
critical outstanding problems and promising research directions are discussed.Comment: accepted for publication in the Royal Society Philosophical
Transactions
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Gate-error analysis in simulations of quantum computers with transmon qubits
In the model of gate-based quantum computation, the qubits are controlled by
a sequence of quantum gates. In superconducting qubit systems, these gates can
be implemented by voltage pulses. The success of implementing a particular gate
can be expressed by various metrics such as the average gate fidelity, the
diamond distance, and the unitarity. We analyze these metrics of gate pulses
for a system of two superconducting transmon qubits coupled by a resonator, a
system inspired by the architecture of the IBM Quantum Experience. The metrics
are obtained by numerical solution of the time-dependent Schr\"odinger equation
of the transmon system. We find that the metrics reflect systematic errors that
are most pronounced for echoed cross-resonance gates, but that none of the
studied metrics can reliably predict the performance of a gate when used
repeatedly in a quantum algorithm
Robust multi-fidelity design of a micro re-entry unmanned space vehicle
This article addresses the preliminary robust design of a small-scale re-entry unmanned space vehicle by means of a hybrid optimization technique. The approach, developed in this article, closely couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. The evolutionary part handles the shape parameters of the vehicle and the uncertain objective functions, while the direct transcription method generates an optimal control profile for the re-entry trajectory. Uncertainties on the aerodynamic forces and characteristics of the thermal protection material are incorporated into the vehicle model, and a Monte-Carlo sampling procedure is used to compute relevant statistical characteristics of the maximum heat flux and internal temperature. Then, the hybrid algorithm searches for geometries that minimize the mean value of the maximum heat flux, the mean value of the maximum internal temperature, and the weighted sum of their variance: the evolutionary part handles the shape parameters of the vehicle and the uncertain functions, while the direct transcription method generates the optimal control profile for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are utilized to approximate the aerodynamic forces required by the optimal control solver. The artificial neural networks are trained and updated by means of a multi-fidelity approach: initially a low-fidelity analytical model, fitted on a waverider type of vehicle, is used to train the neural networks, and through the evolution a mix of analytical and computational fluid dynamic, high-fidelity computations are used to update it. The data obtained by the high-fidelity model progressively become the main source of updates for the neural networks till, near the end of the optimization process, the influence of the data obtained by the analytical model is practically nullified. On the basis of preliminary results, the adopted technique is able to predict achievable performance of the small spacecraft and the requirements in terms of thermal protection materials
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