11,155 research outputs found
Multilevel leapfrogging initialization for quantum approximate optimization algorithm
The quantum approximate optimization algorithm (QAOA) is a prospective hybrid
quantum-classical algorithm widely used to solve combinatorial optimization
problems. However, the external parameter optimization required in QAOA tends
to consume extensive resources to find the optimal parameters of the
parameterized quantum circuit, which may be the bottleneck of QAOA. To meet
this challenge, we first propose multilevel leapfrogging learning (M-Leap) that
can be extended to quantum reinforcement learning, quantum circuit design, and
other domains. M-Leap incrementally increases the circuit depth during
optimization and predicts the initial parameters at level () based
on the optimized parameters at level , cutting down the optimization rounds.
Then, we propose a multilevel leapfrogging-interpolation strategy (MLI) for
initializing optimizations by combining M-Leap with the interpolation
technique. We benchmark its performance on the Maxcut problem. Compared with
the Interpolation-based strategy (INTERP), MLI cuts down at least half the
number of rounds of optimization for the classical outer learning loop.
Remarkably, the simulation results demonstrate that the running time of MLI is
1/3 of INTERP when MLI gets quasi-optimal solutions. In addition, we present
the greedy-MLI strategy by introducing multi-start, which is an extension of
MLI. The simulation results show that greedy-MLI can get a higher average
performance than the remaining two methods. With their efficiency to find the
quasi-optima in a fraction of costs, our methods may shed light in other
quantum algorithms
Magnetic Control of Acoustic Resonators and Metamaterials
The magneto-elastic coupling between spin and acoustic excitations offers an excellent opportunity to combine, within the same signal processing devices, the magnetic tuneability and re-programmability inherent to magnonics with the energy efficiency of phononics. Relevant recent studies have focused on characterisation of the interaction strength in magnetoacoustic devices and on the excitation and detection of an acoustically induced magnetic signal. The work presented in this thesis focuses on the magnetic control of propagating acoustic waves, with the aim to reveal and to characterise the signatures of the magneto-elastic coupling in reflection and transmission of acoustic waves in magnetoacoustic metamaterials, and to explore their tuning using magnetic stimuli
System Identification on the Families of Auto-Regressive with Least-Square-Batch Algorithm
The theories of system identification have been highly elaborated so as to
achieve the true system. This paper much discuses regarding the stochastic
processes along with the divergent of whether or not the system has zero-mean
under scenario of either white and coloured noise. The mathematical
foundations, including mean, variance, covariance, optimal parameters, along
with some modified scenarios among them, are presented in detail from various
system along with some basic idea behind them. The families of auto-regressive
(1) and (2) are compared both mathematical and simulation in order to obtain
the best design approaching the true system, Moreover, the least-square
algorithm is used to examine the effectiveness of some number of iteration
along with "batch" theoremComment: 8 pages; 10 figure
Pipelined Architecture for Soft-decision Iterative Projection Aggregation Decoding for RM Codes
The recently proposed recursive projection-aggregation (RPA) decoding
algorithm for Reed-Muller codes has received significant attention as it
provides near-ML decoding performance at reasonable complexity for short codes.
However, its complicated structure makes it unsuitable for hardware
implementation. Iterative projection-aggregation (IPA) decoding is a modified
version of RPA decoding that simplifies the hardware implementation. In this
work, we present a flexible hardware architecture for the IPA decoder that can
be configured from fully-sequential to fully-parallel, thus making it suitable
for a wide range of applications with different constraints and resource
budgets. Our simulation and implementation results show that the IPA decoder
has 41% lower area consumption, 44% lower latency, four times higher
throughput, but currently seven times higher power consumption for a code with
block length of 128 and information length of 29 compared to a state-of-the-art
polar successive cancellation list (SCL) decoder with comparable decoding
performance
Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction
Multi-modal regression is important in forecasting nonstationary processes or
with a complex mixture of distributions. It can be tackled with multiple
hypotheses frameworks but with the difficulty of combining them efficiently in
a learning model. A Structured Radial Basis Function Network is presented as an
ensemble of multiple hypotheses predictors for regression problems. The
predictors are regression models of any type that can form centroidal Voronoi
tessellations which are a function of their losses during training. It is
proved that this structured model can efficiently interpolate this tessellation
and approximate the multiple hypotheses target distribution and is equivalent
to interpolating the meta-loss of the predictors, the loss being a zero set of
the interpolation error. This model has a fixed-point iteration algorithm
between the predictors and the centers of the basis functions. Diversity in
learning can be controlled parametrically by truncating the tessellation
formation with the losses of individual predictors. A closed-form solution with
least-squares is presented, which to the authors knowledge, is the fastest
solution in the literature for multiple hypotheses and structured predictions.
Superior generalization performance and computational efficiency is achieved
using only two-layer neural networks as predictors controlling diversity as a
key component of success. A gradient-descent approach is introduced which is
loss-agnostic regarding the predictors. The expected value for the loss of the
structured model with Gaussian basis functions is computed, finding that
correlation between predictors is not an appropriate tool for diversification.
The experiments show outperformance with respect to the top competitors in the
literature.Comment: 63 Pages, 40 Figure
2023-2024 Boise State University Undergraduate Catalog
This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State
Molecular dynamics simulations of nanoclusters in neuromorphic systems
Neuromorphic computing is a new computing paradigm that deals with computing tasks using inter-connected artificial neurons inspired by the natural neurons in the human brain. This computational architecture is more efficient in performing many complex tasks such a pattern recognition and has promise at overcoming some of the limitations of conventional computers. Among the emerging types of artificial neurons, a cluster-based neuromorphic device is a promising system with good costefficiency because of a simple fabrication process. This device functions using the formation and breakage of the connections (“synapses”) between clusters, driven by the bias voltage applied to the clusters. The mechanisms of the formation and breakage of these connections are thus of the utmost interest. In this thesis, the molecular dynamics simulation method is used to explore the mechanisms of the formation and breakage of the connections (“filaments”) between the clusters in a model of neuromorphic device. First, the Joule heating mechanism of filament breakage is explored using a model consisting of Au nanowire that connects two Au1415 clusters. Upon heating, the atoms of the nanofilament gradually aggregate towards the clusters, causing the middle of the wire to graduallythin and then suddenly break. Most of the system remains crystalline during this process, but the centre becomes molten. The terminal clusters increase the melting point of the nanowires by fixing them and act as recrystallisation regions. A strong dependence of the breaking temperature is found not only on the width of the nanowires but also their length and atomic structure. Secondly, the bridge formation and thermal breaking processes between Au1415 clusters on a graphite substrate are also simulated. The bridging process , which can heal a broken filament, is driven by diffusion of gold along the graphite substrate. The characteristic times of bridge formation are explored at elevated simulation temperatures to estimate the longer characteristic times of formation at room-temperature conditions. The width of the bridge formed has a power-law dependence on the simulation time, and the mechanism is a combination of diffusion and viscous flow. Simulations of bridgebreaking are also conducted and reveal the existence of a voltage threshold that must be reached to activate the breakage. The role of the substrate in the bridge formation and breakage processes is revealed as a medium of diffusion. Lastly, to explore future potential cluster materials, the thermal behaviour of Pb-Al core-shell clusters is studied. The core and shell are found to melt separately. In fact, the core atoms of nanoclusters tend to escape their shells and partially cover them, leading to a preference for a segregated state. The melting point of the core can either be depressed or elevated, depending on the thickness of the shell due to different mechanisms
Tutorial: Nonlinear magnonics
Nonlinear magnonics studies the nonlinear interaction between magnons and
other physical platforms (phonon, photon, qubit, spin texture) to generate
novel magnon states for information processing. In this tutorial, we first
introduce the nonlinear interactions of magnons in pure magnetic systems and
hybrid magnon-phonon and magnon-photon systems. Then we show how these
nonlinear interactions can generate exotic magnonic phenomena. In the classical
regime, we will cover the parametric excitation of magnons, bistability and
multistability, and the magnonic frequency comb. In the quantum regime, we will
discuss the single magnon state, Schr\"{o}dinger cat state and the entanglement
and quantum steering among magnons, photons and phonons. The applications of
the hybrid magnonics systems in quantum transducer and sensing will also be
presented. Finally, we outlook the future development direction of nonlinear
magnonics.Comment: 50 pages, 26 figure
SU(2) Symmetry of Coherent Photons and Application to Poincar\'e Rotator
Lie algebra is a hidden mathematical structure behind various quantum systems
realised in nature. Here, we consider wavefunctions for polarisation
states of coherent photons emitted from a laser source, and discuss the
relationship to spin expectation values with SO(3) symmetry based on
isomorphism theorems. In particular, we found rotated half-wave-plates
correspond to mirror reflections in the Poincar\'e sphere, which do not form a
subgroup in the projected O(2) plane due to anti-hermitian property. This could
be overcome experimentally by preparing another half-wave-plate to realise a
pristine rotator in , which allows arbitrary rotation angles determined
by the physical rotation. By combining another 2 quarter-wave-plates, we could
also construct a genuine phase-shifter, thus, realising passive control over
the full Poincar\'e sphere
Algorithmic Security is Insufficient: A Comprehensive Survey on Implementation Attacks Haunting Post-Quantum Security
This survey is on forward-looking, emerging security concerns in post-quantum
era, i.e., the implementation attacks for 2022 winners of NIST post-quantum
cryptography (PQC) competition and thus the visions, insights, and discussions
can be used as a step forward towards scrutinizing the new standards for
applications ranging from Metaverse, Web 3.0 to deeply-embedded systems. The
rapid advances in quantum computing have brought immense opportunities for
scientific discovery and technological progress; however, it poses a major risk
to today's security since advanced quantum computers are believed to break all
traditional public-key cryptographic algorithms. This has led to active
research on PQC algorithms that are believed to be secure against classical and
powerful quantum computers. However, algorithmic security is unfortunately
insufficient, and many cryptographic algorithms are vulnerable to side-channel
attacks (SCA), where an attacker passively or actively gets side-channel data
to compromise the security properties that are assumed to be safe
theoretically. In this survey, we explore such imminent threats and their
countermeasures with respect to PQC. We provide the respective, latest
advancements in PQC research, as well as assessments and providing visions on
the different types of SCAs
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