90,574 research outputs found
Quantum-Assisted Simulation: A Framework for Designing Machine Learning Models in the Quantum Computing Domain
Machine learning (ML) models are trained using historical data to classify
new, unseen data. However, traditional computing resources often struggle to
handle the immense amount of data, commonly known as Big Data, within a
reasonable timeframe. Quantum computing (QC) provides a novel approach to
information processing. Quantum algorithms have the potential to process
classical data exponentially faster than classical computing. By mapping
quantum machine learning (QML) algorithms into the quantum mechanical domain,
we can potentially achieve exponential improvements in data processing speed,
reduced resource requirements, and enhanced accuracy and efficiency. In this
article, we delve into both the QC and ML fields, exploring the interplay of
ideas between them, as well as the current capabilities and limitations of
hardware. We investigate the history of quantum computing, examine existing QML
algorithms, and aim to present a simplified procedure for setting up
simulations of QML algorithms, making it accessible and understandable for
readers. Furthermore, we conducted simulations on a dataset using both machine
learning and quantum machine learning approaches. We then proceeded to compare
their respective performances by utilizing a quantum simulator
Atom Optics with Microfabricated Optical Elements
We introduce a new direction in the field of atom optics, atom interferometry, and neutral-atom quantum information processing. It is based on the use of microfabricated optical elements. With these elements versatile and integrated atom optical devices can be created in a compact fashion. This approach opens the possibility to scale, parallelize, and miniaturize atom optics for new investigations in fundamental research and application. It will lead to new, compact sources of ultracold atoms, compact sensors based on matter wave interference and new approaches towards quantum computing with neutral atoms. The exploitation of the unique features of the quantum mechanical behavior of matter waves and the capabilities of powerful state-of-the-art micro- and nanofabrication techniques lend this approach a special attraction
Realization of quantum algorithms with qudits
The paradigm behind digital quantum computing inherits the idea of using
binary information processing. The nature in fact gives much more rich
structures of physical objects that can be used for encoding information, which
is especially interesting in the quantum mechanical domain. In this Colloquium,
we review several ideas indicating how multilevel quantum systems, also known
as qudits, can be used for efficient realization of quantum algorithms, which
are represented via standard qubit circuits. We focus on techniques of
leveraging qudits for simplifying decomposition of multiqubit gates, and for
compressing quantum information by encoding multiple qubits in a single qudit.
As we discuss, these approaches can be efficiently combined. This allows
reducing in the number of entangling (two-body) operations and the number of
the used quantum information carriers compared to straightforward qubit
realizations. These theoretical schemes can be implemented with quantum
computing platforms of various nature, such as trapped ions, neutral atoms,
superconducting junctions, and quantum light. We conclude with summarizing a
set of open problems, whose resolving is an important further step towards
employing universal qudit-based processors for running qubit algorithms.Comment: 24 pages, 19 figure
Quantum Computation of Hydrogen Bond Dynamics and Vibrational Spectra
Calculating the observable properties of chemical systems is often
classically intractable, and is widely viewed as a promising application of
quantum information processing. This is because a full description of chemical
behavior relies upon the complex interplay of quantum-mechanical electrons and
nuclei, demanding an exponential scaling of computational resources with system
size. While considerable progress has been made in mapping electronic-structure
calculations to quantum hardware, these approaches are unsuitable for
describing the quantum dynamics of nuclei, proton- and hydrogen-transfer
processes, or the vibrational spectra of molecules. Here, we use the QSCOUT
ion-trap quantum computer to determine the quantum dynamics and vibrational
properties of a shared proton within a short-strong hydrogen-bonded system. For
a range of initial states, we experimentally drive the ion-trap system to
emulate the quantum trajectory of the shared proton wavepacket as it evolves
along the potential surface generated by the nuclear frameworks and electronic
structure. We then extract the characteristic vibrational frequencies for the
shared proton motion to spectroscopic accuracy and determine all energy
eigenvalues of the system Hamiltonian to > 99.9% fidelity. Our approach offers
a new paradigm for studying the quantum chemical dynamics and vibrational
spectra of molecules, and when combined with quantum algorithms for electronic
structure, opens the possibility to describe the complete behavior of molecules
using exclusively quantum computation techniques.Comment: 10 pages, 4 figure
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