Results 31 to 40 of about 3,302 (256)
Quantum imaginary-time control for accelerating the ground-state preparation
Physical Review Research, 2023 Quantum computers have been widely speculated to offer significant advantages in obtaining the ground state of difficult Hamiltonian in chemistry and physics.
Yu-Cheng Chen +4 more
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Quantum generative adversarial imitation learning
New Journal of Physics, 2023 Investigating quantum advantage in the NISQ era is a challenging problem whereas quantum machine learning becomes the most promising application that can be resorted to.
Tailong Xiao +4 more
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A variational quantum algorithm for approximating convex roofs
Quantum Information and Computation, 2022 Many entanglement measures are first defined for pure states of a bipartite Hilbert space, and then extended to mixed states via the convex roof extension. In this article we alter the convex roof extension of an entanglement measure, to produce a sequence of extensions that we call $f$-$d$ extensions, for $d \in \mathbb{N}$, where $f:[0,1]\to [0 ...
George Androulakis, Ryan McGaha
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Variational quantum eigensolver techniques for simulating carbon monoxide oxidation
Communications Physics, 2022 The variational quantum eigensolver is a quantum-classical algorithm used to solve optimisation problems in machine learning but demonstrates limitations when applied to simulations of large molecules.
Mariia D. Sapova, Aleksey K. Fedorov
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Training variational quantum algorithms with random gate activation
Physical Review Research, 2023 Variational quantum algorithms (VQAs) hold great potential for near-term applications and are promising to achieve quantum advantage in practical tasks. However, VQAs suffer from severe barren plateau problems and have a significant probability of being ...
Shuo Liu +3 more
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A Comparative Study on Solving Optimization Problems With Exponentially Fewer Qubits
IEEE Transactions on Quantum EngineeringVariational quantum optimization algorithms, such as the variational quantum eigensolver (VQE) or the quantum approximate optimization algorithm (QAOA), are among the most studied quantum algorithms.
David Winderl +2 more
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Variational quantum algorithms
, 2022 Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers, owing to the extremely high computational cost. Quantum computers promise a solution, although fault-tolerant quantum computers will probably not be available in the near future.
Cerezo, M +10 more
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Optimizing Variational Quantum Neural Networks Based on Collective Intelligence
MathematicsQuantum machine learning stands out as one of the most promising applications of quantum computing, widely believed to possess potential quantum advantages.
Zitong Li +4 more
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Noise-assisted variational quantum thermalization
Scientific Reports, 2022 Preparing thermal states on a quantum computer can have a variety of applications, from simulating many-body quantum systems to training machine learning models.
Jonathan Foldager +2 more
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FEBS Open Bio, EarlyView.Unique biological samples, such as site‐specific mutant proteins, are available only in limited quantities. Here, we present a polarization‐resolved transient infrared spectroscopy setup with referencing to improve signal‐to‐noise tailored towards tracing small signals. We provide an overview of characterizing the excitation conditions for polarization‐
Clark Zahn, Karsten Heyne
wiley +1 more source