3,454 research outputs found
A Microscopic Mechanism for Muscle's Motion
The SIRM (Stochastic Inclined Rods Model) proposed by H. Matsuura and M.
Nakano can explain the muscle's motion perfectly, but the intermolecular
potential between myosin head and G-actin is too simple and only repulsive
potential is considered. In this paper we study the SIRM with different complex
potential and discuss the effect of the spring on the system. The calculation
results show that the spring, the effective radius of the G-actin and the
intermolecular potential play key roles in the motion. The sliding speed is
about calculated from the model which well agrees with
the experimental data.Comment: 9 pages, 6 figure
Engineering the Cost Function of a Variational Quantum Algorithm for Implementation on Near-Term Devices
Variational hybrid quantum-classical algorithms are some of the most
promising workloads for near-term quantum computers without error correction.
The aim of these variational algorithms is to guide the quantum system to a
target state that minimizes a cost function, by varying certain parameters in a
quantum circuit. This paper proposes a new approach for engineering cost
functions to improve the performance of a certain class of these variational
algorithms on today's small qubit systems. We apply this approach to a
variational algorithm that generates thermofield double states of the
transverse field Ising model, which are relevant when studying phase
transitions in condensed matter systems. We discuss the benefits and drawbacks
of various cost functions, apply our new engineering approach, and show that it
yields good agreement across the full temperature range.Comment: 8 pages, 4 figure
Introducing the Quantum Research Kernels: Lessons from Classical Parallel Computing
Quantum computing represents a paradigm shift for computation requiring an
entirely new computer architecture. However, there is much that can be learned
from traditional classical computer engineering. In this paper, we describe the
Parallel Research Kernels (PRK), a tool that was very useful for designing
classical parallel computing systems. The PRK are simple kernels written to
expose bottlenecks that limit classical parallel computing performance. We
hypothesize that an analogous tool for quantum computing, Quantum Research
Kernels (QRK), may similarly aid the co-design of software and hardware for
quantum computing systems, and we give a few examples of representative QRKs.Comment: 2 page
Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning
In this paper, we present a machine learning framework to design
high-fidelity multi-qubit gates for quantum processors based on quantum dots in
silicon, with qubits encoded in the spin of single electrons. In this hardware
architecture, the control landscape is vast and complex, so we use the deep
reinforcement learning method to design optimal control pulses to achieve high
fidelity multi-qubit gates. In our learning model, a simulator models the
physical system of quantum dots and performs the time evolution of the system,
and a deep neural network serves as the function approximator to learn the
control policy. We evolve the Hamiltonian in the full state-space of the
system, and enforce realistic constraints to ensure experimental feasibility
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