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Found 13 papers, 3 papers with code
Machine Learning for maximizing the memristivity of single and coupled quantum memristors
We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors.
Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation
We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum circuits comprised of systems with $N_{Q}$ qubits.
An approach to interfacing the brain with quantum computers: practical steps and caveats
At the time of writing, available quantum computing hardware and brain activity sensing technology are not sufficiently developed for real-time control of quantum states with the brain.
One-photon Solutions to Multiqubit Multimode quantum Rabi model
General solutions to the quantum Rabi model involve subspaces with unbounded number of photons.
Quantum Physics Optics
Efficient Hamiltonian Simulation for Solving Option Price Dynamics
Here, we present a digital quantum algorithm to solve Black-Scholes equation on a quantum computer by mapping it to the Schr\"odinger equation.
Quantum Physics General Finance Pricing of Securities
Spreading the word -- current status of VO tutorials and schools
In addition to the VO schools on the European level, different national teams have also put effort into VO dissemination.
Instrumentation and Methods for Astrophysics
Quantized Three-Ion-Channel Neuron Model for Neural Action Potentials
We study the behavior of both ion channel conductivities and the circuit voltage, and we compare the results with those of the single channel, for a given quantum state of the source.
Towards Pricing Financial Derivatives with an IBM Quantum Computer
Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce the time-evolution of interest rates.
Quantum Physics Mesoscale and Nanoscale Physics
Quantized Single-Ion-Channel Hodgkin-Huxley Model for Quantum Neurons
This reduced version is essentially a classical memristor, a resistor whose resistance depends on the history of electric signals that have crossed it, coupled to a voltage source and a capacitor.
Experimental Implementation of a Quantum Autoencoder via Quantum Adders
Quantum autoencoders allow for reducing the amount of resources in a quantum computation by mapping the original Hilbert space onto a reduced space with the relevant information.
Perceptrons from Memristors
Memristors, resistors with memory whose outputs depend on the history of their inputs, have been used with success in neuromorphic architectures, particularly as synapses and non-volatile memories.
A Quantum Algorithm for Solving Linear Differential Equations: Theory and Experiment
We present and experimentally realize a quantum algorithm for efficiently solving the following problem: given an $N\times N$ matrix $\mathcal{M}$, an $N$-dimensional vector $\textbf{\emph{b}}$, and an initial vector $\textbf{\emph{x}}(0)$, obtain a target vector $\textbf{\emph{x}}(t)$ as a function of time $t$ according to the constraint $d\textbf{\emph{x}}(t)/dt=\mathcal{M}\textbf{\emph{x}}(t)+\textbf{\emph{b}}$.
Quantum Physics
Supervised Quantum Learning without Measurements
We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations.
