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Found 7 papers, 2 papers with code
Inference-Based Quantum Sensing
We show that, for a general class of unitary families of encoding, $\mathcal{R}(\theta)$ can be fully characterized by only measuring the system response at $2n+1$ parameters.
Dynamical simulation via quantum machine learning with provable generalization
Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated.
Out-of-distribution generalization for learning quantum dynamics
However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution.
Binary Operations on Neuromorphic Hardware with Application to Linear Algebraic Operations and Stochastic Equations
We use the mechanisms to construct a neuromorphic, binary matrix multiplication algorithm that may be used as a primitive for linear differential equation integration, deep networks, and other standard calculations.
Absence of Barren Plateaus in Quantum Convolutional Neural Networks
To derive our results we introduce a novel graph-based method to analyze expectation values over Haar-distributed unitaries, which will likely be useful in other contexts.
Quantum assisted quantum compiling
Our other circuit gives ${\rm Tr}(V^\dagger U)$ and is a generalization of the power-of-one-qubit circuit that we call the power-of-two-qubits.
Quantum Physics
Learning the quantum algorithm for state overlap
Furthermore, we apply our approach to the hardware-specific connectivity and gate alphabets used by Rigetti's and IBM's quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error - compared to the Swap Test - on these computers.
Quantum Physics
