REVIEW 6 cited by
Quantum neural network
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
It is suggested that a quantum neural network (QNN), a type of artificial neural network, can be built using the principles of quantum information processing. The input and output qubits in the QNN can be implemented by optical modes with different polarization, the weights of the QNN can be implemented by optical beam splitters and phase shifters
Forward citations
Cited by 6 Pith papers
-
Selective Feature Re-Encoded Quantum Convolutional Neural Network with Joint Optimization for Image Classification
Selective re-encoding of top PCA features inside a QCNN, plus joint optimization of a PCA-based and an autoencoder-based QCNN, improves binary image classification accuracy on MNIST and Fashion-MNIST over the paper's ...
-
Scaling Quantum Algorithms via Dissipation: Avoiding Barren Plateaus
Dissipative quantum circuits with periodic qubit resets provably avoid both unitary and noise-induced barren plateaus for gates near the final measurement.
-
Parameter-Shift Rules for Gradients in Boson Sampling Experiments
Lossy Fock boson sampling probabilities are finite Fourier series in each phase, so their exact gradients can be recovered from shifted photon-count measurements; Gaussian boson sampling under general loss admits no s...
-
Circuit structure-preserving error mitigation for High-Fidelity Quantum Simulations
A structure-preserving error mitigation technique that inverts a noise matrix measured from an identity-equivalent circuit is demonstrated on variational simulations of a non-Hermitian Ising chain, showing improved ag...
-
Efficient Quantum Approximate $k$NN Algorithm via Granular-Ball Computing
Granular-ball compression plus quantum swap-test similarity checks is claimed to give a kNN search time logarithmic in the number of granular balls.
-
Research progress on quantum neural networks and quantum machine learning
Survey summarizing performance metrics of fully connected QNNs, quantum CNNs, equivariant QNNs, quantum Hopfield networks, quantum Boltzmann machines, quantum reservoir computing, and composite networks for reinforcem...
Discussion (0). Continue with ORCID to comment.