bsiegelwax
Quantum algorithm designer and author of "Dungeons & Qubits" and "Choose Your Own Quantum Adventure." Writes at https://medium.com/@bsiegelwax.
Pinned Repositories
784-Dimensional-Quantum-MNIST
Quantum MNIST using amplitude encoding instead of dimensionality reduction.
Maximum-Quantum-Classification
This was an attempt to push the limits of the IBM cloud quantum computing simulator.
no-ancilla-MCX
A no-ancilla MCX submission for the 2022 Classiq Coding Competition.
Quantum-Classification
Quantum Machine Learning (QML) does not require quantum neural networks. SWAP Tests can be used to easily perform classification tasks.
Quantum-Classification-of-Amplitudes
This is non-optimized code intended solely to test whether or not quantum classification works with amplitude encoding.
Quantum-Clustering-and-Classification
Quantum clustering and classification in one circuit, written exclusively in OpenQASM.
Quantum-Imperfect-Cloning
NOT cloning quantum states, but trying to get as close as possible.
Quantum-Inspired-MNIST
Inspired by quantum classification, this is MNIST with no models, no weights, no activation functions, no optimizers, nor anything else that resembles traditional MNIST implementations.
Quantum-MNIST
MNIST classification of handwritten digits on a quantum computing simulator using OpenQASM.
Standard-Deviation-MNIST
"Quantum-Inspired MNIST" achieved 72% accuracy using nothing but means, addition, and subtraction. This experiment adds standard deviations.
bsiegelwax's Repositories
bsiegelwax/784-Dimensional-Quantum-MNIST
Quantum MNIST using amplitude encoding instead of dimensionality reduction.
bsiegelwax/Quantum-MNIST
MNIST classification of handwritten digits on a quantum computing simulator using OpenQASM.
bsiegelwax/Maximum-Quantum-Classification
This was an attempt to push the limits of the IBM cloud quantum computing simulator.
bsiegelwax/Quantum-Classification
Quantum Machine Learning (QML) does not require quantum neural networks. SWAP Tests can be used to easily perform classification tasks.
bsiegelwax/Quantum-Imperfect-Cloning
NOT cloning quantum states, but trying to get as close as possible.
bsiegelwax/no-ancilla-MCX
A no-ancilla MCX submission for the 2022 Classiq Coding Competition.
bsiegelwax/Quantum-Classification-of-Amplitudes
This is non-optimized code intended solely to test whether or not quantum classification works with amplitude encoding.
bsiegelwax/Quantum-Clustering-and-Classification
Quantum clustering and classification in one circuit, written exclusively in OpenQASM.
bsiegelwax/Quantum-Inspired-MNIST
Inspired by quantum classification, this is MNIST with no models, no weights, no activation functions, no optimizers, nor anything else that resembles traditional MNIST implementations.
bsiegelwax/Standard-Deviation-MNIST
"Quantum-Inspired MNIST" achieved 72% accuracy using nothing but means, addition, and subtraction. This experiment adds standard deviations.