56 citations · 136 across the 8 of their papers we have counts for
10 papers
Spectral Born machines: classically trainable quantum generative models for discrete data
Austin Huang, William Maxwell, Vasilis Belis +4
We present \emph{spectral Born machines}, a class of quantum generative models that results from viewing and generalizing the class of IQP Born machines through the lens of group F…
Spectral methods: crucial for machine learning, natural for quantum computers?
Vasilis Belis, Joseph Bowles, Rishabh Gupta +2
This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularis…
Generalization despite overfitting in quantum machine learning models
Evan Peters, Maria Schuld
The widespread success of deep neural networks has revealed a surprise in classical machine learning: very complex models often generalize well while simultaneously overfitting tra…
Bandwidth Enables Generalization in Quantum Kernel Models
Abdulkadir Canatar, Evan Peters, Cengiz Pehlevan +2
Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings. For example, quantum kernel methods have been…
Qubit assignment using time reversal
Evan Peters, Prasanth Shyamsundar, Andy C. Y. Li +1
As the number of qubits available on noisy quantum computers grows, it will become necessary to efficiently select a subset of physical qubits to use in a quantum computation. For…
Simulations of Quantum Circuits with Approximate Noise using qsim and Cirq
Sergei V. Isakov, Dvir Kafri, Orion Martin +15
We introduce multinode quantum trajectory simulations with qsim, an open source high performance simulator of quantum circuits. qsim can be used as a backend of Cirq, a Python soft…
Perturbative readout error mitigation for near term quantum computers
Evan Peters, Andy C. Y. Li, Gabriel N. Perdue
Readout errors on near-term quantum computers can introduce significant error to the empirical probability distribution sampled from the output of a quantum circuit. These errors c…
Machine learning of high dimensional data on a noisy quantum processor
Evan Peters, João Caldeira, Alan Ho +6
We present a quantum kernel method for high-dimensional data analysis using Google's universal quantum processor, Sycamore. This method is successfully applied to the cosmological…
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
Michael Broughton, Guillaume Verdon, Trevor McCourt +26
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high…
Measurement of the muon anti-neutrino double-differential cross section for quasi-elastic scattering on hydrocarbon at~ GeV
C. E. Patrick, L. Aliaga, A. Bashyal +64
We present double-differential measurements of anti-neutrino quasi-elastic scattering in the MINERvA detector. This study improves on a previous single differential measurement by…