314 research outputs found
Quantum embeddings for machine learning
Quantum classifiers are trainable quantum circuits used as machine learning
models. The first part of the circuit implements a quantum feature map that
encodes classical inputs into quantum states, embedding the data in a
high-dimensional Hilbert space; the second part of the circuit executes a
quantum measurement interpreted as the output of the model. Usually, the
measurement is trained to distinguish quantum-embedded data. We propose to
instead train the first part of the circuit---the embedding---with the
objective of maximally separating data classes in Hilbert space, a strategy we
call quantum metric learning. As a result, the measurement minimizing a linear
classification loss is already known and depends on the metric used: for
embeddings separating data using the l1 or trace distance, this is the Helstrom
measurement, while for the l2 or Hilbert-Schmidt distance, it is a simple
overlap measurement. This approach provides a powerful analytic framework for
quantum machine learning and eliminates a major component in current models,
freeing up more precious resources to best leverage the capabilities of
near-term quantum information processors.Comment: 11 pages, 6 figures; tutorial available at
https://pennylane.ai/qml/app/tutorial_embeddings_metric_learning.html
[Version 2 contains minor update
Predicting Properties of Quantum Systems with Conditional Generative Models
Machine learning has emerged recently as a powerful tool for predicting
properties of quantum many-body systems. For many ground states of gapped
Hamiltonians, generative models can learn from measurements of a single quantum
state to reconstruct the state accurately enough to predict local observables.
Alternatively, classification and regression models can predict local
observables by learning from measurements on different but related states. In
this work, we combine the benefits of both approaches and propose the use of
conditional generative models to simultaneously represent a family of states,
learning shared structures of different quantum states from measurements. The
trained model enables us to predict arbitrary local properties of ground
states, even for states not included in the training data, without
necessitating further training for new observables. We first numerically
validate our approach on 2D random Heisenberg models using simulations of up to
45 qubits. Furthermore, we conduct quantum simulations on a neutral-atom
quantum computer and demonstrate that our method can accurately predict the
quantum phases of square lattices of 1313 Rydberg atoms.Comment: 10 pages, 14 figures, 5 pages appendix. Open-source code is available
at https://github.com/PennyLaneAI/generative-quantum-state
Strawberry Fields: A Software Platform for Photonic Quantum Computing
We introduce Strawberry Fields, an open-source quantum programming
architecture for light-based quantum computers, and detail its key features.
Built in Python, Strawberry Fields is a full-stack library for design,
simulation, optimization, and quantum machine learning of continuous-variable
circuits. The platform consists of three main components: (i) an API for
quantum programming based on an easy-to-use language named Blackbird; (ii) a
suite of three virtual quantum computer backends, built in NumPy and
TensorFlow, each targeting specialized uses; and (iii) an engine which can
compile Blackbird programs on various backends, including the three built-in
simulators, and -- in the near future -- photonic quantum information
processors. The library also contains examples of several paradigmatic
algorithms, including teleportation, (Gaussian) boson sampling, instantaneous
quantum polynomial, Hamiltonian simulation, and variational quantum circuit
optimization.Comment: Try the Strawberry Fields Interactive website, located at
http://strawberryfields.ai . Source code available at
https://github.com/XanaduAI/strawberryfields. Accepted in Quantu
PennyLane: Automatic differentiation of hybrid quantum-classical computations
PennyLane is a Python 3 software framework for optimization and machine
learning of quantum and hybrid quantum-classical computations. The library
provides a unified architecture for near-term quantum computing devices,
supporting both qubit and continuous-variable paradigms. PennyLane's core
feature is the ability to compute gradients of variational quantum circuits in
a way that is compatible with classical techniques such as backpropagation.
PennyLane thus extends the automatic differentiation algorithms common in
optimization and machine learning to include quantum and hybrid computations. A
plugin system makes the framework compatible with any gate-based quantum
simulator or hardware. We provide plugins for Strawberry Fields, Rigetti
Forest, Qiskit, Cirq, and ProjectQ, allowing PennyLane optimizations to be run
on publicly accessible quantum devices provided by Rigetti and IBM Q. On the
classical front, PennyLane interfaces with accelerated machine learning
libraries such as TensorFlow, PyTorch, and autograd. PennyLane can be used for
the optimization of variational quantum eigensolvers, quantum approximate
optimization, quantum machine learning models, and many other applications.Comment: Code available at https://github.com/XanaduAI/pennylane/ .
Significant contributions to the code (new features, new plugins, etc.) will
be recognized by the opportunity to be a co-author on this pape
Applications of Near-Term Photonic Quantum Computers: Software and Algorithms
Gaussian Boson Sampling (GBS) is a near-term platform for photonic quantum
computing. Recent efforts have led to the discovery of GBS algorithms with
applications to graph-based problems, point processes, and molecular vibronic
spectra in chemistry. The development of dedicated quantum software is a key
enabler in permitting users to program devices and implement algorithms. In
this work, we introduce a new applications layer for the Strawberry Fields
photonic quantum computing library. The applications layer provides users with
the necessary tools to design and implement algorithms using GBS with only a
few lines of code. This paper serves a dual role as an introduction to the
software, supported with example code, and also a review of the current state
of the art in GBS algorithms.Comment: Code available at https://github.com/XanaduAI/strawberryfields/ and
documentation available at https://strawberryfields.readthedocs.io
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