Variational Quantum Policy Gradients with an Application to Quantum
Control
- URL: http://arxiv.org/abs/2203.10591v1
- Date: Sun, 20 Mar 2022 16:14:49 GMT
- Title: Variational Quantum Policy Gradients with an Application to Quantum
Control
- Authors: Andr\'e Sequeira, Luis Paulo Santos, Lu\'is Soares Barbosa
- Abstract summary: Quantum Machine Learning models are composed by Variational Quantum Circuits (VQCs) in a very natural way.
In this work, we consider Policy Gradients using a hardware-efficient ansatz.
We prove that the complexity of obtaining an epsilon-approximation of the gradient using quantum hardware scales only logarithmically with the number of parameters.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum Machine Learning models are composed by Variational Quantum Circuits
(VQCs) in a very natural way. There are already some empirical results proving
that such models provide an advantage in supervised/unsupervised learning
tasks. However, when applied to Reinforcement Learning (RL), less is known. In
this work, we consider Policy Gradients using a hardware-efficient ansatz. We
prove that the complexity of obtaining an {\epsilon}-approximation of the
gradient using quantum hardware scales only logarithmically with the number of
parameters, considering the number of quantum circuits executions. We test the
performance of such models in benchmarking environments and verify empirically
that such quantum models outperform typical classical neural networks used in
those environments, using a fraction of the number of parameters. Moreover, we
propose the utilization of the Fisher Information spectrum to show that the
quantum model is less prone to barren plateaus than its classical counterpart.
As a different use case, we consider the application of such variational
quantum models to the problem of quantum control and show its feasibility in
the quantum-quantum domain.
Related papers
- Quantum Equilibrium Propagation for efficient training of quantum systems based on Onsager reciprocity [0.0]
Equilibrium propagation (EP) is a procedure that has been introduced and applied to classical energy-based models which relax to an equilibrium.
Here, we show a direct connection between EP and Onsager reciprocity and exploit this to derive a quantum version of EP.
This can be used to optimize loss functions that depend on the expectation values of observables of an arbitrary quantum system.
arXiv Detail & Related papers (2024-06-10T17:22:09Z) - Ansatz-Agnostic Exponential Resource Saving in Variational Quantum
Algorithms Using Shallow Shadows [5.618657159109373]
Variational Quantum Algorithms (VQA) have been identified as a promising candidate for the demonstration of near-term quantum advantage.
We present a protocol based on shallow shadows that achieves similar levels of savings for almost any shallow ansatz studied in the literature.
We show that two important applications in quantum information for which VQAs can be a powerful option, namely variational quantum state preparation and variational quantum circuit synthesis.
arXiv Detail & Related papers (2023-09-09T11:00:39Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for
Deep Quantum Machine Learning [52.77024349608834]
Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing.
Key issue is how to address the inherent non-linearity of classical deep learning.
We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
arXiv Detail & Related papers (2022-12-22T16:06:24Z) - Quantum variational learning for entanglement witnessing [0.0]
This work focuses on the potential implementation of quantum algorithms allowing to properly classify quantum states defined over a single register of $n$ qubits.
We exploit the notion of "entanglement witness", i.e., an operator whose expectation values allow to identify certain specific states as entangled.
We made use of Quantum Neural Networks (QNNs) in order to successfully learn how to reproduce the action of an entanglement witness.
arXiv Detail & Related papers (2022-05-20T20:14:28Z) - Quantum Neuron with Separable-State Encoding [0.0]
It is not yet possible to test advanced quantum neuron models on a large scale in currently available quantum processors.
We propose a quantum perceptron (QP) model that uses a reduced number of multi-qubit gates.
We demonstrate the performance of the proposed model by implementing a few qubits version of the QP in a simulated quantum computer.
arXiv Detail & Related papers (2022-02-16T19:26:23Z) - Generalization Metrics for Practical Quantum Advantage in Generative
Models [68.8204255655161]
Generative modeling is a widely accepted natural use case for quantum computers.
We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance.
Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
arXiv Detail & Related papers (2022-01-21T16:35:35Z) - Model-Independent Error Mitigation in Parametric Quantum Circuits and
Depolarizing Projection of Quantum Noise [1.5162649964542718]
Finding ground states and low-lying excitations of a given Hamiltonian is one of the most important problems in many fields of physics.
quantum computing on Noisy Intermediate-Scale Quantum (NISQ) devices offers the prospect to efficiently perform such computations.
Current quantum devices still suffer from inherent quantum noise.
arXiv Detail & Related papers (2021-11-30T16:08:01Z) - Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems.
Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
arXiv Detail & Related papers (2021-08-30T23:50:05Z) - Quantum algorithms for quantum dynamics: A performance study on the
spin-boson model [68.8204255655161]
Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator.
variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware.
We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
arXiv Detail & Related papers (2021-08-09T18:00:05Z) - Preparing random states and benchmarking with many-body quantum chaos [48.044162981804526]
We show how to predict and experimentally observe the emergence of random state ensembles naturally under time-independent Hamiltonian dynamics.
The observed random ensembles emerge from projective measurements and are intimately linked to universal correlations built up between subsystems of a larger quantum system.
Our work has implications for understanding randomness in quantum dynamics, and enables applications of this concept in a wider context.
arXiv Detail & Related papers (2021-03-05T08:32:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.