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Found 34 papers, 9 papers with code
Curriculum reinforcement learning for quantum architecture search under hardware errors
In the case of parameter optimization alone, noise effects have been observed to dramatically influence the performance of the optimizer and final outcomes, which is a key line of study.
Compilation of product-formula Hamiltonian simulation via reinforcement learning
In some cases this order is fixed by the desire to minimise the error of approximation; when it is not the case, we propose that the order can be chosen to optimize compilation to a native quantum architecture.
On the expressivity of embedding quantum kernels
After proving the universality of embedding quantum kernels for both shift-invariant and composition kernels, we identify the directions towards new, more exotic, and unexplored quantum kernel families, for which it still remains open whether they correspond to efficient embedding quantum kernels.
Exponential separations between classical and quantum learners
Despite significant effort, the quantum machine learning community has only demonstrated quantum learning advantages for artificial cryptography-inspired datasets when dealing with classical data.
Enhancing variational quantum state diagonalization using reinforcement learning techniques
We demonstrate that the circuits proposed by the reinforcement learning methods are shallower than the standard variational quantum state diagonalization algorithm and thus can be used in situations where hardware capabilities limit the depth of quantum circuits.
Shadows of quantum machine learning
Quantum machine learning is often highlighted as one of the most promising uses for a quantum computer to solve practical problems.
Quantum policy gradient algorithms
Understanding the power and limitations of quantum access to data in machine learning tasks is primordial to assess the potential of quantum computing in artificial intelligence.
On establishing learning separations between classical and quantum machine learning with classical data
Despite years of effort, the quantum machine learning community has only been able to show quantum learning advantages for certain contrived cryptography-inspired datasets in the case of classical data.
Reinforcement Learning Assisted Recursive QAOA
Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems.
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets
In this domain, one of the more investigated approaches is the use of a special type of quantum circuit - a so-called quantum neural network -- to serve as a basis for a machine learning model.
Equivariant quantum circuits for learning on weighted graphs
When training a parametrized quantum circuit in this setting to solve a specific problem, the choice of ansatz is one of the most important factors that determines the trainability and performance of the algorithm.
High Dimensional Quantum Machine Learning With Small Quantum Computers
In an attempt to placate this limitation techniques can be applied for evaluating a quantum circuit using a machine with fewer qubits than the circuit naively requires.
Quantum machine learning beyond kernel methods
In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models.
Structural risk minimization for quantum linear classifiers
Firstly, using relationships to well understood classical models, we prove that two model parameters -- i. e., the dimension of the sum of the images and the Frobenius norm of the observables used by the model -- closely control the models' complexity and therefore its generalization performance.
Reinforcement learning for optimization of variational quantum circuit architectures
The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in recent times as they may lead to real-world applications of near-term quantum devices.
Parametrized quantum policies for reinforcement learning
With the advent of real-world quantum computing, the idea that parametrized quantum computations can be used as hypothesis families in a quantum-classical machine learning system is gaining increasing traction.
Certificates of quantum many-body properties assisted by machine learning
A number of standard methods are used to tackle such problems: variational approaches focus on parameterizing a subclass of solutions within the feasible set; in contrast, relaxation techniques have been proposed to approximate it from outside, thus complementing the variational approach by providing ultimate bounds to the global optimal solution.
Transfer Learning
Quantum Physics
Towards quantum advantage via topological data analysis
Our results provide a number of useful applications for full-blown, and restricted quantum computers with a guaranteed exponential speedup over classical methods, recovering some of the potential for linear-algebraic QML to become one of quantum computing's killer applications.
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
Quantum enhancements for deep reinforcement learning in large spaces
In the past decade, the field of quantum machine learning has drawn significant attention due to the prospect of bringing genuine computational advantages to now widespread algorithmic methods.
On the convergence of projective-simulation-based reinforcement learning in Markov decision processes
Specifically, we prove that one version of the projective simulation model, understood as a reinforcement learning approach, converges to optimal behavior in a large class of Markov decision processes.
Optimizing Quantum Error Correction Codes with Reinforcement Learning
Using efficient simulations with about 70 data qubits with arbitrary connectivity, we demonstrate that such a reinforcement learning agent can determine near-optimal solutions, in terms of the number of data qubits, for various error models of interest.
Computational speedups using small quantum devices
Suppose we have a small quantum computer with only M qubits.
Smooth input preparation for quantum and quantum-inspired machine learning
Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage.
Neural Network Operations and Susuki-Trotter evolution of Neural Network States
We show that this parametrization contains a set of universal quantum gates, from which it follows that the state prepared by any quantum circuit can be expressed as a Neural Network State with a number of hidden nodes that grows linearly with the number of elementary operations in the circuit.
Quantum Physics
Exponential improvements for quantum-accessible reinforcement learning
Quantum computers can offer dramatic improvements over classical devices for data analysis tasks such as prediction and classification.
Machine learning \& artificial intelligence in the quantum domain
For instance, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world.
Speeding-up the decision making of a learning agent using an ion trap quantum processor
We report a proof-of-principle experimental demonstration of the quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions.
Active learning machine learns to create new quantum experiments
We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence.
Quantum-enhanced machine learning
Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements.
Meta-learning within Projective Simulation
The extended model is examined on three different kinds of reinforcement learning tasks, in which the agent has different optimal values of the meta-parameters, and is shown to perform well, reaching near-optimal to optimal success rates in all of them, without ever needing to manually adjust any meta-parameter.
Framework for learning agents in quantum environments
In this paper we provide a broad framework for describing learning agents in general quantum environments.
Projective simulation with generalization
Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.
Faster quantum mixing for slowly evolving sequences of Markov chains
Markov chain methods are remarkably successful in computational physics, machine learning, and combinatorial optimization.
