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Found 25 papers, 5 papers with code
Challenges and Opportunities in Quantum Machine Learning
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics.
Resource frugal optimizer for quantum machine learning
In this work, we advocate for simultaneous random sampling over both the dataset as well as the measurement operators that define the loss function.
Practical Black Box Hamiltonian Learning
We study the problem of learning the parameters for the Hamiltonian of a quantum many-body system, given limited access to the system.
Dynamical simulation via quantum machine learning with provable generalization
Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated.
Out-of-distribution generalization for learning quantum dynamics
However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution.
Covariance matrix preparation for quantum principal component analysis
We also argue that PCA on quantum datasets is natural and meaningful, and we numerically implement our method for molecular ground-state datasets.
Generalization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i. e., generalizing).
Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
On the other hand, our positive results for CDR highlight the possibility of engineering error mitigation methods to improve trainability.
A semi-agnostic ansatz with variable structure for quantum machine learning
Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization.
Qubit-efficient exponential suppression of errors
Here we present an alternative method, the Resource-Efficient Quantum Error Suppression Technique (REQUEST), that adapts this breakthrough to much fewer qubits by making use of active qubit resets, a feature now available on commercial platforms.
Quantum Physics
Long-time simulations with high fidelity on quantum hardware
Specifically, we simulate an XY-model spin chain on the Rigetti and IBM quantum computers, maintaining a fidelity of at least 0. 9 for over 600 time steps.
Variational Quantum Algorithms
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost.
Effect of barren plateaus on gradient-free optimization
We numerically confirm this by training in a barren plateau with several gradient-free optimizers (Nelder-Mead, Powell, and COBYLA algorithms), and show that the numbers of shots required in the optimization grows exponentially with the number of qubits.
Noise-Induced Barren Plateaus in Variational Quantum Algorithms
Specifically, for the local Pauli noise considered, we prove that the gradient vanishes exponentially in the number of qubits $n$ if the depth of the ansatz grows linearly with $n$.
Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
With the recent rise of quantum machine learning, it is natural to ask whether there is a quantum analog of the NFL theorem, which would restrict a quantum computer's ability to learn a unitary process (the quantum analog of a function) with quantum training data.
State preparation and measurement in a quantum simulation of the O(3) sigma model
Recently, Singh and Chandrasekharan showed that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site.
Quantum Physics High Energy Physics - Lattice
Trainability of Dissipative Perceptron-Based Quantum Neural Networks
Several architectures have been proposed for quantum neural networks (QNNs), with the goal of efficiently performing machine learning tasks on quantum data.
Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
Variational quantum algorithms (VQAs) optimize the parameters $\vec{\theta}$ of a parametrized quantum circuit $V(\vec{\theta})$ to minimize a cost function $C$.
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems
Proposed recently, the Quantum Approximate Optimization Algorithm (QAOA) is considered as one of the leading candidates for demonstrating quantum advantage in the near term.
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
The Quantum Approximate Optimization Algorithm (QAOA) is arguably one of the leading quantum algorithms that can outperform classical state-of-the-art methods in the near term.
Variational Fast Forwarding for Quantum Simulation Beyond the Coherence Time
Finally, we implement VFF on Rigetti's quantum computer to show simulation beyond the coherence time.
Quantum Physics
Variational Quantum Linear Solver
Specifically, we prove that $C \geq \epsilon^2 / \kappa^2$, where $C$ is the VQLS cost function and $\kappa$ is the condition number of $A$.
Quantum Physics
Variational Quantum State Diagonalization
In these algorithms, a quantum computer evaluates the cost of a gate sequence (with speedup over classical cost evaluation), and a classical computer uses this information to adjust the parameters of the gate sequence.
Quantum Physics
Quantum assisted quantum compiling
Our other circuit gives ${\rm Tr}(V^\dagger U)$ and is a generalization of the power-of-one-qubit circuit that we call the power-of-two-qubits.
Quantum Physics
Learning the quantum algorithm for state overlap
Furthermore, we apply our approach to the hardware-specific connectivity and gate alphabets used by Rigetti's and IBM's quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error - compared to the Swap Test - on these computers.
Quantum Physics
