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Found 11 papers, 2 papers with code
Blind quantum machine learning with quantum bipartite correlator
Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes.
Quantum Deep Hedging
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.
Numerical evidence against advantage with quantum fidelity kernels on classical data
We show that the general-purpose hyperparameter tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well-approximated by a classical kernel, removing the possibility of quantum advantage.
Bandwidth Enables Generalization in Quantum Kernel Models
Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings.
Importance of Kernel Bandwidth in Quantum Machine Learning
Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems.
Exploiting Symmetry Reduces the Cost of Training QAOA
We show how by considering only the terms that are not connected by symmetry, we can significantly reduce the cost of evaluating the QAOA energy.
Quantum Physics
Classical symmetries and the Quantum Approximate Optimization Algorithm
Our approach formalizes the connection between quantum symmetry properties of the QAOA dynamics and the group of classical symmetries of the objective function.
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.
Evaluating Quantum Approximate Optimization Algorithm: A Case Study
We perform a large-scale numerical study of the approximation ratios attainable by QAOA is the low- to medium-depth regime.
Quantum Physics Data Structures and Algorithms
Hypergraph Partitioning With Embeddings
As a result, hypergraph partitioning is an NP-Hard problem to both solve or approximate.
