A Sub-Problem Quantum Alternating Operator Ansatz for Correlation ...
The Quantum Alternating Operator Ansatz (QAOA) is a hybrid quantum-classical variational algorithm for approximately solving combinatorial optimization problems on Noisy Intermediate-Scale Quantum …
Quantum Optimization via Gradient-Based Hamiltonian Descent
Recently, the connection between accelerated gradient methods and damped heavy-ball motion, particularly within the framework of Hamiltonian dynamics, has inspired the development of …
Proceedings of Machine Learning Research | Proceedings of the 42nd ...
QEM-Bench: Benchmarking Learning-based Quantum Error Mitigation and QEMFormer as a Multi-ranged Context Learning Baseline Tianyi Bao, Ruizhe Zhong, Xinyu Ye, Yehui Tang, Junchi Yan; …
Limitations of measure-first protocols in quantum machine learning
Specifically, we investigate quantum machine learning algorithms that, when dealing with quantum data, can either process it entirely using quantum methods or measure the input data through a fixed …
Quantum Algorithms for Finite-horizon Markov Decision Processes
Quantum Algorithms for Finite-horizon Markov Decision Processes. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research …
Accelerating Quantum Reinforcement Learning with a Quantum …
This paper introduces a Quantum Natural Policy Gradient (QNPG) algorithm, which replaces the random sampling used in classical Natural Policy Gradient (NPG) estimators with a deterministic gradient …
Quantum Speedups in Regret Analysis of Infinite Horizon Average …
Our approach involves the design of an optimism-driven tabular Reinforcement Learning algorithm that harnesses quantum signals acquired by the agent through efficient quantum mean estimation …
Reinforced Learning Explicit Circuit Representations for Quantum State ...
Characterizing quantum states is essential for advancing many quantum technologies. Recently, deep neural networks have been applied to learn quantum states by generating compressed implicit …
Reinforcement Learning for Quantum Control under Physical Constraints
Abstract Quantum control is concerned with the realisation of desired dynamics in quantum systems, serving as a linchpin for advancing quantum technologies and fundamental research.
Hybrid Quantum-Classical Multi-Agent Pathfinding
However, current quantum hardware is still in its infancy and thus limited in terms of computing power and error robustness. In this work, we present the first optimal hybrid quantum-classical MAPF …