Quantum

A New Framework for Quantum Reinforcement Learning

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Briefings

A new quantum reinforcement learning framework introduces conditional quantum channels for policy and environment modeling, enabling fully quantum control without measurement-induced state collapse. The paper proposes a quantum policy-gradient algorithm using a parameter-shift rule for gradient estimation, which shifts only one sampled policy block to avoid evaluating entire trajectories. This approach establishes convergence to stationary points under standard smoothness and step-size assumptions, offering a path to accelerate learning tasks with quantum resources.

Key points

• The framework models policy and environment as conditional quantum channels, eliminating the need for classical measurement-based state encoding.
• A parameter-shift rule gradient estimator shifts only one sampled policy block per iteration, avoiding full trajectory evaluation for infinite-horizon objectives.

Context

Quantum reinforcement learning (QRL) aims to control quantum systems and accelerate learning tasks using quantum resources. Existing QRL formulations often retain classical interfaces where states and actions are encoded in computational bases, which restricts genuine quantum environments by discarding entanglement information through measurements. This new framework addresses those limitations by modeling both policy and environment as conditional quantum channels.

Limitations

The study relies on theoretical derivations and assumes standard smoothness and step-size conditions for convergence. It does not provide empirical validation on specific quantum hardware, nor does it address practical constraints such as noise, decoherence, or scalability beyond the mathematical assumptions.

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