arXiv:2406.07884v3 Announce Type: replace-cross Abstract: Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantum interactive dynamics with the potential to address outstanding challenges in quantum state preparation and compression, quantum control, and quantum complexity. We present a deep reinforcement learning (RL) approach using an actor-critic algorithm for constructing short disentangling circuits for states with up to 16 qubits. With access to only two-qubit reduced density matrices, our agent decid
This research is emerging now as advancements in reinforcement learning meet the growing practical challenges of quantum state control and error correction in multi-qubit systems.
This development is crucial for accelerating quantum computing progress by making multiqubit systems more manageable, potentially enabling more robust quantum algorithms and hardware.
The ability to efficiently disentangle multiqubit states with partial observation could significantly lower the barrier for quantum state preparation and error correction, enabling larger and more stable quantum computers.
- · Quantum computing hardware developers
- · Quantum algorithm researchers
- · Labs focused on quantum control
- · AI researchers in reinforcement learning
- · Classical computing architectures for specific tasks
More efficient and scalable quantum computer development due to improved state control and error correction capabilities.
Faster development and deployment of quantum algorithms for complex problems, particularly in materials science, drug discovery, and cryptography.
Potential for quantum supremacy in practical applications sooner than anticipated, fundamentally altering computational landscapes and scientific discovery processes.
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Read at arXiv cs.LG