mlsanityData to 14 Oct 2026

Quantum state-agnostic work extraction (almost) without dissipation

arXiv 2505.09456 ↗First posted 14 May 2025quant-ph cs.AI cs.LGcited by 2

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Abstract (arXiv)

We investigate work extraction protocols designed to transfer the maximum possible energy to a battery using sequential access to N copies of an unknown pure qubit state. The core challenge is designing interactions to optimally balance two competing goals: charging of the battery optimally using the qubit in hand, and acquiring more information by qubit to improve energy harvesting in subsequent rounds. Here, we leverage exploration-exploitation trade-off in reinforcement learning to develop adaptive strategies achieving energy dissipation that scales only poly-logarithmically in N. This represents an exponential improvement over current protocols based on full state tomography.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 29 references that are in the map. First posted on arXiv 14 May 2025.

Builds on

4 of 29 references.

References · 4

  1. 2022When Does Adaptivity Help for Quantum State Learning?cited by 12
  2. 2024Linear bandits with polylogarithmic minimax regretcited by 8
  3. 2023Quantum chi-squared tomography and mutual information testingcited by 7
  4. 2024Learning pure quantum states (almost) without regretcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Reinforcement learning for quantum processes with memorycited by 1
  2. 2026Learning Pure Quantum States in Any Dimension (Almost) Without Regretcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries
  2. 2026Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis
  3. 2025Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
  4. 2024Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning
  5. 2024Dynamic Inhomogeneous Quantum Resource Scheduling with Reinforcement Learning
  6. 2026Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing
  7. 2026Lowering the implementation barrier of neutral-atom quantum computing with agentic workflows
  8. 2025Reinforcement Learning for Optimizing Large Qubit Array based Quantum Sensor Circuits