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arXiv:2610.38527

Harnessing Non-Markovian Memory for Quantum-Battery Capacity

Jun-Li Jiang, Xin-Zhu Liu, Xiu-Yong Ding, Xue Yang, Ming-Xing Luo

quant-phSubmitted Oct 28, 2026• Announced Oct 29, 2026• 0 citations
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Abstract

Non-Markovianity is commonly characterized through information-theoretic signatures of memory. Here we take a thermodynamic perspective and ask whether such memory can be operationally harnessed to enhance quantum-battery capacity. Within the process-tensor framework, we compare memoryless and memory-enhanced strategies and define their performance gap as the memory-assisted capacity advantage of a multitime process. We prove that this advantage vanishes for Markovian process tensors and is bounded by a trace-distance measure of process non-Markovianity. In a complementary continuous-time setting, we show using random telegraph dephasing that memory accumulated during the evolution can enhance capacity retention relative to a matched Markovian reference under the same control class. These results identify non-Markovian memory as an operational resource for quantum-battery capacity.

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Submitted to Physical Review A

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Non-Markovianity is commonly characterized through information-theoretic signatures of memory. Here we take a thermodynamic perspective and ask whether such memory can be operationally harnessed to enhance quantum-battery capacity. Within the process-tensor framework, we compare memoryless and memory-enhanced strategies and define their performance gap as the memory-assisted capacity advantage of a multitime process. We prove that this advantage vanishes for Markovian process tensors and is bounded by a trace-distance measure of process non-Markovianity. In a complementary continuous-time setting, we show using random telegraph dephasing that memory accumulated during the evolution can enhance capacity retention relative to a matched Markovian reference under the same control class. These results identify non-Markovian memory as an operational resource for quantum-battery capacity.
Quantum thermodynamics studies how quantum systems and operations may be exploited as sources of work to perform useful thermodynamic tasks. In real-world conditions, the evolution of open quantum systems typically displays memory effects, resulting in a non-Markovian dynamics. The associated information backflow has been observed to provide advantage in certain thermodynamic tasks. However, a general operational connection between non-Markovianity and thermodynamics in the quantum regime has remained elusive. Here, we analyze the role of non-Markovianity in the central task of extracting work via thermal operations from general multitime quantum processes, as described by process tensors. By defining a hierarchy of four classes of extraction protocols, expressed as quantum combs, we reveal three different physical mechanisms (work investment, multitime correlations, and system-environment correlations) through which non-Markovianity increases the work distillable from the process. The advantages arising from these mechanisms are linked precisely to a quantifier of the non-Markovianity of the process. These results show in very general terms how non-Markovianity of any given quantum process is a fundamental resource that unlocks an enhanced performance in thermodynamics.
We develop a resource-theoretic framework that allows one to bridge the gap between two approaches to quantum thermodynamics based on Markovian thermal processes (which model memoryless dynamics) and thermal operations (which model arbitrarily non-Markovian dynamics).Our approach is built on the notion of memory-assisted Markovian thermal processes, where memoryless thermodynamic processes are promoted to non-Markovianity by explicitly modeling ancillary memory systems initialized in thermal equilibrium states.Within this setting, we propose a family of protocols composed of sequences of elementary two-level thermalizations that approximate all transitions between energy-incoherent states accessible via thermal operations.We prove that, as the size of the memory increases, these approximations become arbitrarily good for all transitions in the infinite temperature limit, and for a subset of transitions in the finite temperature regime.Furthermore, we present solid numerical evidence for the convergence of our protocol to any transition at finite temperatures.We also explain how our framework can be used to quantify the role played by memory effects in thermodynamic protocols such as work extraction.Finally, our results show that elementary control over two energy levels at a given time is sufficient to generate all energy-incoherent transitions accessible via thermal operations if one allows for ancillary thermal systems.
Abstract We investigate non-Markovianity and its effects on thermodynamic principles and quantum battery performance within a structured nonequilibrium environment consisting of a three-level memory unit and two thermal reservoirs. We demonstrate that temperature imbalance, average temperature, and memory structure serve as effective control parameters for modulating non-Markovianity, exhibiting distinct behaviors for bosonic and fermionic reservoirs. An improved Landauer’s bound applicable to non-Markovian processes is derived, overcoming the limitations of its conventional counterpart. By treating the central system as a quantum battery and the environment as a charger, we show that steady-state ergotropy can be achieved and tuned via thermodynamic parameters. Furthermore, non-Markovian dynamics significantly delay the discharge of the battery. Our study provides a pathway for constructing non-Markovian thermodynamic environments and harnessing non-Markovian effects to enhance the performance of quantum thermal devices.
Non-Markovian quantum dynamics, characterized by information backflow from the environment to the system, has emerged as a potential resource for quantum technologies. A key challenge is therefore to control and enhance such memory effects. In this work, we investigate the use of reinforcement learning (RL) to maximize non-Markovianity in a driven two-level system coupled to a structured reservoir. We compare RL-based control strategies with standard optimal control theory (OCT). We show that OCT produces localized but relatively weak revivalsin the instantaneous non-Markovianity rate, whereas RL policies generate significantly stronger and better-timed information backflow by synchronizing the system dynamics with favorable memory intervals of the environment. This enhanced exploitation of memory effects leads to a higher total integrated non-Markovianity for RL than for OCT, with SAC achieving the largest overall enhancement and PPO delivering slightly lower but still strongly improved performance with smoother, experimentally attractive pulses. Our results contribute to the emerging view of non-Markovianity as an operational resource and illustrate how RL can serve as a flexible, model-free tool for non-Markovian quantum control.
Featuring memory of past inputs is a fundamental requirement for machine learning models processing time-dependent data. In quantum reservoir computing, all architectures proposed so far rely on Markovian dynamics, which, as we prove, inherently lead to an exponential decay of past information, thereby limiting long-term memory capabilities. We demonstrate that non-Markovian dynamics can overcome this limitation, enabling extended memory retention. By analytically deriving memory bounds and supporting our findings with numerical simulations, we show that non-Markovian reservoirs can outperform their Markovian counterparts, particularly in tasks that require a coexistence of short- and long-term correlations. We introduce an embedding approach that allows a controlled transition from Markovian to non-Markovian evolution, providing a path for practical implementations. Our results establish quantum non-Markovianity as a key resource for enhancing memory in quantum machine learning architectures, with broad implications in quantum neural networks.
We investigate the performance of the charger-mediated quantum battery modeled by a two-qubit system. One of the qubits acts as the battery and the other acts as the charger which is subjected to a reservoir. We derived the time-local master equation in Lindblad form with a time-dependent dephasing rate. The dephasing rate may be negative in the early-stage of the charging process and thus indicate the presence of the memory effect. We find that such early-stage memory effect could increase the maximal ergotropy of the battery compared with the one under Markovian approximation with the corresponding asymptotic dephase rate. The enhancement of the performance is explained by means of the non-Markovian quantum jumps. Moreover, a discrete time scheme of the measurement-enhanced quantum battery is proposed in a quantum circuit with global and random local operations.