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arXiv:2503.19896 · Physical Review Letters

Energetic Advantages for Quantum Agents in Online Execution of Complex Strategies

Jayne Thompson, Paul M. Riechers, Andrew J. P. Garner, Thomas J. Elliott, Mile Gu

Published Jul 17, 2025• 4 citations
arXivHTMLPDF

Abstract

Agents often execute complex strategies, adapting their response to each input stimulus depending on past observations and actions. Here, we derive the minimal energetic cost for classical agents to execute a given strategy, highlighting that they must dissipate a certain amount of heat with each decision beyond Landauer's limit. We then prove that quantum agents can reduce this dissipation below classical limits. We establish the necessary and sufficient conditions for a strategy to guarantee quantum agents have energetic advantage, and illustrate settings where this advantage grows without bound. Our results establish a fundamental energetic advantage for agents utilizing quantum processing to enact complex adaptive behavior.

DOI
10.1103/dryx-l5rt

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Agents often execute complex strategies, adapting their response to each input stimulus depending on past observations and actions. Here, we derive the minimal energetic cost for classical agents to execute a given strategy, highlighting that they must dissipate a certain amount of heat with each decision beyond Landauer's limit. We then prove that quantum agents can reduce this dissipation below classical limits. We establish the necessary and sufficient conditions for a strategy to guarantee quantum agents have energetic advantage, and illustrate settings where this advantage grows without bound. Our results establish a fundamental energetic advantage for agents utilizing quantum processing to enact complex adaptive behavior.
Whether winning blackjack or navigating busy streets, achieving desired outcomes requires agents to execute adaptive strategies, strategies where actions depend contextually on past events. In complexity science, this motivates memory as an operational quantifier of complexity: given two strategies, the more complex one demands the agent to track more about the past. Here, we show that conclusions about complexity fundamentally depend on whether agents can process and store quantum information. Thus, while classical agents might find Strategy A more complex to execute than Strategy B, quantum agents can reach the opposite conclusion. We derive sufficient conditions for such contradictory conclusions and illustrate the phenomenon across multiple scenarios. As a byproduct, our results yield an information-theoretic lower bound on the minimal memory required by any agent - classical or quantum - to execute a given strategy.
Central to the success of adaptive systems is their ability to interpret signals from their environment and respond accordingly-they act as agents interacting with their surroundings.Such agents typically perform better when able to execute increasingly complex strategies.This comes with a cost: the more information the agent must recall from its past experiences, the more memory it will need.Here we investigate the power of agents capable of quantum information processing.We uncover the most general form a quantum agent need adopt to maximize memory compression advantages and provide a systematic means of encoding their memory states.We show these encodings can exhibit extremely favorable scaling advantages relative to memoryminimal classical agents, particularly when information must be retained about events increasingly far into the past.
Adaptive agents realise complex reactive behaviours by using a memory of past input stimuli and output actions to guide structured future responses. Quantum adaptive agents can operate while storing less information in memory than optimal classical counterparts; yet, this does not necessarily translate into a reduced dimension of the memory that must be physically realised. We introduce a route-truncate-repair procedure that can convert entropic quantum memory advantages into reductions in memory dimension. Routing a reference input process through an agent yields a temporal matrix product state representation whose canonical bond is identified with the joint reference--agent memory. Truncating the agent's share of this bond and locally repairing the resulting dynamics produces a smaller, physically-valid agent that remains capable of responding to arbitrary input sequences. A fidelity-divergence certificate quantifies the resulting trade-off between accuracy and memory dimension. Benchmark adaptive processes exhibit substantial dimension reduction whilst preserving the underlying behaviour with high fidelity. The construction extends to feedback and coherent quantum interactions, with accuracy guarantees under arbitrary adaptive interrogation for a class of agents. For a resettable clock, four quantum memory dimensions attain an input--output fidelity divergence rate first matched with eleven states in our search over classical reduced models. These results establish a route from entropic memory advantages to practical, dimension-reduced adaptive quantum agents.
Combining quantum computing techniques in the form of amplitude amplification with classical reinforcement learning has led to the so-called “hybrid agent for quantum-accessible reinforcement learning”, which achieves a quadratic speedup in sample complexity for certain learning problems. So far, this hybrid agent has only been applied to stationary learning problems, that is, learning problems without any time dependency within components of the Markov decision process. In this work, we investigate the applicability of the hybrid agent to dynamic RL environments and thus to more realistic scenarios. To this end, we enhance the hybrid agent by introducing a dissipation mechanism and, with the resulting learning agent, perform an empirical comparison with a classical RL agent in an RL environment with a time-dependent reward function. Our findings suggest that the modified hybrid agent can adapt its behavior to changes in the environment quickly, leading to a higher average success probability compared to its classical counterpart.
Irreversibility has a fundamental thermodynamic cost, erasing information inevitably generates heat. This connection is quantified by the Landauer bound, which gives the minimum dissipation needed to erase a single bit of information. While this bound applies in both classical and quantum settings, it is saturated only in idealised limits of infinite resources. Here, we provide a unified first principles description of finite-resource erasure in both classical and quantum systems. We begin by proving the communal folklore that in the idealised regime the erasure cost of a bit encoded in a quantum or classical system is the same. Despite this, we show that their practical implementation differs substantially: achieving comparable erasure quality in quantum systems requires more control, larger accessible energy gaps and longer operation times. Classical protocols can achieve the erasure of a comparable quantum protocol under far weaker constraints which we expose in trade-off relations. Our results explain why practical erasure schemes fall short of Landauer's bound and show that classical systems enjoy several fundamental thermodynamic advantages.
The amount of heat generated by computers is rapidly becoming one of the main problems for developing new generations of information technology. The thermodynamics of computation sets the ultimate physical bounds on heat generation. A lower bound is set by the Landauer limit, at which computation becomes thermodynamically reversible. For classical computation there is no physical principle which prevents this limit being reached, and approaches to it are already being experimentally tested. In this paper we show that for quantum computation with a set of signal states satisfying given conditions, there is an unavoidable excess heat generation that renders it inherently thermodynamically irreversible. The Landauer limit cannot, in general, be reached by quantum computers. We show the existence of a lower bound to the heat generated by quantum computing that exceeds that given by the Landauer limit, give the special conditions where this excess cost may be avoided, and provide a protocol for achieving the limiting heat cost when these conditions are met. We also show how classical computing falls within the special conditions.