Thermodynamics of Computations with Absolute Irreversibility, Unidirectional Transitions, and Stochastic Computation Times

被引:8
|
作者
Manzano, Gonzalo [1 ]
Kardes, Guelce [2 ,3 ]
Roldan, Edgar [4 ]
Wolpert, David H. [3 ,4 ]
机构
[1] UIB CSIC, Inst Cross Disciplinary Phys & Complex Syst IFISC, Mallorca, Spain
[2] Univ Colorado, Boulder, CO 80309 USA
[3] Santa Fe Inst, Santa Fe, NM 87501 USA
[4] ICTP Abdus Salam Int Ctr Theoret Phys, Str Costiera 11, I-34151 Trieste, Italy
来源
PHYSICAL REVIEW X | 2024年 / 14卷 / 02期
关键词
SYSTEMS; INFORMATION; PRINCIPLE;
D O I
10.1103/PhysRevX.14.021026
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Developing a thermodynamic theory of computation is a challenging task at the interface of nonequilibrium thermodynamics and computer science. In particular, this task requires dealing with difficulties such as stochastic halting times, unidirectional (possibly deterministic) transitions, and restricted initial conditions, features common in real-world computers. Here, we present a framework which tackles all such difficulties by extending the martingale theory of nonequilibrium thermodynamics to generic nonstationary Markovian processes, including those with broken detailed balance and/or absolute irreversibility. We derive several universal fluctuation relations and second-law-like inequalities that provide both lower and upper bounds on the intrinsic dissipation (mismatch cost) associated with any periodic process-in particular, the periodic processes underlying all current digital computation. Crucially, these bounds apply even if the process has stochastic stopping times, as it does in many computational machines. We illustrate our results with exhaustive numerical simulations of deterministic finite automata processing bit strings, one of the fundamental models of computation from theoretical computer science. We also provide universal equalities and inequalities for the acceptance probability of words of a given length by a deterministic finite automaton in terms of thermodynamic quantities, and outline connections between computer science and stochastic resetting. Our results, while motivated from the computational context, are applicable far more broadly.
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页数:32
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