Memory compression and thermal efficiency of quantum implementations of non-deterministic hidden Markov models
Stochastic modelling is an essential component of the quantitative sciences, with hidden Markov models (HMMs) often playing a central role. Concurrently, the rise of quantum technologies promises a host of advantages in computational problems, typically in terms of the scaling of requisite resources such as time and memory. HMMs are no exception to this, with recent results highlighting quantum implementations of deterministic HMMs exhibiting superior memory and thermal efficiency relative to their classical counterparts. In many contexts however, non-deterministic HMMs are viable alternatives; compared to them the advantages of current quantum implementations do not always hold. Here, we provide a systematic prescription for constructing quantum implementations of non-deterministic HMMs that re-establish the quantum advantages against this broader class. Crucially, we show that whenever the classical implementation suffers from thermal dissipation due to its need to process information in a time-local manner, our quantum implementations will both mitigate some of this dissipation, and achieve an advantage in memory compression.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Improving Quantum Machine Learning via Heat-Bath Algorithmic Cooling
This work introduces an approach rooted in quantum thermodynamics to enhance sampling efficiency in quantum machine learning (QML). We propose conceptualizing quantum supervised learning as a thermodynamic cooling proces…
Quantum Machine LearningQuantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
We introduce a new class of generative quantum-neural-network-based models called Quantum Hamiltonian-Based Models (QHBMs). In doing so, we establish a paradigmatic approach for quantum-probabilistic hybrid variational l…
Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learning
A quantum thermal machine is an open quantum system that enables the conversion between heat and work at the micro or nano-scale. Optimally controlling such out-of-equilibrium systems is a crucial yet challenging task wi…
FrictionReinforcement Learning (RL)Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting
We present the Quantum Kernel-Based Long short-memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures to enhance predictive accuracy and computational efficiency in climate ti…
Computational EfficiencyTime SeriesTime Series ForecastingStructured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs
Both industry and academia have extensively investigated hardware accelerations. In this work, to address the increasing demands in computational capability and memory requirement, we propose structured weight matrices (…