Classical stochastic processes can be generated by quantum simulators instead of the more standard classical ones, such as hidden Markov models. One reason for using quantum simulators has recently come to the fore: they generally require less memory than their classical counterparts. Here, we examine this quantum advantage for strongly coupled spin systems-in particular, the Dyson one-dimensional Ising spin chain with variable interaction length. We find that the advantage scales with both interaction range and temperature, growing without bound as interaction range increases. In particular, simulating Dyson's original spin chain with the most memory-efficient classical algorithm known requires infinite memory, while a quantum simulator re...
We present a comprehensive comparison of spin and energy dynamics in quantum and classical spin mode...
We present a comprehensive comparison of spin and energy dynamics in quantum and classical spin mode...
We introduce a quantum algorithm for memory-efficient biased sampling of rare events generated by cl...
Classical stochastic processes can be generated by quantum simulators instead of the more standard c...
Classical stochastic processes can be generated by quantum simulators instead of the more s...
Simulating the stochastic evolution of real quantities on a digital computer requires a trade-off be...
We investigate the problem of simulating classical stochastic processes through quantum dynamics and...
Computer simulation of observable phenomena is an indispensable tool for engineering new technology,...
The minimal memory required to model a given stochastic process - known as the statistical complexit...
Stochastic processes underlie a vast range of natural and social phenomena. Some processes such as a...
Thesis (Ph.D.)--University of Washington, 2015In this dissertation we study classical and quantum sp...
Quantum coherence allows for reduced-memory simulators of classical processes. Using recent results ...
This thesis studies computational advantages that could be achieved by using quantum resources in tw...
Continuous-time stochastic processes pervade everyday experience, and the simulation of models of th...
Simulating stochastic processes using less resources is a key pursuit in many sciences. This involve...
We present a comprehensive comparison of spin and energy dynamics in quantum and classical spin mode...
We present a comprehensive comparison of spin and energy dynamics in quantum and classical spin mode...
We introduce a quantum algorithm for memory-efficient biased sampling of rare events generated by cl...
Classical stochastic processes can be generated by quantum simulators instead of the more standard c...
Classical stochastic processes can be generated by quantum simulators instead of the more s...
Simulating the stochastic evolution of real quantities on a digital computer requires a trade-off be...
We investigate the problem of simulating classical stochastic processes through quantum dynamics and...
Computer simulation of observable phenomena is an indispensable tool for engineering new technology,...
The minimal memory required to model a given stochastic process - known as the statistical complexit...
Stochastic processes underlie a vast range of natural and social phenomena. Some processes such as a...
Thesis (Ph.D.)--University of Washington, 2015In this dissertation we study classical and quantum sp...
Quantum coherence allows for reduced-memory simulators of classical processes. Using recent results ...
This thesis studies computational advantages that could be achieved by using quantum resources in tw...
Continuous-time stochastic processes pervade everyday experience, and the simulation of models of th...
Simulating stochastic processes using less resources is a key pursuit in many sciences. This involve...
We present a comprehensive comparison of spin and energy dynamics in quantum and classical spin mode...
We present a comprehensive comparison of spin and energy dynamics in quantum and classical spin mode...
We introduce a quantum algorithm for memory-efficient biased sampling of rare events generated by cl...