Quantum-enhanced analysis of discrete stochastic processes

Blank, Carsten; Park, Daniel K.; Petruccione, Francesco

doi:10.48550/arxiv.2008.06443

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…Moreover, unitarity means such quantum models can generate a quantum superposition of all possible futures if the outputs remain unmeasured. Thus the quantum model produces a key resource for various quantum data analytics algorithms, such as amplitude estimation, Grover's search, value at risk, and importance sampling [43][44][45][46]. The capacity for our models to generate data by sequential application of the same unitary makes them particularly amenable to certain contemporary hardware architectures (e.g., loop-based photonics where only a single optical circuit implementing U needs to be engineered [47]).…”

Section: Discussionmentioning

confidence: 99%

Provable superior accuracy in machine learned quantum models

Yang¹,

Garner²,

Liu³

et al. 2021

Preprint

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In modelling complex processes, the potential past data that influence future expectations are immense. Models that track all this data are not only computationally wasteful but also shed little light on what past data most influence the future. There is thus enormous interest in dimensional reduction -finding automated means to reduce the memory dimension of our models while minimizing its impact on its predictive accuracy. Here we construct dimensionally reduced quantum models by machine learning methods that can achieve greater accuracy than provably optimal classical counterparts. We demonstrate this advantage on present-day quantum computing hardware. Our algorithm works directly off classical time-series data and can thus be deployed in real-world settings. These techniques illustrate the immediate relevance of quantum technologies to time-series analysis and offer a rare instance where the resulting quantum advantage can be provably established.

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Section: Discussionmentioning

confidence: 99%

Provable superior accuracy in machine learned quantum models

Yang¹,

Garner²,

Liu³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition to the memory consideration, a second key advantage of quantum models is their capacity to generate conditional future distributions in quantum superposition [4]. This superposition state then forms a key resource for quantum amplitude estimation protocols that are key for quantum-enhanced analysis of stochastic data [56,57]. In this instance, the extra memory overhead due to error mitigation is not necessarily a significant issue, as it simply reflects the necessity to run simulation a certain extra number of times.…”

Section: Discussionmentioning

confidence: 99%

Enhancing quantum models of stochastic processes with error mitigation

Ho,

Takagi,

2021

Preprint

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Error mitigation has been one of the recently sought after methods to reduce the effects of noise when computation is performed on a noisy near-term quantum computer. Interest in simulating stochastic processes with quantum models gained popularity after being proven to require less memory than their classical counterparts. With previous work on quantum models focusing primarily on further compressing memory, this work branches out into the experimental scene; we aim to bridge the gap between theoretical quantum models and practical use with the inclusion of error mitigation methods. It is observed that error mitigation is successful in improving the resultant expectation values. While our results indicate that error mitigation work, we show that its methodology is ultimately constrained by hardware limitations in these quantum computers.

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Quantum-enhanced analysis of discrete stochastic processes

Cited by 2 publications

References 36 publications

Provable superior accuracy in machine learned quantum models

Provable superior accuracy in machine learned quantum models

Enhancing quantum models of stochastic processes with error mitigation

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