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Springer eBooks

Efficient Classical Simulation of Continuous Variable Quantum Information Processes

Stephen D. Bartlett, Barry C. Sanders, Samuel L. Braunstein, Kae Nemoto

Published Jan 1, 2002• 31 citations
Publisher
DOI
10.1007/978-94-015-1258-9_6

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We obtain sufficient conditions for the efficient simulation of a continuous variable quantum algorithm or process on a classical computer. The resulting theorem is an extension of the Gottesman-Knill theorem to continuous variable quantum information. For a collection of harmonic oscillators, any quantum process that begins with unentangled Gaussian states, performs only transformations generated by Hamiltonians that are quadratic in the canonical operators, and involves only measurements of canonical operators (including finite losses) and suitable operations conditioned on these measurements can be simulated efficiently on a classical computer.

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Abstract Quantum reservoir computing (QRC) is a machine learning paradigm in which a quantum system is used to perform information processing. A prospective approach to its physical realization is a photonic platform in which continuous variable quantum information methods are applied. The simplest continuous variable quantum states are Gaussian states, which can be efficiently simulated classically. As such, they provide a benchmark for the level of performance that non-Gaussian states should surpass in order to give a quantum advantage. In this article we propose two methods to increase the information processing capacity of QRC with Gaussian states compared to previous QRC schemes. We consider better utilization of the measurement distribution by sampling its cumulative distribution function. We show it provides memory in areas that conventional approaches are lacking, as well as improving the overall processing capacity of the reservoir. We also consider storing past measurement results in classical memory, and show that it improves the memory capacity and can be used to mitigate the effects of statistical noise due to finite measurement ensemble.

arXiv:2507.03557•New Journal of Physics•Sep 1, 2025•1 citation
We obtain sufficient conditions for the efficient simulation of a continuous variable quantum algorithm or process on a classical computer. The resulting theorem is an extension of the Gottesman-Knill theorem to continuous variable quantum information. For a collection of harmonic oscillators, any quantum process that begins with unentangled Gaussian states, performs only transformations generated by Hamiltonians that are quadratic in the canonical operators, and involves only measurements of canonical operators (including finite losses) and suitable operations conditioned on these measurements can be simulated efficiently on a classical computer.
We prove that continuous variable quantum information processing via Gaussian preserving operations, with Gaussian input states plus a (highly nonclassical) Fock number state, and subject to homodyne measurements and feedforward, can be efficiently simulated on a classical computer. This result reinforces the importance of the cubic phase state as an offline resource for universal continuous variable quantum information processing. We study the practical realization of this state by determining the Wigner function for an approximate cubic phase state prepared via two-mode squeezing with postselection on the signal field by displacing and counting photons in the idler field.
Abstract Quantum reservoir computing (QRC) is a machine learning paradigm in which a quantum system is used to perform information processing. A prospective approach to its physical realization is a photonic platform in which continuous variable quantum information methods are applied. The simplest continuous variable quantum states are Gaussian states, which can be efficiently simulated classically. As such, they provide a benchmark for the level of performance that non-Gaussian states should surpass in order to give a quantum advantage. In this article we propose two methods to increase the information processing capacity of QRC with Gaussian states compared to previous QRC schemes. We consider better utilization of the measurement distribution by sampling its cumulative distribution function. We show it provides memory in areas that conventional approaches are lacking, as well as improving the overall processing capacity of the reservoir. We also consider storing past measurement results in classical memory, and show that it improves the memory capacity and can be used to mitigate the effects of statistical noise due to finite measurement ensemble.