Reconstructing Quantum States With Quantum Reservoir Networks

Ghosh, Sanjib; Opala, Andrzej; Matuszewski, Michał; Paterek, Tomasz; Liew, Timothy Chi Hin

doi:10.1109/tnnls.2020.3009716

Cited by 51 publications

(54 citation statements)

References 43 publications

(38 reference statements)

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“…The dynamical evolution of the density matrix of the coupled quantum substrate and the quantum input modes enables different tasks. [ 46,50 ] For instance, quantum input states can be classified when encoded into an ancilla interacting with a fermionic network. [ 46 ] Further, the input quantum state, either in finite dimension or in continuous variable, can be reconstructed.…”

Section: Quantum Resources For Unconventional Computingmentioning

confidence: 99%

“…[ 46 ] Further, the input quantum state, either in finite dimension or in continuous variable, can be reconstructed. [ 50 ] Recently, a quantum input has been also considered in classical RC. For instance, in continuously monitored superconducting qubits.…”

Section: Quantum Resources For Unconventional Computingmentioning

confidence: 99%

“…Conventional quantum tomography schemes are challenging but, with the quantum reservoir approach in ref. [50], the unknown density matrix of the input quantum state can be reconstructed after a single measurement on local observables of the reservoir nodes without the need of any correlation detection. The characterization of quantum state is indeed an important quantum task, also explored in other platforms, like for instance the quantum neuron proposed in ref.…”

Section: Quantum Resources For Unconventional Computingmentioning

confidence: 99%

“…[ 47 ] Inspired by these neuromorphic approaches other quantum tasks have been reported such as quantum states preparation [ 48,49 ] or reconstruction. [ 50 ] Proof‐of‐principle experiments are also ongoing. [ 39,51 ] In particular, QELM has been reported in a NMR experiment, [ 51 ] while QRC has been realized on the quantum computation platform of IBM.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

et al. 2021

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Quantum reservoir computing and quantum extreme learning machines are two emerging approaches that have demonstrated their potential both in classical and quantum machine learning tasks. They exploit the quantumness of physical systems combined with an easy training strategy, achieving an excellent performance. The increasing interest in these unconventional computing approaches is fueled by the availability of diverse quantum platforms suitable for implementation and the theoretical progresses in the study of complex quantum systems. In this review article, recent proposals and first experiments displaying a broad range of possibilities are reviewed when quantum inputs, quantum physical substrates and quantum tasks are considered. The main focus is the performance of these approaches, on the advantages with respect to classical counterparts and opportunities.

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Section: Quantum Resources For Unconventional Computingmentioning

confidence: 99%

Section: Quantum Resources For Unconventional Computingmentioning

confidence: 99%

Section: Quantum Resources For Unconventional Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

et al. 2021

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“…As it is easier to engineer a fixed and random network than a well controlled one, reservoir computing has been successfully implemented in a variety of physical systems [20][21][22][23]. Recently, the reservoir computing concept was brought to the quantum domain [24], using networks of quantum nodes [25,26] and the performance of specific non-classical tasks [27] including quantum state preparation [28] and tomography [29]. While these examples operate with quantum systems, they work with classical data either in the input or output and are far from being quantum computers, which should be able to implement unitary transformations (at least approximately) of a quantum state.…”

mentioning

confidence: 99%

Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

Ghosh

Krisnanda

Paterek

et al. 2021

Preprint

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Quantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing architecture we show how a random network of quantum nodes can be used as a robust hardware for quantum computing. It induces quantum operations by optimising only a single layer of quantum nodes, a key advantage over the traditional neural networks where many layers of neurons have to be optimised. We demonstrate how a single network can induce different quantum gates, including a universal gate set. Moreover, we show that sequences of multiple quantum gates in quantum circuits can be compressed with a single operation, greatly reducing the operation time and complexity. As the key resource is a random network of nodes, with no specific topology or structure, this architecture is a hardware friendly alternative paradigm for quantum computation.

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Time-series quantum reservoir computing with weak and projective measurements

et al. 2023

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Time-series processing is a major challenge in machine learning with enormous progress in the last years in tasks such as speech recognition and chaotic series prediction. A promising avenue for sequential data analysis is quantum machine learning, with computational models like quantum neural networks and reservoir computing. An open question is how to efficiently include quantum measurement in realistic protocols while retaining the needed processing memory and preserving the quantum advantage offered by large Hilbert spaces. In this work, we propose different measurement protocols and assess their efficiency in terms of resources, through theoretical predictions and numerical analysis. We show that it is possible to exploit the quantumness of the reservoir and to obtain ideal performance both for memory and forecasting tasks with two successful measurement protocols. One repeats part of the experiment after each projective measurement while the other employs weak measurements operating online at the trade-off where information can be extracted accurately and without hindering the needed memory, in spite of back-action effects. Our work establishes the conditions for efficient time-series processing paving the way to its implementation in different quantum technologies.

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Reconstructing Quantum States With Quantum Reservoir Networks

Abstract: Reconstructing quantum states with quantum reservoir networks

Cited by 51 publications

References 43 publications

Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

Time-series quantum reservoir computing with weak and projective measurements

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