Optimization of the memory reset rate of a quantum echo-state network for time sequential tasks

Molteni, Riccardo; Destri, C.; Prati, Enrico

doi:10.1016/j.physleta.2023.128713

Cited by 7 publications

(7 citation statements)

References 23 publications

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“…Already in the classical framework (see [45]), it has been shown that this can be avoided by working with affine instead of purely linear systems. In the quantum context, it can also be observed (see [37,41]) that the nonhomogeneous statespace system given by ρ t = (1 − )T (ρ t−1 , z t ) + σ , where T (ρ t−1 , z t ) is a CPTP map, σ an arbitrary density matrix, and 0 < < 1, defines a unique filter U (z…”

Section: Resultsmentioning

confidence: 99%

“…where B i are the basis elements of a single qubit in the operator space {I, σ x , σ y , σ z } and S (U ) i j := U † SU is a unitary transformation of the Choi matrix S. Applying (41) to the definition of matrix T in (19), we can find the map of the single-qubit observables…”

Section: Constrains On Cptp Maps For Qrcmentioning

confidence: 99%

“…We work in an idealized framework in which observables are obtained after an infinite number of measurements, with no statistical error. Even in such an idealized setting, we expect that our results can contribute to the general understanding of QRC experiments in finite-dimensional systems, as they have already been implemented in [37,38,40,41].…”

Section: Introductionmentioning

confidence: 96%

“…Since the first work on QRC [34], many have followed (see [35,36] for reviews). This includes both experimental implementations [37][38][39][40][41] and theoretical contributions to the universal approximation question [37,42,43]. The latter was inspired by the works in the classical framework [44][45][46][47], where the discrete-time setting of RC theory fits well within the quantum dynamical map description.…”

Section: Introductionmentioning

confidence: 99%

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Quantum reservoir computing in finite dimensions

Martínez-Peña

Ortega

2023

Phys. Rev. E

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Section: Resultsmentioning

confidence: 99%

Section: Constrains On Cptp Maps For Qrcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantum reservoir computing in finite dimensions

Martínez-Peña

Ortega

2023

Phys. Rev. E

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“…By using QAE, it is possible to estimate the integrals with the same precision by extracting fewer samples, thus significantly reducing the computational resources required for numerical integration. Moreover, QAE can be used to solve a wide range of numerical integration problems, including those in finance [11], physics [12,13], and machine learning [14][15][16][17]. It has been shown that QAE can be applied to estimate options pricing [18] in financial derivatives [19,20], to solve differential equations [21], and to perform quantum simulation, among others.…”

Section: Introductionmentioning

confidence: 99%

The Quantum Amplitude Estimation Algorithms on Near-Term Devices: A Practical Guide

Maronese,

Incudini,

Asproni

et al. 2023

Quantum Reports

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The Quantum Amplitude Estimation (QAE) algorithm is a major quantum algorithm designed to achieve a quadratic speed-up. Until fault-tolerant quantum computing is achieved, being competitive over classical Monte Carlo (MC) remains elusive. Alternative methods have been developed so as to require fewer resources while maintaining an advantageous theoretical scaling. We compared the standard QAE algorithm with two Noisy Intermediate-Scale Quantum (NISQ)-friendly versions of QAE on a numerical integration task, with the Monte Carlo technique of Metropolis–Hastings as a classical benchmark. The algorithms were evaluated in terms of the estimation error as a function of the number of samples, computational time, and length of the quantum circuits required by the solutions, respectively. The effectiveness of the two QAE alternatives was tested on an 11-qubit trapped-ion quantum computer in order to verify which solution can first provide a speed-up in the integral estimation problems. We concluded that an alternative approach is preferable with respect to employing the phase estimation routine. Indeed, the Maximum Likelihood estimation guaranteed the best trade-off between the length of the quantum circuits and the precision in the integral estimation, as well as greater resistance to noise.

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Quantum Parallel Training of a Boltzmann Machine on an Adiabatic Quantum Computer

Noè,

Rocutto,

Moro

et al. 2024

Adv Quantum Tech

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Despite the anticipated speed‐up of quantum computing, the achievement of a measurable advantage remains subject to ongoing debate. Adiabatic Quantum Computers (AQCs) are quantum devices designed to solve quadratic uncostrained binary optimization (QUBO) problems, but their intrinsic thermal noise can be leveraged to train computationally demanding machine learning algorithms such as the Boltzmann Machine (BM). Despite an asymptotic advantage is expected only for large networks, a limited quantum speed up can be already achieved on a small BM is shown, by exploiting parallel adiabatic computation. This approach exhibits a 8.6‐fold improvement in wall time on the Bars and Stripes dataset when compared to a parallelized classical Gibbs sampling method, which has never been outperformed before by quantum approaches.

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Optimization of the memory reset rate of a quantum echo-state network for time sequential tasks

Cited by 7 publications

References 23 publications

Quantum reservoir computing in finite dimensions

Quantum reservoir computing in finite dimensions

The Quantum Amplitude Estimation Algorithms on Near-Term Devices: A Practical Guide

Quantum Parallel Training of a Boltzmann Machine on an Adiabatic Quantum Computer

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