$i$-QER: An Intelligent Approach towards Quantum Error Reduction

Basu, Saikat; Saha, Amit; Chakrabarti, Amlan; Sur-Kolay, Susmita

doi:10.48550/arxiv.2110.06347

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Using partitioning schemes produces an additional benefit: reduced noise, stemming from the smaller circuit size [7,11], this can be the motivation for cut selection, even when the full circuit would "fit" on a quantum machine [12]. This noise reduction is similar to the increased accuracy we may be able to provide to gradients in our model.…”

Section: Related Workmentioning

confidence: 78%

High Dimensional Quantum Machine Learning With Small Quantum Computers

C.¹,

Casper²,

Dunjko³

2022

Preprint

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Quantum computers hold great promise to enhance machine learning, but their current qubit counts restrict the realisation of this promise. In an attempt to placate this limitation techniques can be applied for evaluating a quantum circuit using a machine with fewer qubits than the circuit naively requires. These techniques work by evaluating many smaller circuits on the smaller machine, that are then combined in a polynomial to replicate the output of the larger machine. This scheme requires more circuit evaluations than are practical for general circuits. However, we investigate the possibility that for certain applications many of these subcircuits are superfluous, and that a much smaller sum is sufficient to estimate the full circuit. We construct a machine learning model that may be capable of approximating the outputs of the larger circuit with much fewer circuit evaluations. We successfully apply our model to the task of digit recognition, using simulated quantum computers much smaller than the data dimension. The model is also applied to the task of approximating a random 10 qubit PQC with simulated access to a 5 qubit computer, even with only relatively modest number of circuits our model provides an accurate approximation of the 10 qubit PQCs output, superior to a neural network attempt. The developed method might be useful for implementing quantum models on larger data throughout the NISQ era.

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Section: Related Workmentioning

confidence: 78%

High Dimensional Quantum Machine Learning With Small Quantum Computers

C.¹,

Casper²,

Dunjko³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Then, as a figure of merit, we can assume 50% to be the limit, ). A similar reduction in noise is also possible for other types of circuit partitioning methods [50].…”

Section: Discussionmentioning

confidence: 75%

Classical Splitting of Parametrized Quantum Circuits

Tüysüz¹,

Clemente²,

Crippa³

et al. 2022

Preprint

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Barren plateaus appear to be a major obstacle to using variational quantum algorithms to simulate large-scale quantum systems or replace traditional machine learning algorithms. They can be caused by multiple factors such as expressivity, entanglement, locality of observables, or even hardware noise. We propose classical splitting of ansätze or parametrized quantum circuits to avoid barren plateaus. Classical splitting is realized by splitting an N qubit ansatz to multiple ansätze that consists of O(log N ) qubits. We show that such an ansatz can be used to avoid barren plateaus. We support our results with numerical experiments and perform binary classification on classical and quantum datasets. Then, we propose an extension of the ansatz that is compatible with variational quantum simulations. Finally, we discuss a speed-up for gradient-based optimization and hardware implementation, robustness against noise and parallelization, making classical splitting an ideal tool for noisy intermediate scale quantum (NISQ) applications.

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$i$-QER: An Intelligent Approach towards Quantum Error Reduction

Cited by 2 publications

References 21 publications

High Dimensional Quantum Machine Learning With Small Quantum Computers

High Dimensional Quantum Machine Learning With Small Quantum Computers

Classical Splitting of Parametrized Quantum Circuits

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