Detecting crosstalk errors in quantum information processors

Sarovar, Mohan; Proctor, Timothy; Rudinger, Kenneth; Young, Kevin; Nielsen, Erik; Blume-Kohout, Robin

doi:10.22331/q-2020-09-11-321

Cited by 168 publications

(105 citation statements)

References 49 publications

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“…Furthermore, this noise model does not include any crosstalk effects (see, e.g. [30]), despite evidence that they play some role in today's NISQ processors.…”

Section: Noisy Simulationmentioning

confidence: 99%

Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

et al. 2021

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Our recent work (Ayral et al. in Proceedings of IEEE computer society annual symposium on VLSI, ISVLSI, pp 138–140, 2020. 10.1109/ISVLSI49217.2020.00034) showed the first implementation of the Quantum Divide and Compute (QDC) method, which allows to break quantum circuits into smaller fragments with fewer qubits and shallower depth. This accommodates the limited number of qubits and short coherence times of quantum processors. This article investigates the impact of different noise sources—readout error, gate error and decoherence—on the success probability of the QDC procedure. We perform detailed noise modeling on the Atos Quantum Learning Machine, allowing us to understand tradeoffs and formulate recommendations about which hardware noise sources should be preferentially optimized. We also describe in detail the noise models we used to reproduce experimental runs on IBM’s Johannesburg processor. This article also includes a detailed derivation of the equations used in the QDC procedure to compute the output distribution of the original quantum circuit from the output distribution of its fragments. Finally, we analyze the computational complexity of the QDC method for the circuit under study via tensor-network considerations, and elaborate on the relation the QDC method with tensor-network simulation methods.

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“…Furthermore, this noise model does not include any crosstalk effects (see, e.g. [30]), despite evidence that they play some role in today's NISQ processors.…”

Section: Noisy Simulationmentioning

confidence: 99%

Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

et al. 2021

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“…The recent advent of high-performance quantum information processors (QIPs) has precipitated greater sensitivity to complex dynamical effects. In particular, it is clear that device behaviour must be understood under a relaxed Markov assumption [1][2][3] . The resulting non-Markovian dynamics includes more general errors that may be temporally correlated or dependent on broader environmental context [4][5][6] .…”

mentioning

confidence: 99%

Demonstration of non-Markovian process characterisation and control on a quantum processor

et al. 2020

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In the scale-up of quantum computers, the framework underpinning fault-tolerance generally relies on the strong assumption that environmental noise affecting qubit logic is uncorrelated (Markovian). However, as physical devices progress well into the complex multi-qubit regime, attention is turning to understanding the appearance and mitigation of correlated — or non-Markovian — noise, which poses a serious challenge to the progression of quantum technology. This error type has previously remained elusive to characterisation techniques. Here, we develop a framework for characterising non-Markovian dynamics in quantum systems and experimentally test it on multi-qubit superconducting quantum devices. Where noisy processes cannot be accounted for using standard Markovian techniques, our reconstruction predicts the behaviour of the devices with an infidelity of 10−3. Our results show this characterisation technique leads to superior quantum control and extension of coherence time by effective decoupling from the non-Markovian environment. This framework, validated by our results, is applicable to any controlled quantum device and offers a significant step towards optimal device operation and noise reduction.

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“…5 and we find amplitude reduction of a factor of about 0.82 with a negligible phase shift. The difference between simulation and experimental results can be attributed to time-dependent noise, various cross-talk effects 35 , and non-Markovian noise.…”

Section: Experimental and Simulation Resultsmentioning

confidence: 87%

Quantum classifier with tailored quantum kernel

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et al. 2020

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Kernel methods have a wide spectrum of applications in machine learning. Recently, a link between quantum computing and kernel theory has been formally established, opening up opportunities for quantum techniques to enhance various existing machine-learning methods. We present a distance-based quantum classifier whose kernel is based on the quantum state fidelity between training and test data. The quantum kernel can be tailored systematically with a quantum circuit to raise the kernel to an arbitrary power and to assign arbitrary weights to each training data. Given a specific input state, our protocol calculates the weighted power sum of fidelities of quantum data in quantum parallel via a swap-test circuit followed by two single-qubit measurements, requiring only a constant number of repetitions regardless of the number of data. We also show that our classifier is equivalent to measuring the expectation value of a Helstrom operator, from which the well-known optimal quantum state discrimination can be derived. We demonstrate the performance of our classifier via classical simulations with a realistic noise model and proof-of-principle experiments using the IBM quantum cloud platform.

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Detecting crosstalk errors in quantum information processors

Cited by 168 publications

References 49 publications

Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

Demonstration of non-Markovian process characterisation and control on a quantum processor

Quantum classifier with tailored quantum kernel

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