Whole‐Device Entanglement in a 65‐Qubit Superconducting Quantum Computer

Mooney, Gary J.; White, Gregory A. L.; Hill, Charles D.; Hollenberg, Lloyd C. L.

doi:10.1002/qute.202100061

Cited by 43 publications

(18 citation statements)

References 52 publications

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“…As a result of measurement errors, actual quantum states and channels are often more similar to expected states and channels than tomography results would suggest. Since measurement errors on IBM quantum processors are mostly classical 22 , quantum readout error mitigation can be used to obtain tomography results which more accurately reflect the prepared states and channels, as has been successfully done in a number of recent studies which also involved measurements on highly entangled states on IBM quantum processors 32 – 34 . To mitigate readout errors in a n -qubit experiment (the main experiment), we first use quantum detector tomography 35 to construct a by calibration matrix .…”

Section: Introductionmentioning

confidence: 99%

Implementation of single-qubit measurement-based t-designs using IBM processors

Strydom

Tame

2022

Sci Rep

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Random unitary matrices sampled from the uniform Haar ensemble have a number of important applications both in cryptography and in the simulation of a variety of fundamental physical systems. Since the Haar ensemble is very expensive to sample, pseudorandom ensembles in the form of t-designs are frequently used as an efficient substitute, and are sufficient for most applications. We investigate t-designs generated using a measurement-based approach on superconducting quantum computers. In particular, we implemented an exact single-qubit 3-design on IBM quantum processors by performing measurements on a 6-qubit graph state. By analysing channel tomography results, we were able to show that the ensemble of unitaries realised was a 1-design, but not a 2-design or a 3-design under the test conditions set, which we show to be a result of depolarising noise during the measurement-based process. We obtained improved results for the 2-design test by implementing an approximate 2-design, in which measurements were performed on a smaller 5-qubit graph state, but the test still did not pass for all states. This suggests that the practical realisation of measurement-based t-designs on superconducting quantum computers will require further work on the reduction of depolarising noise in these devices.

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Section: Introductionmentioning

confidence: 99%

Implementation of single-qubit measurement-based t-designs using IBM processors

Strydom

Tame

2022

Sci Rep

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“…121 Later, Mooney et al 122 have generated and verified 27-qubit GHZ states with a fidelity of 0.546 ± 0.017 on the quantum computer ibmq_montreal 123 after quantum readout error mitigation, with a detectable improvement in fidelity after parity verification. The same investigators 124 have demonstrated entanglement of all 53 qubits on ibmq_rochester 125 and all 65 qubits on ibmq_manhattan. 126 Earlier, Sager, Smart, and Mazziotti 127 had prepared states interpretable as exciton condensates of photon particles and holes on ibmq_rochester, using 53 qubits.…”

Section: Introductionmentioning

confidence: 92%

Shannon and von Neumann entropies of multi-qubit Schrödinger's cat states

Jansen

Loucks

Gilbert

et al. 2022

Phys. Chem. Chem. Phys.

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“…To investigate the effect this has on our results we re-run our simulation with a noise model [1]. To reduce the impact of these errors we implement a measurement error mitigation technique [20] recently demonstrated in the context of verifying whole-array entanglement in the IBM Quantum ibmq_manhattan device [17]. This procedure consists of performing an initial calibration of the basis states for the noisy device.…”

Section: Simulations With Noise and Error Mitigationmentioning

confidence: 99%

Quantum Support Vector Machines for Continuum Suppression in B Meson Decays

Heredge

Hill

Hollenberg

et al. 2021

Comput Softw Big Sci

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Quantum computers have the potential to speed up certain computational tasks. A possibility this opens up within the field of machine learning is the use of quantum techniques that may be inefficient to simulate classically but could provide superior performance in some tasks. Machine learning algorithms are ubiquitous in particle physics and as advances are made in quantum machine learning technology there may be a similar adoption of these quantum techniques. In this work a quantum support vector machine (QSVM) is implemented for signal-background classification. We investigate the effect of different quantum encoding circuits, the process that transforms classical data into a quantum state, on the final classification performance. We show an encoding approach that achieves an average Area Under Receiver Operating Characteristic Curve (AUC) of 0.848 determined using quantum circuit simulations. For this same dataset the best classical method tested, a classical Support Vector Machine (SVM) using the Radial Basis Function (RBF) Kernel achieved an AUC of 0.793. Using a reduced version of the dataset we then ran the algorithm on the IBM Quantum ibmq_casablanca device achieving an average AUC of 0.703. As further improvements to the error rates and availability of quantum computers materialise, they could form a new approach for data analysis in high energy physics.

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Whole‐Device Entanglement in a 65‐Qubit Superconducting Quantum Computer

Cited by 43 publications

References 52 publications

Implementation of single-qubit measurement-based t-designs using IBM processors

Implementation of single-qubit measurement-based t-designs using IBM processors

Shannon and von Neumann entropies of multi-qubit Schrödinger's cat states

Quantum Support Vector Machines for Continuum Suppression in B Meson Decays

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