Exploiting Dynamic Quantum Circuits in a Quantum Algorithm with Superconducting Qubits

Córcoles, Antonio; Takita, Maika; Inoue, Ken; Lekuch, Scott; Minev, Zlatko K.; Chow, Jerry M.; Gambetta, Jay

doi:10.1103/physrevlett.127.100501

Cited by 95 publications

(49 citation statements)

References 62 publications

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“…These operations are a limiting factor for the hardware implementation of many quantum algorithms, as measuring or resetting the state of a qubit can result in collapsing the wavefunction of other qubits in the system. IBMQ is one quantum development platform which has recently enabled reset operations in its newest hardware for dynamic circuit execution 37 , but avoiding qubit reset in favor of sequential circuit execution remains desirable for many quantum hardware platforms and applications.…”

Section: Resultsmentioning

confidence: 99%

A quantum Hopfield associative memory implemented on an actual quantum processor

Miller

Mukhopadhyay

2021

Sci Rep

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In this work, we present a Quantum Hopfield Associative Memory (QHAM) and demonstrate its capabilities in simulation and hardware using IBM Quantum Experience.. The QHAM is based on a quantum neuron design which can be utilized for many different machine learning applications and can be implemented on real quantum hardware without requiring mid-circuit measurement or reset operations. We analyze the accuracy of the neuron and the full QHAM considering hardware errors via simulation with hardware noise models as well as with implementation on the 15-qubit ibmq_16_melbourne device. The quantum neuron and the QHAM are shown to be resilient to noise and require low qubit overhead and gate complexity. We benchmark the QHAM by testing its effective memory capacity and demonstrate its capabilities in the NISQ-era of quantum hardware. This demonstration of the first functional QHAM to be implemented in NISQ-era quantum hardware is a significant step in machine learning at the leading edge of quantum computing.

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Section: Resultsmentioning

confidence: 99%

A quantum Hopfield associative memory implemented on an actual quantum processor

Miller

Mukhopadhyay

2021

Sci Rep

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“…One interesting model is dynamic quantum circuits (see e.g. [36], [37]) in which quantum measurements can occur at the middle of the circuits and the measurement outcomes are used to conditionally control subsequent steps of the computation. A fault in these circuits may happen not only at a quantum gate but also at a measurement.…”

Section: Discussionmentioning

confidence: 99%

Automatic Test Pattern Generation for Robust Quantum Circuit Testing

Chen¹,

Ying²

2022

Preprint

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Quantum circuit testing is essential for detecting potential faults in realistic quantum devices, while the testing process itself also suffers from the inexactness and unreliability of quantum operations. This paper alleviates the issue by proposing a novel framework of automatic test pattern generation (ATPG) for the robust quantum circuit testing. We introduce the stabilizer projector decomposition (SPD) for representing the quantum test pattern, and construct the test application using classical randomness and Clifford-only circuits, which are rather robust and efficient as shown in the fault-tolerant quantum computation. However, it is generally hard to generate SPDs due to the super-exponentially growing number of the stabilizer projectors. To circumvent this difficulty, we develop an SPD generation algorithm, as well as several acceleration techniques which can exploit both locality and sparsity in generating SPDs. Furthermore, we provide in-depth analyses for the proposed algorithms, both theoretically and empirically. To demonstrate the effectiveness and efficiency, we implement and evaluate our algorithms on several commonly used benchmark circuits, such as Quantum Fourier Transform (QFT), Quantum Volume (QV) and Bernstein-Vazirani (BV) in IBM Qiskit. For example, test patterns are automatically generated by our algorithm for a 10qubit QFT circuit, and then a fault is detected by simulating the test application with detection accuracy higher than 91%.

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“…A quantum circuit is a computational routine consisting of an ordered sequence of quantum operations including gates, measurements, and resets on quantum data (qubits) and concurrent real-time classical computation [1,2]. Data flows between the quantum operations and the real-time classical compute so that the classical compute can incorporate measurement results and the quantum operations may be conditioned upon or parameterized by data from the real-time classical compute.…”

Section: Definitions a Quantum Circuits And Programsmentioning

confidence: 99%

“…Physical qubit architectures may effect the repetition time, gate times, reset times, and measurement times and can vary significantly across technologies. For example, the repetition rate and the gate rate of superconducting qubits [1] can be orders of magnitude faster than the ones of ion trap qubits [10] which significantly impacts the CLOPS. Similarly, software components such as run-time compilation, orchestration of the control electronics, etc.…”

Section: Fig 3 Matrix Of Circuits Used For Clops Benchmarkmentioning

confidence: 99%

Quality, Speed, and Scale: three key attributes to measure the performance of near-term quantum computers

Wack¹,

Paik²,

Javadi-Abhari³

et al. 2021

Preprint

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Exploiting Dynamic Quantum Circuits in a Quantum Algorithm with Superconducting Qubits

Cited by 95 publications

References 62 publications

A quantum Hopfield associative memory implemented on an actual quantum processor

A quantum Hopfield associative memory implemented on an actual quantum processor

Automatic Test Pattern Generation for Robust Quantum Circuit Testing

Quality, Speed, and Scale: three key attributes to measure the performance of near-term quantum computers

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