Quantum compiling means fast, device-aware implementation of quantum algorithms (i.e. quantum circuits, in the quantum circuit model of computation). In this paper, we present a strategy for compiling IBM Q-aware, low-depth quantum circuits that generate Greenberger-Horne-Zeilinger (GHZ) entangled states. The resulting compiler can replace the QISKit compiler for the specific purpose of obtaining improved GHZ circuits. It is well known that GHZ states have several practical applications, including quantum machine learning. We illustrate our experience in implementing and querying a uniform quantum example oracle based on the GHZ circuit, for solving the classically hard problem of learning parity with noise.

Efficient and effective quantum compiling for entanglement-based machine learning on IBM Q devices / Ferrari, Davide; Amoretti, Michele. - In: INTERNATIONAL JOURNAL OF QUANTUM INFORMATION. - ISSN 0219-7499. - 16:8(2018). [10.1142/S0219749918400063]

Efficient and effective quantum compiling for entanglement-based machine learning on IBM Q devices

FERRARI, DAVIDE
Software
;
Michele Amoretti
Conceptualization
2018

Abstract

Quantum compiling means fast, device-aware implementation of quantum algorithms (i.e. quantum circuits, in the quantum circuit model of computation). In this paper, we present a strategy for compiling IBM Q-aware, low-depth quantum circuits that generate Greenberger-Horne-Zeilinger (GHZ) entangled states. The resulting compiler can replace the QISKit compiler for the specific purpose of obtaining improved GHZ circuits. It is well known that GHZ states have several practical applications, including quantum machine learning. We illustrate our experience in implementing and querying a uniform quantum example oracle based on the GHZ circuit, for solving the classically hard problem of learning parity with noise.
Efficient and effective quantum compiling for entanglement-based machine learning on IBM Q devices / Ferrari, Davide; Amoretti, Michele. - In: INTERNATIONAL JOURNAL OF QUANTUM INFORMATION. - ISSN 0219-7499. - 16:8(2018). [10.1142/S0219749918400063]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2854764
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