Efficient and Effective Quantum Compiling for Entanglement-based Machine Learning on IBM Q Devices

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.

14 pages, 13 figures

References in corpus (4)

• Charge insensitive qubit design derived from the Cooper pair box
• Quantum algorithm for solving linear systems of equations
• Improved Bounds on Quantum Learning Algorithms
• Linear and logarithmic time compositions of quantum many-body operators

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• Distributed Quantum Computing: a Survey
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• Compiler Design for Distributed Quantum Computing
• Resource-efficient verification of quantum computing using Serfling's bound
• Quantum Theory of the Classical: Quantum Jumps, Born's Rule, and Objective Classical Reality via Quantum Darwinism
• Eliminating Ensembles from Equilibrium Statistical Physics: Maxwell's Demon, Szilard's Engine, and Thermodynamics via Entanglement
• Reducing the CNOT count for Clifford+T circuits on NISQ architectures
• Deterministic Algorithms for Compiling Quantum Circuits with Recurrent Patterns