Single-qubit universal classifier implemented on an ion-trap quantum device

Abstract

Quantum computers can provide solutions to classically intractable problems under specific and adequate conditions. However, current devices have only limited computational resources, and an effort is made to develop useful quantum algorithms under these circumstances. This work experimentally demonstrates that a single-qubit device can host a universal classifier. The quantum processor used in this work is based on ion traps, providing highly accurate control on small systems. The algorithm chosen is the re-uploading scheme, which can address general learning tasks. Ion traps suit the needs of accurate control required by re-uploading. In the experiment here presented, a set of non-trivial classification tasks are successfully carried. The training procedure is performed in two steps combining simulation and experiment. Final results are benchmarked against exact simulations of the same method and also classical algorithms, showing a competitive performance of the ion-trap quantum classifier. This work constitutes the first experimental implementation of a classification algorithm based on the re-uploading scheme.

arXiv comment13 pages, 11 figures, and 1 table

References in corpus (28)

• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum Machine Learning
• Supervised learning with quantum enhanced feature spaces
• Quantum computational advantage using photons
• Quantum support vector machine for big data classification
• Noisy intermediate-scale quantum (NISQ) algorithms
• Experimental realization of Shor's quantum factoring algorithm using nuclear magnetic resonance
• Strong quantum computational advantage using a superconducting quantum processor
• Randomized Benchmarking of Quantum Gates
• Circuit-centric quantum classifiers
• Demonstration of a small programmable quantum computer with atomic qubits
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Data re-uploading for a universal quantum classifier
• Efficient Z-Gates for Quantum Computing
• A rigorous and robust quantum speed-up in supervised machine learning
• High-Fidelity Universal Gate Set for ${}^9$Be${}^{+}$ Ion Qubits
• Quantum chemistry calculations on a trapped-ion quantum simulator
• Single ion-qubit exceeding one hour coherence time
• Complete 3-Qubit Grover Search on a Programmable Quantum Computer
• Entanglement-Based Machine Learning on a Quantum Computer
• Single-qubit-gate error below 10^-4 in a trapped ion
• Basic circuit compilation techniques for an ion-trap quantum machine
• Single atom energy-conversion device with a quantum load
• One qubit as a Universal Approximant
• Quantum advantage for computations with limited space
• Optical Barium Ion Qubit
• A single atom noise probe operating beyond the Heisenberg limit

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• Data re-uploading with a single qudit
• Demonstration of a bosonic quantum classifier with data re-uploading
• Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator
• The Power of One Clean Qubit in Supervised Machine Learning
• Virtual distillation with noise dilution
• A didactic approach to quantum machine learning with a single qubit
• A scalable narrow linewidth high power laser for barium ion optical qubit
• Quantum machine learning for multiclass classification beyond kernel methods
• Optimized numerical gradient and Hessian estimation for variational quantum algorithms
• Demonstration of sequential processors with quantum advantage and analysis of classical performance limits
• Investigating Roles of Triple Excitations for High-precision Determination of Clock Properties of Alkaline Earth Metal Singly Charged Ions
• Robustness of optimized numerical estimation schemes for noisy variational quantum algorithms
• Application a quantum machine learning algorithm to the applied problem of modeling the thermal regime of a servers