# The Power of One Qubit in Machine Learning.

@article{Ghobadi2019ThePO, title={The Power of One Qubit in Machine Learning.}, author={Roohollah Ghobadi and Jaspreet Singh Oberoi and Ehsan Zahedinejhad}, journal={arXiv: Quantum Physics}, year={2019} }

Kernel methods are used extensively in classical machine learning, especially in the field of pattern analysis. In this paper, we propose a kernel-based quantum machine learning algorithm that can be implemented on a near-term, intermediate scale quantum device. Our proposal is based on estimating classically intractable kernel functions, using a restricted quantum model known as deterministic quantum computing with one qubit. Our method provides a framework for studying the role of quantum…

## 9 Citations

Quantum Multiple Kernel Learning

- Computer ScienceArXiv
- 2020

This work proposes an MKL method, referred to as quantum MKL, which combines multiple quantum kernels, and leverages the power of deterministic quantum computing with one qubit to estimate the combined kernel for a set of classically intractable individual quantum kernels.

Patterns for Hybrid Quantum Algorithms

- Computer ScienceSummerSOC
- 2021

The best practices for splitting strategies as patterns are described to foster a common understanding of hybrid algorithms, which split the computational tasks between classical and quantum computers circumventing some of these limitations.

Relevance of Near-Term Quantum Computing in the Cloud: A Humanities Perspective

- Computer Science, PhysicsCLOSER
- 2020

It is shown that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts, and that the cloud is a fine environment for performing quantum programs.

A Hybrid System for Learning Classical Data in Quantum States

- Computer Science2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)
- 2021

This paper proposes GenQu, a hybrid and general-purpose quantum framework for learning classical data through quantum states, and demonstrates that, compared with classical solutions, the proposed models running on GenQu framework achieve similar accuracy with a much smaller number of qubits, while significantly reducing the parameter size.

Data re-uploading for a universal quantum classifier

- PhysicsQuantum
- 2020

Extensive benchmarking on different examples of the single- and multi-qubit quantum classifier validates its ability to describe and classify complex data.

Classical Optical Analogue of Quantum Discord

- Physics
- 2022

: Quantum discord has been shown to be a resource for quantum advantage in addition to quantum entanglement. While many experiments have demonstrated classical analogies of entanglement, none have…

Quantum in the Cloud: Application Potentials and Research Opportunities

- Computer Science, PhysicsCLOSER
- 2020

It is shown that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts, showing that the cloud is a fine environment for performing quantum programs.

GENQU: A HYBRID SYSTEM FOR LEARNING CLASSI-

- Computer Science
- 2020

GenQu is proposed, a hybrid and general-purpose quantum framework for learning classical data through quantum states and demonstrates that, comparing with classical solutions, the proposed models running on GenQu framework achieve similar accuracy with a much smaller number of qubits, while significantly reducing the parameter size.

Quantum Fan-out: Circuit Optimizations and Technology Modeling

- Computer Science, Physics2021 IEEE International Conference on Quantum Computing and Engineering (QCE)
- 2021

This work leverages this simultaneous fan-out primitive to optimize circuit synthesis for NISQ (Noisy Intermediate-Scale Quantum) workloads and introduces novel quantum memory architectures based on fan- out.

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