## Referring to Quantum Inspire

When you need to refer to Quantum Inspire, e.g. in a paper, please use the following reference:

QuTech. (2018). Quantum Inspire Home. Retrieved from Quantum Inspire: https://www.quantum-inspire.com/

## Usefull background papers

QUANTUM COMPUTING with semiconductor spins
*Introductory article on spin qubits in Physics Today*

Interfacing spin qubits in quantum dots and donors—hot,
dense, and coherent
*Review paper in NPJ Quantum Information including a brief summary of electron spin qubits in quantum dots and donors, a derivement of the control signal requirements and challenges and possible solutions to overcome some scaling challenges.*

A programmable two-qubit quantum processor in silicon
*Nature paper on two-qubit algorithms with spin qubits*

Classification using a two-qubit quantum chip
*Example of a two-qubit quantum classificiation algorithm executed on Spin-2*

A Quantum Algorithm for Minimising the Effective Graph Resistance upon Edge Addition
*Article using results from Quantum Inspire, showing that the time complexity of Durr and Høyer’s algorithm for minimising the effective graph resistance is better than the time complexity of
an exhaustive search. This means that there exists a quantum circuit that solves the optimisation problem and whose depth, compared to the depth of a classical circuit implementing an exhaustive search, increases significantly less fast as the number of vertices N increases.*

Implementation and Compact Data Representation in Variational Quantum Machine Learning
*Machine learning (ML) has become an important tool to process data and extract information from them in a great variety of applications. In this work the autors apply a variational quantum circuit on Quantum Inspire and show the results of the learning and the dense representation in their algorithm.*

Hybrid Helmholtz machines: a gate-based quantum circuit implementation
*Quantum machine learning has the potential to overcome problems that current classical machine learning algorithms face, such as large data requirements or long learning times. Sampling is one of the aspects of classical machine learning that might benefit from quantum machine learning, as quantum computers intrinsically excel at sampling. This work proposes the first implementation of a gated quantum-classical hybrid Helmholtz machine, a gate-based quantum circuit approximation of a neural network for unsupervised tasks. Using a bars and stripes data set, the model, implemented on the Quantum Inspire platform, is shown to outperform classical Helmholtz machines in terms of the Kullback–Leibler divergence.*