Data re-uploading for a universal quantum classifier

Abstract

A single qubit provides sufficient computational capabilities to construct a universal quantum classifier. This fact may be surprising since a single qubit only offers a simple superposition of two states and single qubit gates only make a rotation in the Bloch sphere. The key ingredient to circumvent these limitations is to allow for multiple data re-uploading. A quantum circuit can then be organized as a series of data re-uploading and single-qubit processing units. Furthermore, both data re-uploading and measurements can accommodate multiple dimensions in the input and multiple categories in the output, so as to conform a universal quantum classifier. The extension of this idea to several qubits enhances the efficiency of the strategy as entanglement expands the superpositions carried along the classification. Extensive benchmarking on different examples of the single-and multi-qubit quantum classifier validates its ability to describe and classify complex data.

A feasible approach for automatically differentiable unitary coupled-cluster on quantum computers

“…The original proposal of VQE inspired development of numerous new variational quantum algorithms for estimating energies of quantum chemical and many-body models on quantum computers (see recent reviews 4,5 ) as well as in various other elds, including quantum machine learning, [6][7][8][9][10][11][12][13] combinatorial optimization, 14 and quantum optics. [15][16][17] These developments in combination with the recent availability of open access quantum computers, 18 and signicant improvements of currently available quantum hardware [19][20][21] are currently clearing the path towards Feynman's original idea of simulating physics with quantum computers 22 leveraging this powerful tool to elucidate challenging chemical processes.…”

Quantum unary approach to option pricing

“…These NISQ devices may nonetheless be useful tools for a variety of applications due to the introduction of hybrid variational methods. Some of the proposed applications include quantum chemistry [4][5][6], simulation of physical systems [7][8][9], combinatorial optimization [10], solving large systems of linear equations [11][12][13], state diagonalization [14,15] or quantum machine learning [16][17][18]. Some exact, non-variational, quantum algorithms are also well suited for NISQ devices [19][20][21][22].…”

Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions

“…Some examples include variational autoencoders, [ 8–10 ] variational quantum eigensolvers (VQE), [ 6,11 ] the quantum approximate optimization algorithm (QAOA), [ 12 ] quantum generative adversarial networks (QGANs), [ 13–18 ] among others. [ 19–23 ]…”