Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers

Abstract

Noisy intermediate-scale quantum computing devices are an exciting platform for the exploration of the power of near-term quantum applications. Performing nontrivial tasks in such a framework requires a fundamentally different approach than what would be used on an error-corrected quantum computer. One such approach is to use hybrid algorithms, where problems are reduced to a parameterized quantum circuit that is often optimized in a classical feedback loop. Here we described one such hybrid algorithm for machine learning tasks by building upon the classical algorithm known as random kitchen sinks. Our technique, called quantum kitchen sinks, uses quantum circuits to nonlinearly transform classical inputs into features that can then be used in a number of machine learning algorithms. We demonstrate the power and flexibility of this proposal by using it to solve binary classification problems for synthetic datasets as well as handwritten digits from the MNIST database. We can show, in particular, that small quantum circuits provide significant performance lift over standard linear classical algorithms, reducing classification error rates from 50% to < 0.1%, and from 4.1% to 1.4% in these two examples, respectively.

Kernel Matrix Completion for Offline Quantum-Enhanced Machine Learning

“…Unfortunately, an understanding of how quantum-enhanced ML might be affected by practical considerations of data science workflows has been lacking in the research literature. In particular, existing work has often applied quantum-enhanced ML to canonical data sets such as Iris and MNIST [16,[27][28][29][30]. To our knowledge, no work has been done studying the implications of quantum-enhanced ML that would process streaming data.…”

Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices

“…Quantum machine learning on nearterm devices, especially for quantum optical systems, is proposed in Refs [67,68]. Quantum circuit learning with parameterized quantum circuits has been already experimentally demonstrated on superconducting qubit systems [46,69] and a trapped ion system [70].…”

Predicting excited states from ground state wavefunction by supervised quantum machine learning

“…On the other hand, machine learning algorithms on a quantum processor have been developed since the invention of Harrow-Hassidim-Lloyd (HHL) algorithm, and they are dubbed as "quantum machine learning" [9,10,11,12,13,14,15,16]. In the last few years, there has been surging interest in quantum machine learning leveraging the variational method [17,18,19,20,21,22] because a primitive type of quantum computers is about to be realized in the near future, and such machines may have the potentials to outperform classical computers [23,24]. Those near-term quantum computers are called noisy intermediate-scale quantum (NISQ) devices [25] and consist of hundreds to thousands of physical, non-fault-tolerant qubits.…”