Alejandro Perdomo-Ortiz Senior Research Scientist, Quantum AI Lab. at NASA Ames Research Center and at the University Space Research Association, USA Honorary Senior Research Associate, Computer Science Dept., UCL, UK National Harbor, MD, September 28, 2017 Opportunities and challenges in quantum-enhanced machine learning in near-term quantum computers QUBITS D-wave User Group 2017 Funding: Perdomo-Ortiz, Benedetti, Realpe-Gomez, and Biswas. arXiv:1708.09757 (2017). To appear in the Quantum Science and Technology (QST) invited special issue on “What would you do with a 1000 qubit device?”
Opportunities and challenges for quantum-assisted machine learning in near-termquantum computers
Alejandro Perdomo-Ortiz,1, 2, 3, ⇤ Marcello Benedetti,1, 3 John Realpe-Gomez,1, 4, 5 and Rupak Biswas6
1Quantum Artificial Intelligence Lab., NASA Ames Research Center, Mo↵ett Field, CA 94035, USA2USRA Research Institute for Advanced Computer Science (RIACS), 615 National, Mountain View CA 94043, USA
3Department of Computer Science, University College London, WC1E 6BT London, United Kingdom4SGT Inc., 7701 Greenbelt Rd., Suite 400, Greenbelt, MD 20770, USA
5Instituto de Matematicas Aplicadas, Universidad de Cartagena, Bolıvar 130001, Colombia6Exploration Technology Directorate, NASA Ames Research Center, Mo↵ett Field, CA 94035, USA
[Abstract here]
I. INTRODUCTION
With the advent of quantum computing technologiesnearing the era of commercialization and of quantumsupremacy [? ], it is a pressing task to think of potentialapplications that might benefit from these devices. Ma-chine learning stands out as a powerful statistical frame-work that have allowed for the solution of problems wheredeterministic algorithms are hard to develop. Examplesof such algorithms include image and voice recognition,medical applications [Marc: mention here other selectedkick ass app for ML]. The development of quantum algo-rithms that can assist or replace in it entirety the classi-cal ML routine is an ongoing e↵ort that has attracted alot of interest in the scientific quantum information com-munity. [cite all or most representative work here, e.g.,Dorband et al :’D]
Although the focus has been on tasks such as classifi-cation [cite Rebentrost, etc, etc], linear regression [cite],Gaussian models [cite Fitzsimmons] [find other represen-tative examples] corresponding to the most widely usednowadays by machine learning practitioners, we do notforesee these ones would be important for near-term usesof quantum computers. The same reasons that makethese techniques so popular, e.g., their scalability andalgorithmic e�ciency in tackling huge datasets, makesthese techniques less appealing to become top candi-dates as killer application in quantum machine learningwith devices in the range of 100-1000 qubits. In otherwords, to reach interesting industrial scale applicationsit would be required at least millions or even billionsof qubits before one make them competitive with classi-cal e�cient algorithms. This is even under the assump-tion of a quadratic or exponential speedup, which be-comes a mute advantage when dealing with real-worlddatasets and with the quantum devices to come in thenext decades nearing the 1000s of qubits regime. Only agame changer, as elaborated later on this perspective, forexample by introducing hybrid algorithms might be ableto also make a dent in speeding up such routine tasks.
In our perspective here, we propose and emphasize two
approaches to maximize the possibilities of finding killerapplications on near-term quantum computers:
(i) Focus on problems that are currently hard and in-tractable for the ML community: For example,fully generative models, unsupervised and semi-supervised learning as described in Sec. II.
(ii) Focus on hybrid quantum algorithms that can beeasily integrated in the intractable step of the MLalgorithmic pipeline, as described in Sec. IV.
Each one of these tasks have their own challenges andsignificant work need to be done towards having experi-mental implementations on available quantum hardware(see e.g., [Benedetti1, Benedetti2]. Based on our pastexperience in implementing quantum-assisted ML algo-rithms on existing quantum hardware devices, we providehere some insights into the main challenges ahead in de-veloping such opportunities for QML. Although the focushere is on implementations on quantum annealers, we at-tempt to provide parallel insights in other computationalparadigms such as the gate model of quantum computa-tion. The guidance on which problems to be tackled isguided by our expertise in ML and by the advice fromexperts in the ML community.In Sec. II we present examples of domains in ML we
believe o↵er vlable opportunities for near-term quantumcomputers. In Sec. III we present the challenges ahead ofsuch implementations in real hardware, while in Sec. IVwe provide some advice on potential solutions to over-coming these challenges towards such implementations.In Sec. ?? we summarize our work.
II. OPPORTUNITIES IN QML
a. Scenarios where labeled data is scarce: Our fo-cus is on unsupervised learning techniques that canextract salient spatiotemporal features from unlabeleddata. This is important because one of the central as-pects of science is the discovery of unknown patterns;nobody tells scientists which patterns they should lookfor. This also serves as a pre-training phase that can sub-stantially reduce the amount of labeled data needed for
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