Slide 1: Opportunities and challenges in quantum-enhanced machine learning in near-term quantum computers
Opportunities and challenges in quantum-enhanced machine learning in near-term quantum computers 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
Slide 2: D-Wave System Capability
D-Wave System Capability 1) As a discrete optimization solver: Potential NASA applications: - planning Given {hj, Jij}, find {sk = ± 1} NP-hard - scheduling that minimizes problem - fault diagnosis - graph analysis N X N X - communication networks, etc. ⇠(s1 , ..., sN ) = hj s j + Jij si sj QUBO: Qu
Slide 3: Unsupervised learning (generative models)
Unsupervised learning (generative models) Learn the “best” model distribution that can generate the same kind of data MODEL P ( Image ) Learning algorithm NO LABELS DATASET
Slide 4: Unsupervised learning (generative models)
Unsupervised learning (generative models) Learn the “best” model distribution that Example application: can generate the same kind of data Image reconstruction MODEL P ( Image ) Reconstructed image Learning algorithm LEARNED MODEL P ( Image ) NO LABELS Damaged image DATASET
Slide 5: Supervised learning (discriminative models)
Supervised learning (discriminative models) Learn the “best” model that can Example application: perform a specific task Image recognition MODEL Predicted P ( Label | Image ) label 61 Learning algorithm LEARNED MODEL Labels P ( Label | Image ) 26624 98 66 175 Image to be recognized DATASET
Slide 6: A near-term approach for quantum-enhanced machine learning
A near-term approach for quantum-enhanced machine learning State-of-the-art QML - Most previous proposed work have - Need for qRAM (case of most gate- highly optimized powerful classical based proposal). counterparts (e.g., on - Qubits represent visible units; issue discriminative/classification tas
Slide 7: A near-term approach for quantum-enhanced machine learning: hybrid approaches
A near-term approach for quantum-enhanced machine learning Lesson 2: Need for novel hybrid approaches. Challenges solved: PREDICTIONS Benedetti, et al. Estimation of effective temperatures in quantum annealers for F [ P(s|Θt) ] sampling applications: A case study with possible applications in deep l
Slide 8: A near-term approach: challenges of the hybrid approach
A near-term approach for quantum-enhanced machine learning Challenges of the hybrid approach: - Need to solve classical-quantum model mismatch Training Method: Stochastic gradient ascent X @ ln L(✓|v) / hvi uj idata hvi uj imodel @Jij v2D Classical - Quantum Teff? Benedetti et al . Phys. Rev. A 94,
Slide 9: Resolving model mismatch with importance sampling
Without Importance Sampling A near-term approach for quantum-enhanced machine learning 11 0 200 400 600 800 Resolving model mismatch allows for restarting from classical preprocessing 1000 6 (c) QuALe @ TDW2X = 0.033 with Restart 7 QuALe @ Te↵ with Restart 8 CD-1 Lav 9 10 11 0 100 200 300 400 500 it
Slide 10: A near-term approach: challenges of the hybrid approach
A near-term approach for quantum-enhanced machine learning Challenges of the hybrid approach: - Need to solve classical-quantum model - Robustness to noise, Fully visible models mismatch intrinsic control errors, and to deviations from Training Method: Stochastic gradient ascent sampling model (e.g.
Slide 11: Quantum-assisted unsupervised learning on digits
Quantum-assisted unsupervised learning on digits OptDigits Datasets 32x32 8x8 7x6 7x6, binarized Dataset: Optical Recognition of Handwritten Digits (OptDigits)
Slide 12: Quantum-assisted unsupervised learning on digits
Quantum-assisted unsupervised learning on digits OptDigits Datasets Dataset: Optical Recognition of Handwritten Digits (OptDigits)
Slide 13: Quantum-assisted unsupervised learning on digits
Quantum-assisted unsupervised learning on digits Overcoming the curse of limited connectivity in hardware. 46 fully-connected logical (visible) variables 42 for pixels + 4 to one-hot encode the class (only digits 1-4) - Are the results from this training on 940 qubit experiment meaningful? - Is the
Slide 14: Quantum-assisted unsupervised learning on digits
Quantum-assisted unsupervised learning on digits Human or (quantum) machine? (Turing test) Human (quantum) machine Dataset: Optical Recognition of Handwritten Digits (OptDigits) Results from experiments using 940 qubits, without post-processing. The hardware-embedded model represents a 46 node fully
Slide 15: A near-term approach for quantum-enhanced machine learning
A near-term approach for quantum-enhanced machine learning Challenges of the hybrid approach: - Need to solve classical-quantum model - Robustness to noise, Fully visible models mismatch intrinsic control errors, and to deviations from Training Method: Stochastic gradient ascent sampling model (e.g.
Slide 16: Perspective on quantum-enhanced machine learning: classical pre- and post-processing
Perspective on quantum-enhanced machine learning • New hybrid proposal that works directly on a low-dimensional representation of the data. processing Quantum Compressed data Classical pre- and post-processing Hidden Inference layers Raw input data Training samples Visible units Hidden units Qubits
Slide 17: Perspective on quantum-enhanced machine learning: classical pre- and post-processing
Perspective on quantum-enhanced machine learning • New hybrid proposal that works directly on a low-dimensional representation of the data. Quantum sampling dˆ ⇢✓ (t) processing i~ = [Ĥ✓ , ⇢ˆ✓ ] Quantum dˆ ⇢✓ (t) dt i~ = [Ĥ✓ , ⇢ˆ✓ ] dt ✓ Compressed data Classical pre- and post-processing Classical
Slide 18: Perspective on quantum-enhanced machine learning: classical pre- and post-processing
Perspective on quantum-enhanced machine learning • New hybrid proposal that works directly on a low-dimensional representation of the data. Quantum sampling dˆ ⇢✓ (t) processing i~ = [Ĥ✓ , ⇢ˆ✓ ] Quantum dˆ ⇢✓ (t) dt i~ = [Ĥ✓ , ⇢ˆ✓ ] dt ✓ Compressed data Classical pre- and post-processing Classical
Slide 19: Experimental implementation of the QAHM
Experimental implementation of the QAHM Experiments using 1644 qubits (no further postprocessing!) Max. CL = 43 Benedetti, Realpe-Gomez, and Perdomo-Ortiz. Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices. arXiv:1708.09784
Slide 20: Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers Alejandro Perdomo-Ortiz,1, 2, 3, ⇤ Marcello Benedetti,1, 3 John Realpe-Gómez,1, 4, 5 and Rupak Biswas6 1 Quantum Artificial Intelligence Lab., NASA Ames Research Center, Mo↵ett Field, CA 94035, USA 2 U
Slide 21: Job advertisement
Job advertisement NASA Ames Research Center Opportunities at NASA Quantum AI Lab. (NASA QuAIL) at different levels: internships, postdoc, or Research Scientist. For details, please contact: Eleanor Rieffel: NASA QuAIL Lead, or, Alejandro Perdomo-Ortiz: Quantum Machine Learning Lead. eleanor.rieffel@
Slide 22: Support slides
Support slides