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Quantum advantage in learning from experiments

Journal Article · · Science
 [1];  [2];  [3];  [4];  [5];  [2];  [2];  [2];  [6];  [7];  [2]
  1. Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA.; Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA.
  2. Google Quantum AI, Venice, CA 90291, USA.
  3. Harvard Society of Fellows, Cambridge, MA 02138, USA.; Black Hole Initiative, Cambridge, MA 02138, USA.
  4. Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA, USA.; Simons Institute for the Theory of Computing, Berkeley, CA, USA.
  5. Microsoft Research AI, Redmond, WA 98052, USA.
  6. Institute for Integrated Circuits, Johannes Kepler University Linz, Austria.
  7. Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA.; Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA.; AWS Center for Quantum Computing, Pasadena, CA 91125, USA.

Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting properties of physical systems, performing quantum principal component analysis, and learning about physical dynamics. Furthermore, the quantum resources needed for achieving an exponential advantage are quite modest in some cases. Conducting experiments with 40 superconducting qubits and 1300 quantum gates, we demonstrated that a substantial quantum advantage is possible with today’s quantum processors.

Research Organization:
Harvard Univ., Cambridge, MA (United States); California Institute of Technology (CalTech), Pasadena, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0007870; SC0020290; NA0003525
OSTI ID:
1980744
Journal Information:
Science, Vol. 376, Issue 6598; ISSN 0036-8075
Publisher:
AAAS
Country of Publication:
United States
Language:
English

References (42)

Quantum sensing journal July 2017
Perspectives on quantum transduction journal March 2020
Optical quantum memory journal December 2009
Topological quantum memory journal September 2002
Longer-Baseline Telescopes Using Quantum Repeaters journal August 2012
Quantum memories and the double-slit experiment: implications for astronomical interferometry journal June 2021
Advances in quantum metrology journal March 2011
Information-Theoretic Bounds on Quantum Advantage in Machine Learning journal May 2021
Quantum supremacy using a programmable superconducting processor journal October 2019
Quantum nonlocality without entanglement journal February 1999
Exponential Separations Between Learning With and Without Quantum Memory conference February 2022
Quantum principal component analysis journal July 2014
The dominant eigenvector of a noisy quantum state journal December 2021
Quantum Virtual Cooling journal July 2019
Virtual Distillation for Quantum Error Mitigation journal November 2021
Exponential Error Suppression for Near-Term Quantum Devices journal September 2021
Variational quantum state diagonalization journal June 2019
Quantum Principal Component Analysis Only Achieves an Exponential Speedup Because of Its State Preparation Assumptions journal August 2021
Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing Quantum machine learning conference June 2020
Predicting many properties of a quantum system from very few measurements journal June 2020
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification conference January 2015
Nonlinear Component Analysis as a Kernel Eigenvalue Problem journal July 1998
Quantum machine learning journal September 2017
Quantum Computing in the NISQ era and beyond journal August 2018
Quantum-process tomography: Resource analysis of different strategies journal March 2008
Provably efficient machine learning for quantum many-body problems journal September 2022
Long Short-Term Memory journal November 1997
Entanglement is Necessary for Optimal Quantum Property Testing conference November 2020
Direct characterization of quantum dynamics: General theory journal June 2007
Computations with greater Quantum depth are strictly more powerful (relative to an oracle) conference June 2020
On the need for large Quantum depth conference June 2020
A theory of the learnable journal November 1984
Optimal quantum learning of a unitary transformation journal March 2010
Quantum State Discrimination Using the Minimum Average Number of Copies journal January 2017
Experimental multi-state quantum discrimination through optical networks journal March 2022
Online identification of symmetric pure states journal February 2022
Gentle measurement of quantum states and differential privacy conference June 2019
Assouad, Fano, and Le Cam book January 1997
Pseudorandom Quantum States book January 2018
High-Dimensional Probability: An Introduction with Applications in Data Science book September 2018
The Theory of Quantum Information book May 2018
Generalization in quantum machine learning from few training data journal August 2022

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