P02 - Models of Quantum Learning and Computation

Hans Jürgen Briegel


Quantum machine learning is a new and rapidly growing research field within quantum information. It studies the use of quantum computers to enhance the efficiency of machine learning algorithms, for example for pattern recognition and big data analysis, and, conversely, the use of classical machine learning techniques in quantum physics, for example for the design of new quantum experiments.

The long-term goals and visions of our project are to (i) contribute towards a general theory of quantum learning within the agent-environment framework; (ii) integrate methods of reinforcement learning with protocols of quantum information; (iii) apply learning agents to the study of complex quantum systems; (iv) bring the field closer to experimental realizations.

PI Hans-Jürgen Briegel on
Models of Quantum Learning and Computation


Subproject Leader: Hans Jürgen Briegel

Co-PI: Fulvio Flamini, Lukas Fiderer

PhDs: Andrea López Incera, Sofiene Jerbi, Isaac David Smith, Hendrik Poulsen Nautrup

Master: Gilles Glesener, Francesco Preti

Admins: Jade Meysami-Hörtnagl


Quantum Enhancements for Deep Reinforcement Learning in Large Spaces
S. Jerbi, L. M. Trenkwalder, H. Poulsen Nautrup, H. J. Briegel, and V. Dunjko
PRX Quantum 2, 010328 (2021)

Entangling logical qubits with lattice surgery
A. Erhard, H. P. Nautrup, M. Meth, L. Postler, R. Stricker, M. Ringbauer, Ph. Schindler, H. J. Briegel, R. Blatt, N. Friis, Th. Monz
Nature 589, 220-224 (2021)

Photonic architecture for reinforcement learning
F. Flamini, A. Hamann, S. Jerbi, L. M. Trenkwalder, H. Poulsen Nautrup, H. J. Briegel
New J. Phys. 22 045002 (2020)

On the convergence of projective-simulation–based reinforcement learning in Markov decision processes
W. L. Boyajian, J. Clausen, L. M. Trenkwalder, V. Dunjko, H. J. Briegel
Quantum Mach. Intell. 2, 13 (2020)

Optimizing quantum error correction codes with reinforcement learning
H. Poulsen Nautrup, N. Delfosse, V. Dunjko, H. J. Briegel, N. Friis,
Quantum 3, 215 (2019)

Experimental quantum speed-up in reinforcement learning agents
V. Saggio, B. E. Asenbeck, A. Hamann, T. Strömberg, P. Schiansky, V. Dunjko, N. Friis, N. C. Harris,
M. Hochberg, D. Englund, S. Wölk, H. J. Briegel, P. Walther,
Nature 591, 229 (2021)

Parametrized quantum policies for reinforcement learning
S. Jerbi, C. Gyurik, S. C. Marshall, H. J. Briegel, V. Dunjko,
NeurIPS 34 (2021)

Machine learning for long-distance quantum communication
J. Wallnöfer, A. A. Melnikov, W. Dür, H. J. Briegel
PRX Quantum 1, 010301 (2020)

Reinforcement learning for optimization of variational quantum circuit architectures
M. Ostaszewski, L. M. Trenkwalder, W. Masarczyk, E. Scerri, V. Dunjko
NeurIPS 34 (2021)

Quantum-accessible reinforcement learning beyond strictly epochal environments
A. Hamann, V. Dunjko, S. Wölk
Quantum Mach. Intell. 3, 22 (2021)

Performance analysis of a hybrid agent for quantum-accessible reinforcement learning
A. Hamann, S. Wölk
New J. Phys. 24, 033044 (2022)

Emergence of biased errors in imperfect optical circuits
F. Flamini
Phys. Rev. Applied 16, 064038 (2021)

Development of swarm behavior in artificial learning agents that adapt to different foraging environments
A. Lopez-Incera, K. Ried, T. Müller, H. J. Briegel
PLoS ONE 15(12), e0243628 (2020)

Honeybee communication during collective defence is shaped by predation
A. López-Incera, M. Nouvian, K. Ried, T. Müller, H. J. Briegel
BMC Biol 19, 106 (2021)

General expressions for the quantum Fisher information matrix with applications to discrete quantum imaging
L. J. Fiderer, T. Tufarelli, S. Piano, G. Adesso
PRX Quantum 2(2), 020308 (2021)

Witnessing Bell violations through probabilistic negativity
B. Morris, L. J. Fiderer, B. Lang, D. Goldwater
Phys. Rev. A 105, 032202 (2021)

Operationally meaningful representations of physical systems in neural networks
H. Poulsen Nautrup, T. Metger, R. Iten, S. Jerbi, L. M. Trenkwalder, H. Wilming, H. J. Briegel, and R. Renner
preprint arXiv:2001.00593 [quant-ph] (2020)

A framework for deep energy-based reinforcement learning with quantum speed-up
S. Jerbi, H. Poulsen Nautrup, L. M. Trenkwalder, H. J. Briegel, V. Dunjko
preprint arXiv:1910.12760 [quant-ph] (2019)

TensorFlow Quantum: A Software Framework for Quantum Machine Learning
M. Broughton et al.
preprint arXiv:2003.02989v2 [quant-ph]

Quantum machine learning beyond kernel methods
S. Jerbi, L. J. Fiderer, H. Poulsen Nautrup, J. M. Kübler, H. J. Briegel, V. Dunjko,
preprint arXiv:2110.13162v2 [quant-ph]

Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
A. Skolik, S. Jerbi, V. Dunjko
preprint arXiv:2103.15084 [quant-ph]

Other publications:

Active learning machine learns to create new quantum experiments
A. A. Melnikov, H. Poulsen Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, H. J. Briegel
PNAS 115, 1221 (2018), [arXiv:1706.00868]

Quantum machine learning with glow for episodic tasks and decision games
J. Clausen, H. J. Briegel
Phys. Rev. A 97, 022303 (2018), [arXiv:1601.07358]

Faster quantum mixing for slowly evolving sequences of Markov chains
D. Orsucci, H. J. Briegel, V. Dunjko
Quantum 2, 105 (2018), [arXiv:1503.01334v4]

Fault-tolerant interface between quantum memories and quantum processors
H. Poulsen Nautrup, N. Friis, H. J. Briegel
Nat. Commun. 8, 1321 (2017), [arXiv:1609.08062]

Quantum speed-up for active learning agents
G. Paparo, V. Dunjko, A. Makmal, M. A. Martin-Delgado, H. J. Briegel
Phys. Rev. X 4, 031002 (2014)

Projective simulation for artificial intelligence
H. J. Briegel, G. De las Cuevas
Scientific Reports 2, 400 (2012)

For further publications: see here.