BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:Closing Remarks
DTSTART:20230616T161000Z
DTEND:20230616T161500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-728@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Roger Melko (Perimeter Institute & University of Wat
erloo)\n\nhttps://events.perimeterinstitute.ca/event/36/contributions/728/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/728/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Machine Learning of Conserved Quantities and Symmetry Invariants
DTSTART:20230615T200000Z
DTEND:20230615T204500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-774@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Sebastian Wetzel (University of Waterloo)\n\nhttps:/
/events.perimeterinstitute.ca/event/36/contributions/774/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/774/
END:VEVENT
BEGIN:VEVENT
SUMMARY:[Virtual] Exploring Quantum Science with Machine Learning
DTSTART:20230612T140000Z
DTEND:20230612T144500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-706@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Di Luo (Massachusetts Institute of Technology)\n\nZO
OM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81Y3VaYVpCQT0
9\n\nhttps://events.perimeterinstitute.ca/event/36/contributions/706/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/706/
END:VEVENT
BEGIN:VEVENT
SUMMARY:A QMC study of the Rydberg phase diagram
DTSTART:20230613T191500Z
DTEND:20230613T194500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-715@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Anna Knörr (Perimeter Institute)\n\nhttps://events
.perimeterinstitute.ca/event/36/contributions/715/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/715/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Near Term Distributed Quantum Computation using Optimal Auxiliary
Encoding
DTSTART:20230612T180000Z
DTEND:20230612T184500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-708@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Abigail McClain Gomez (Harvard University)\n\nZOOM:
https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81Y3VaYVpCQT09\n\
nhttps://events.perimeterinstitute.ca/event/36/contributions/708/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/708/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Discussion Session: The Ethics of Quantum and AI
DTSTART:20230614T201500Z
DTEND:20230614T213000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-721@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Gus Skorburg (University of Guelph)\n\nhttps://event
s.perimeterinstitute.ca/event/36/contributions/721/
LOCATION:PI/3-394 - Skyroom (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/721/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ethics PROBES: Artificial Intelligence\, Machine Learning\, & Quan
tum Computing
DTSTART:20230614T193000Z
DTEND:20230614T201500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-720@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Gus Skorburg (University of Guelph)\n\nhttps://event
s.perimeterinstitute.ca/event/36/contributions/720/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/720/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Panel Discussion
DTSTART:20230615T180000Z
DTEND:20230615T193000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-725@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Mykola Maksymenko (Haiqu)\, Eliška Greplová (Delft
University)\, Edwin Miles Stoudenmire ( Flatiron Institute Center for Com
putational Quantum Physics)\, Juan Felipe Carrasquilla (Vector Institute)\
n\nhttps://events.perimeterinstitute.ca/event/36/contributions/725/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/725/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Data-centric learning of Quantum Many-body States with Classical M
achines
DTSTART:20230613T173000Z
DTEND:20230613T181500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-727@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Eun-Ah Kim (Cornell University)\n\nhttps://events.pe
rimeterinstitute.ca/event/36/contributions/727/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/727/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Colloquium
DTSTART:20230613T200000Z
DTEND:20230613T213000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-776@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Neil Turok (University of Edinburgh)\n\nhttps://even
ts.perimeterinstitute.ca/event/36/contributions/776/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/776/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Automated Characterization of Engineered Quantum Materials
DTSTART:20230613T140000Z
DTEND:20230613T144500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-711@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Eliška Greplová (Delft University)\n\nhttps://even
ts.perimeterinstitute.ca/event/36/contributions/711/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/711/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Learning Feynman Diagrams with Tensor Trains
DTSTART:20230613T151500Z
DTEND:20230613T160000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-712@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Xavier Waintal (CEA Grenoble)\n\nhttps://events.peri
meterinstitute.ca/event/36/contributions/712/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/712/
END:VEVENT
BEGIN:VEVENT
SUMMARY:[VIRTUAL] Emergent Classicality from Information Bottleneck
DTSTART:20230615T140000Z
DTEND:20230615T144500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-722@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Yi-Zhuang You (University of California\, San Diego)
\n\nOur universe is quantum\, but our everyday experience is classical. Wh
ere is the boundary between quantum and classical worlds? How does classic
al reality emerge in quantum many-body systems? Does the collapse of the q
uantum states involve intelligence? These are fundamental questions that h
ave puzzled physicists and philosophers for centuries. The recent developm
ent of quantum information science and artificial intelligence offers new
opportunities to investigate these old problems. In this talk\, we present
our preliminary research on using a transformer-based language model to p
rocess randomized measurement data collected from Schrödinger’s cat qua
ntum state. We show that the classical reality emerges in the language mod
el due to the information bottleneck: although our training data contains
the full quantum information of Schrödinger’s cat\, a weak language mod
el can only learn the classical reality of the cat from the data. Our stud
y opens up a new avenue for using the big data generated on noisy intermed
iate-scale quantum (NISQ) devices to train generative models for represent
ation learning of quantum operators\, which might be a step toward our ult
imate goal of creating an artificial intelligence quantum physicist.\n\nht
tps://events.perimeterinstitute.ca/event/36/contributions/722/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/722/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Welcome and Opening Remarks
DTSTART:20230612T135500Z
DTEND:20230612T140000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-705@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Roger Melko (Perimeter Institute & University of Wat
erloo)\n\nZOOM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm8
1Y3VaYVpCQT09\n\nhttps://events.perimeterinstitute.ca/event/36/contributio
ns/705/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/705/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dimension reduction of the Functional Renormalization Group
DTSTART:20230612T191500Z
DTEND:20230612T194500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-709@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Jiawei Zang (Columbia University)\n\nZOOM: https://p
itp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81Y3VaYVpCQT09\n\nIn this
work\, we use data-driven methods to reduce the dimensionality of the vert
ex function for the Hubbard model and spin liquid model. By employing a de
ep learning architecture based on the autoencoder\, we show that the funct
ional renormalization group (FRG) dynamics can be efficiently learned. Our
approach is compared with other methods\, including principal component a
nalysis and dynamic mode decomposition. Our results demonstrate the effect
iveness of our proposed approach for understanding the FRG flow in these m
odels.\n\nhttps://events.perimeterinstitute.ca/event/36/contributions/709/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/709/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Investigating Topological Order with Recurrent Neural Network Wave
Functions
DTSTART:20230614T183000Z
DTEND:20230614T190000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-717@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Mohamed Hibat Allah (Vector Institute)\n\nRecurrent
neural networks (RNNs)\, originally developed for natural language process
ing\, hold great promise for accurately describing strongly correlated qua
ntum many-body systems. In this talk\, we will illustrate how to use 2D RN
Ns to investigate two prototypical quantum many-body Hamiltonians exhibiti
ng topological order. Specifically\, we will demonstrate that RNN wave fun
ctions can effectively capture the topological order of the toric code and
a Bose-Hubbard spin liquid on the kagome lattice by estimating their topo
logical entanglement entropies. Overall\, we will show that RNN wave funct
ions constitute a powerful tool for studying phases of matter beyond Landa
u's symmetry-breaking paradigm.\n\nhttps://events.perimeterinstitute.ca/ev
ent/36/contributions/717/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/717/
END:VEVENT
BEGIN:VEVENT
SUMMARY:[VIRTUAL] A deep variational free energy approach to dense hydroge
n
DTSTART:20230614T140000Z
DTEND:20230614T150000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-719@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Lei Wang (Chinese Academy of Sciences)\n\nDense hydr
ogen\, the most abundant matter in the visible universe\, exhibits a range
of fascinating physical phenomena such as metallization and high-temperat
ure superconductivity\, with significant implications for planetary physic
s and nuclear fusion research. Accurate prediction of the equations of sta
te and phase diagram of dense hydrogen has long been a challenge for compu
tational methods. In this talk\, we present a deep generative model-based
variational free energy approach to tackle the problem of dense hydrogen\,
overcoming the limitations of traditional computational methods. Our appr
oach employs a normalizing flow network to model the proton Boltzmann dist
ribution and a fermionic neural network to model the electron wavefunction
at given proton positions. The joint optimization of these two neural net
works leads to a comparable variational free energy to previous coupled el
ectron-ion Monte Carlo calculations. Our results suggest that hydrogen in
planetary conditions is even denser than previously estimated using Monte
Carlo and ab initio molecular dynamics methods. Having reliable computatio
n of the equation of state for dense hydrogen\, and in particular\, direct
access to its entropy and free energy\, opens new opportunities in planet
ary modeling and high-pressure physics research.\n\nhttps://events.perimet
erinstitute.ca/event/36/contributions/719/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/719/
END:VEVENT
BEGIN:VEVENT
SUMMARY:The Quantum Cartpole
DTSTART:20230616T140000Z
DTEND:20230616T144500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-726@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Evert van Nieuwenburg (Leiden University)\n\nHow do
you control something you can not look at? For controlling quantum systems
\, information on the system’s state could come through weak measurement
s. Such measurements provide some information\, but will inevitably also p
erturb the system\, meaning there is noise both in the state estimation as
well as in the measurement. We study a simple single particle quantum set
up (the quantum equivalent of the instability problem known as the cartpol
e problem) and investigate several control methods including reinforcement
learning\, and compare their performance.\n\nhttps://events.perimeterinst
itute.ca/event/36/contributions/726/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/726/
END:VEVENT
BEGIN:VEVENT
SUMMARY:2D quantum matter with neural quantum states
DTSTART:20230612T151500Z
DTEND:20230612T160000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-707@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Markus Heyl (Max Planck Institute for the Physics of
Complex Systems)\n\nZOOM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpz
YlhFcGlIRm81Y3VaYVpCQT09\n\nNeural quantum states (NQSs) have emerged as a
novel promising numerical method to solve the quantum many-body problem.
However\, it has remained a central challenge to train modern large-scale
deep network architectures to desired quantum state accuracy\, which would
be vital in\nutilizing the full power of NQSs and making them competitive
or superior to conventional numerical approaches. Here\, we propose a min
imum-step stochastic reconfiguration (MinSR) method that reduces the optim
ization complexity by orders of magnitude while keeping similar accuracy a
s compared to conventional stochastic reconfiguration. MinSR allows for ac
curate training on unprecedentedly deep NQS with up to 64 layers and more
than 105 parameters in the spin-1/2 Heisenberg J1-J2 models on the square
lattice. We find that this approach yields better variational energies as
compared to existing numerical results and we further observe that the acc
uracy of our ground state calculations approaches different levels of mach
ine precision on modern GPU and TPU hardware. The MinSR method opens up th
e potential to make NQS superior as compared to conventional computational
methods with the capability to address yet inaccessible regimes for two-d
imensional quantum matter in the future.\n\nhttps://events.perimeterinstit
ute.ca/event/36/contributions/707/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/707/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Quantum HyperNetworks: Training Binary Neural Networks in Quantum
Superposition
DTSTART:20230616T151500Z
DTEND:20230616T160000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-713@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Estelle Inack (Perimeter Institute)\n\nBinary neural
networks\, i.e.\, neural networks whose parameters and activations are co
nstrained to only two possible values\, offer a compelling avenue for the
deployment of deep learning models on energy- and memory-limited devices.
However\, their training\, architectural design\, and hyperparameter tunin
g remain challenging as these involve multiple computationally expensive c
ombinatorial optimization problems. Here we introduce quantum hypernetwork
s as a mechanism to train binary neural networks on quantum computers\, wh
ich unify the search over parameters\, hyperparameters\, and architectures
in a single optimization loop. Through classical simulations\, we demonst
rate that of our approach effectively finds optimal parameters\, hyperpara
meters and architectural choices with high probability on classification p
roblems including a two-dimensional Gaussian dataset and a scaled-down ver
sion of the MNIST handwritten digits. We represent our quantum hypernetwor
ks as variational quantum circuits\, and find that an optimal circuit dept
h maximizes the probability of finding performant binary neural networks.
Our unified approach provides an immense scope for other applications in t
he field of machine learning.\n\nhttps://events.perimeterinstitute.ca/even
t/36/contributions/713/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/713/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Quantum and Classical Dynamics from a Time Dependent Variational P
rinciple
DTSTART:20230613T184500Z
DTEND:20230613T191500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-714@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Moritz Reh (Heidelberg University)\n\nhttps://events
.perimeterinstitute.ca/event/36/contributions/714/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/714/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Unsupervised detection of quantum phases and their order parameter
s from projective measurements
DTSTART:20230614T180000Z
DTEND:20230614T183000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-716@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Anna Dawid (Flatiron Institute)\n\nRecently\, machin
e learning has become a powerful tool for detecting quantum phases. While
the sole information about the presence of transition is valuable\, the la
ck of interpretability and knowledge on the detected order parameter preve
nts this tool from becoming a customary element of a physicist's toolbox.
Here\, we report designing a special convolutional neural network with ada
ptive kernels\, which allows for fully interpretable and unsupervised dete
ction of local order parameters out of spin configurations measured in arb
itrary bases. With the proposed architecture\, we detect relevant and simp
lest order parameters for the one-dimensional transverse-field Ising model
from any combination of projective measurements in the x\, y\, or z basis
. Moreover\, we successfully tackle the bilinear-biquadratic spin-1 model
with a nontrivial nematic order. We also consider extending the proposed a
pproach to detecting topological order parameters. This work can lead to i
ntegrating machine learning methods with quantum simulators studying new e
xotic phases of matter.\n\nhttps://events.perimeterinstitute.ca/event/36/c
ontributions/716/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/716/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Talk - tbc
DTSTART:20230615T151500Z
DTEND:20230615T160000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-724@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Stefanie Czischek (University of Ottawa)\n\nhttps://
events.perimeterinstitute.ca/event/36/contributions/724/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/724/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Solving 2D quantum matter with neural quantum states
DTSTART:20230615T204500Z
DTEND:20230615T213000Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-775@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Markus Heyl (Max Planck Institute for the Physics of
Complex Systems)\n\nNeural quantum states (NQSs) have emerged as a novel
promising numerical method to solve the quantum many-body problem. However
\, it has remained a central challenge to train modern large-scale deep ne
twork architectures to desired quantum state accuracy\, which would be vit
al in utilizing the full power of NQSs and making them competitive or supe
rior to conventional numerical approaches. Here\, we propose a minimum-ste
p stochastic reconfiguration (MinSR) method that reduces the optimization
complexity by orders of magnitude while keeping similar accuracy as compar
ed to conventional stochastic reconfiguration. MinSR allows for accurate t
raining on unprecedentedly deep NQS with up to 64 layers and more than 105
parameters in the spin-1/2 Heisenberg J1-J2 models on the square lattice.
We find that this approach yields better variational energies as compared
to existing numerical results and we further observe that the accuracy of
our ground state calculations approaches different levels of machine prec
ision on modern GPU and TPU hardware. The MinSR method opens up the potent
ial to make NQS superior as compared to conventional computational methods
with the capability to address yet inaccessible regimes for two-dimension
al quantum matter in the future.\n\nhttps://events.perimeterinstitute.ca/e
vent/36/contributions/775/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/775/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Neural quantum states for simulating strongly interacting fermions
in continuous space
DTSTART:20230612T194500Z
DTEND:20230612T201500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-710@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Jannes Nys ( École Polytechnique Fédérale de Laus
anne)\n\nZOOM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81
Y3VaYVpCQT09\n\nWe introduce a novel neural quantum state architecture for
the accurate simulation of extended\, strongly interacting fermions in co
ntinuous space. The variational state is parameterized via permutation equ
ivariant message passing neural networks to transform single-particle coor
dinates to highly correlated quasi-particle coordinates. We show the versa
tility and accuracy of this Ansatz by simulating the ground-state of the 3
D homogeneous electron gas at different densities and system sizes. Our mo
del respects basic symmetries of the Hamiltonian\, such as continuous tran
slation symmetries. We compare our ground-state energies to results obtain
ed by different state-of-the-art NQS Ansaetze for continuous space\, as we
ll as to different quantum chemistry methods. We obtain better or comparab
le ground-state energies\, while using orders of magnitudes less variation
al parameters and optimization steps. We investigate its capability of ide
ntifying and representing different phases of matter without imposing any
structural bias toward a given phase. We scale up to system sizes of N=128
particles\, opening the door for future work on finite-size extrapolation
s to the thermodynamic limit.\n\nhttps://events.perimeterinstitute.ca/even
t/36/contributions/710/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/710/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Quantum-enhanced reinforcement learning
DTSTART:20230614T153000Z
DTEND:20230614T161500Z
DTSTAMP:20230922T223300Z
UID:indico-contribution-718@events.perimeterinstitute.ca
DESCRIPTION:Speakers: Valeria Saggio (Massachusetts Institute of Technolog
y)\n\nThe field of artificial intelligence (AI) has experienced major deve
lopments over the last decade. Within AI\, of particular interest is the p
aradigm of reinforcement learning (RL)\, where autonomous agents learn to
accomplish a given task via feedback exchange with the world they are plac
ed in\, called an environment. Thanks to impressive advances in quantum te
chnologies\, the idea of using quantum physics to boost the performance of
RL agents has been recently drawing the attention of many scientists. In
my talk I will focus on the bridge between RL and quantum mechanics\, and
show how RL has proven amenable to quantum enhancements. I will provide an
overview of the most recent results — for example\, the development of
agents deciding faster on their next move [1]— and I will then focus on
how the learning time of an agent can be reduced using quantum physics. I
will show that such a reduction can be achieved and quantified only if the
agent and the environment can also interact quantumly\, that is\, if they
can communicate via a quantum channel [2]. This idea has been implemented
on a quantum platform that makes use of single photons as information car
riers. The achieved speed-up in the agent’s learning time\, compared to
the fully classical picture\, confirms the potential of quantum technologi
es for future RL applications.\n\n[1] Sriarunothai\, T. et al. Quantum Sci
ence and Technology 4\, 015014 (2018).\n[2] Saggio\, V. et al. Nature 591\
, 229–233 (2021).\n\nhttps://events.perimeterinstitute.ca/event/36/contr
ibutions/718/
LOCATION:PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)
URL:https://events.perimeterinstitute.ca/event/36/contributions/718/
END:VEVENT
END:VCALENDAR