Matthias Troyer

The Quantum Edge of AI

Antikythera mechanism

100BC

Babbage’s difference engine

1822 (proposed)

ENIAC

1946

Taihu Light

2015

Quantum computer

Next

Developing the full quantum stack

Quantum bits and quantum gates

Classical control

Runtime operating system

Quantum compilers

Quantum and classical libraries

Quantum algorithms

Quantum applications from chemistry to machine learning

Quantum fab

One quantum bit: true randomness

|| ||

0 1photo detectors

photon source

semi-transparent mirror

1. Photon source emits a photon

2. Photon hits semi-transparent mirror?

Yogi Berra

“When You Come to a Fork in the Road, Take It “

One quantum bit: true randomness

|| ||

0 1photo detectors

photon source

semi-transparent mirror

0

1. Photon source emits a photon

2. Photon hits semi-transparent mirror?

3. Photon follows both paths

4. The photo detectors see the photon only in one place: a random bit

Classical and quantum bits

0

1

|0⟩

|1⟩

Classical bit: 0 or 1

or

Schrödinger’s cat paradox

cat = dead + alive

flask = closed + broken

ψ = 0 + 1

hammer = ↓ + ↑

Topological quantum bits: towards a disruptive breakthrough

Split or electrons into two “Majorana” particles and separate them0 1

||Matthias Troyer

Simulating quantum computers on classical computers

Simulating a quantum gate acting on N qubits needs O(2N) memory and operations

11

Qubits Memory Time for one operation

10 16 kByte microseconds on a smartwatch

20 16 MByte milliseconds on smartphone

30 16 GByte seconds on laptop

40 16 TByte seconds on cluster

50 16 PByte minutes on top supercomputers?

Simulating a quantum gate acting on N qubits needs O(2N) memory and operations

250 qubits … more states than atoms in the visible universe

Quantum computers enormously accelerates some calculations by operating on all inputs simultaneously

Initial applications

Nitrogen fixation

Materials science

Carbon capture

Machine learning

100-200 qubits

100-200 qubits

100s-1000s qubits

1000s qubits

AI Edge for Quantum ScienceBig surprise in quantum physics:

Restricted Boltzmann machine learns quantum states as well as the best specialized algorithms

Carleo and TroyerScience 355, 602 (2017)

W (1) W (2) W (3) W (4)

W (5) W (6) W (7) W (8)

W (9) W (10) W (11) W (12)

W (13) W (14) W (15) W (16)

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Heisenberg 2D

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Visible Layer

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1

0.0 0.5 1.0 1.5 2.0t

0.0

0.2

0.4

0.6

0.8

h�xi(

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↵ = 2

↵ = 4

Exact

0.0 0.5 1.0 1.5 2.0t

0.00

0.05

0.10

0.15

0.20

0.25

0.30

r2(t

)

AI Edge for Tuning Quantum ComputersBad/notideal OK

J. Darulova N. Wiebe A. Bocharov K. Svore

M. Troyer D. Wecker B. Bobrov C. McBride

Quantum Edge of AI

Quantum Training for AI

Quantum-accelerated sampling

Quantum models for AI

Quantum Boltzmann machines

See talk by Dr. Nathan Wiebe in this session

Quantum-inspired Edge for AIEarly return on investment: Mimicking quantum optimizers gives breakthroughs in the development of classical algorithms

See talk by Prof. Helmut Katzgraber in this session

Quantum computers offer computational capabilities beyond that of any classical computer

The quantum edge of AI

AI Quantum computers

helps us build

Quantum AIQuantum-inspired AI