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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
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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
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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)
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W (13) W (14) W (15) W (16)
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↵ = 2
↵ = 4
Exact
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0.05
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0.25
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r2(t
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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