Learning quantum physics from experiments with Bayesian ... · Outline •Quantum Computers a...
Transcript of Learning quantum physics from experiments with Bayesian ... · Outline •Quantum Computers a...
Raffaele [email protected]
Learning quantum physics from experiments with Bayesian inference and quantum computers
Outline
• Quantum Computers a (very) short introduction
• Quantum Hamiltonian Learning (QHL) -> Learning parameters of a model.
• From learning parameters to learning models…
• Wiebe et al., Hamiltonian Learning and Certification Using Quantum Resources. Phys. Rev. Lett. 112, (2014)
• Wang et al., Experimental quantum Hamiltonian learning - Nature Physics 1, 149 (2017)
• Santagati et al., Magnetic-field-learning using a single electronic spin in diamond… - Phys. Rev. X (2019)
• Gentile et al., Characterising quantum systems with Bayesian inference – manuscript in preparation (2019)
• Flynn et al., Exploring acyclic graphs for the study of quantum systems – manuscript in preparation (2019)
Quantum physics
EINSTEIN, N. ROSEN and B. PODOLSKY, Phys. Rev. 47, 777 (1935) D. R. INGLIS, Rev. Mod. Phys. 33, 1 (1961)
Entanglement
Quantum mechanics although counterintuitive is correct in its predictions
Defines probability
Schrödinger’s cat
PROBABILISTIC MEASUREMENT OUTCOMES
Superposition
Quantum superposition of 2 states:
Quantum Simulation
Quantum systems are hard to simulate.
If we study a system comprising N particles with two basis states and wewant to store their quantum state on a classical computer using an 8 bits (1 byte) resolution.
2 p 4 bytes
1 p 2 bytes
3 p 8 bytes
200 p ~ 1.6 1060 bytes
The total amount of memory required grows exponentially with the value of N.
Mem
ory
Number of particles
Total memory available on earth today is ~ 5.21 1021 bytes
Quantum Simulation
Quantum systems are hard to simulate.
Feynman - Simulating Physics with Computers International Journal of Theoretical Physics, VoL 21, 6-7, 1982
Mem
ory
Number of particles
We need a new kind of computer to simulatequantum systems efficiently.
0
1
Bit Quantum bit
Measurement outcomes are probabilistic
Probability of 0
Probability of 1
Quantum computers and what they are good at
Factoring Numbers (Shor’s algorithm) Quantum Machine Learning
Quantum Simulation Optimization
Exponential speed-upPoly and exponential speed-up
Exponential speed-ups Poly speed-ups
Ashley Montanaro Quantum Information 2, 15023, 2016
Quantum algorithm zoohttps://math.nist.gov/quantum/zoo/
Quantum computing funding landscape
Topological• UK -> 400 million £
• EU -> 1.6 billion €
• GE -> 600 million €
And many more….
Investments inQuantum technologies
Other Solid state
Photonics
Superconducting
Exponential scaling of
required classical
resources
31
Bayesian approach
(similar to Bayesian phase estim.)
Ø Choose experiment (i.e. evol. time ) given .
Ø Obtain data from the system.
Ø Use the quantum simulator to calculate the
likelihood functions .
Ø Update the prior distribution using Bayes’ rule
The information is encoded in a prior distribution
• Near-optimal choice: .
• Simulated Monte-Carlo approximation used to make the Bayesian update practical.
Quantum Hamiltonian LearningWhat is a Hamiltonian operator?
Quantum System Model of the system (H)
with parameters
Quantum Hamiltonian Learning
QHL aims to find efficiently the set of parameters which best describe the dynamic of the system.
Mohseni et al. Phys. Rev. A 77, 032322 (2008)Hentschel and Sanders Phys. Rev. Lett. 104, 063603 (2010)Da Silva et al. Phys. Rev. Lett. 107, 210404 (2011)Wiebe et al. Phys. Rev. Lett. 112, 190501 (2014)Wiebe et al. Phys. Rev. A 89, 042314 (2014)
1. From choose an
experiment (e.g. )
2. Perform experiment on
System and obtain
outcome E
Phys. Rev. Lett. 112, 190501 (2014)
3. Calculate likelihoods
using quantum simulator
4. Update the prior distribution
using Bayes’ rule:
Nature Physics 13, 551-555 (2017)
31
Bayesian approach
(similar to Bayesian phase estim.)
Ø Choose experiment (i.e. evol. time ) given .
Ø Obtain data from the system.
Ø Use the quantum simulator to calculate the
likelihood functions .
Ø Update the prior distribution using Bayes’ rule
The information is encoded in a prior distribution
• Near-optimal choice: .
• Simulated Monte-Carlo approximation used to make the Bayesian update practical.
Quantum Likelihood Estimation
Experimental set-up
Nature Physics 13, 551-555 (2017)
Hamiltonian: Where we want to
estimate the frequency
of oscillations
c
Experimental results
frequency inferred with QLE:
Rescaling
Nature Physics 13, 551-555 (2017)
First part: QHL Take home message
• Finding the parameter of H in a scalable way
• We learned only parameters not models
• Can we use similar techniques to help us
improve models?
31
Bayesian approach
(similar to Bayesian phase estim.)
Ø Choose experiment (i.e. evol. time ) given .
Ø Obtain data from the system.
Ø Use the quantum simulator to calculate the
likelihood functions .
Ø Update the prior distribution using Bayes’ rule
The information is encoded in a prior distribution
• Near-optimal choice: .
• Simulated Monte-Carlo approximation used to make the Bayesian update practical.
• Gentile et al., Experimental Quantum Model Learning –manuscript in preparation (2019)
• Flynn et al., Exploring acyclic graphs for the study of quantum systems – manuscript in preparation (2019)
Methods to model physical systems with ML
Crutchfield and McNamara, Complex Syst. 1, 417 (1987)Schmidt and Lipson, Science 324, 81 (2009)Torlai and Melko Phys Rev B 94, 165134 (2016)Bairey, Arad, and Lindner PRL 122, 020504 (2019)Iten, et al., arXiv preprint arXiv:1807.10300 (2018)
Devise an automatic strategy to generate and select models by performing a search on a tree for the Hamiltonian model which best approximate the system dynamics.
QMLA prior
Quantum Model Learning
Explore and prune
for in DAG:
QHL update Consolidate
..
.
C. Granade, C. Ferrie, I. Hincks, S. Casagrande, T. Alexander, J. Gross, M. Kononenko, and Y. Sanders, Qinfer: Statistical Inference Software for Quantum Applications, Quantum 1, 5 (2017).
The protocol compares in pairs (Hi,Hj) all models within the
same layer using the Bayes factor
where
Learning models of quantum system:NV centre in diamond
Electron spin interacting with a Carbon nuclear spin
We want to learn the basics of the
interaction between the spin in the NV centre in diamond and
the closest 13C nuclear spin through Hahn echo measurements.
Hyperfine gyromagnetic tensors
Electron gyromagnetic tensors
cc
Results – NV-centre model learning
Conclusions
Wang et al. Nature Physics 13, 551-555 (2017)Santagati et al. Phys. Rev. X 9, 021019 (2019)Gentile et al., man. in prep. (2019)Flynn et al., man. in prep. (2019)
• Quantum computers• Statistical inference (QHL) for parameters in H• Quantum model learning to learn the models of a quantum system• From the experiments we find models compatible with theoretical
developments.
Backup slides
Results – NV-centre model learning
Other models performance
Experiment measurements
c
We measure the traced-out state i.e. Measure only the electron and not theother interacting particles.
It is a bit like studying the property of an iceberg by looking only at the tip.
electron
nucleus
What quantum computers are good at and what they are not?
Quantum computers don’t compute all the answers in parallel
Quantum computers are not faster than classical computers at every task
Quantum computers can give exponential and polynomial speed ups at specific tasks.
G Wendin 2017 Rep. Prog. Phys. 80 106001
https://www.smbc-comics.com/comic/the-talk-3
Experiment – NV-centre model learning
Experimental results: Variance
Variance:
SATURATION = Bad quantum simulator or bad model!
Wang et al. Nature Physics 13, 551-555 (2017)
Model I:
Model II:
From QHL to comparing models
Bayes Factor:
Updated value (for model II, including
chirping) of Norm of covariance matrix:
Old Variance with model I:
Model I
Model II
0 10 20 30 40 5010- 6
10- 5
10- 4
10- 3
10- 2
10- 1
1
10- 6
10- 5
10- 4
10- 3
10- 2
10- 1
1
Experiment Number
2(
)
From quantum simulation to qubits
A new theory of information based on quantum mechanics.
0
1
Bit Quantum bit
Measurement
Measurement outcomes are probabilistic
Probability of 0
Probability of 1
Quantum Computation and Quantum Information - M. Nielsen and I.L. Chuang - Cambridge
Experiment measurements
c
electron
nucleus
Model1 Model2
BF(H2; H1) = 1010
Estimating the number of spins
Gali, Fyta, and Kaxiras Ab initio supercell calculations on nitrogen-vacancy center in diamond:Electronic structure and hyperfine tensors. PHYSICAL REVIEW B 77, 155206 (2008)
Galli - Identification of individual 13C isotopes of nitrogen-vacancy center in diamond by combining thepolarization studies of nuclear spins and first-principles calculations. PHYSICAL REVIEW B 80, 241204 (2009)
Sequential Monte Carlo
Phys. Rev. Lett. 112, 190501 (2014)
2p W
2p W
P(W
)P
(W)
2 spins model – Expectation values
Simulation – NV-centre model learning
Measuring the traced out state state
QMLA prior
Model Learning
for in DAG:
QHL update
Learn from data
each model
Consolidate
The protocol compares in pairs (Hi,Hj) all models within the
same layer using the Bayes factor
where
Explore and prune
Generate new models till
n qubits allows or generate new layer.
.
.
The problem is NP Hard… but
A Bayesian analysis of decision-tree learning has shown that near-optimal solutions can be found in polynomial time.
Muggleton, S. Page, D.: A learnability model of universal representations and its application to top-down induction of decision trees. In K. Furukawa, D. Michie, & S. Muggleton(eds.) Machine Intelligence 15, Oxford University Press, page 16 (1999)
Dynamics examples with BF and PGH times