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Distributed Systems in the Post-Moore Era
Dr. Vincenzo De Maio
vincenzo@ec.tuwien.ac.at
FWF START Prize 2015
http://rucon.ec.tuwien.ac.at/
TEWI KOLLOQUIUM
Klagenfurt, 14th March 2023
The IoT revolution
• “How Much Data Do We
Create Every Day?” – Bernard
Marr, Forbes, 21th May 2018
• Smart devices produce 5
quintillion (5 × 1018
) bytes of
data daily.
• In 5 years, we can expect the
number of these gadgets to be
more than 50 billion!
• 90 ZB (90 × 1021
bytes) of
this data will be from IoT
devices in 2025
• Response time?
2
SMART AGRICULTURE
E-HEALTH
FITNESS TRACKING
TRAFFIC SAFETY
Vincenzo De Maio - Distributed Systems in the Post-Moore Era
Traffic Safety
• InTraSafEd5G Project
• City of Vienna 5G Challenge
• http://intrasafed.ec.tuwien.ac.at/
• Ensure traffic safety with the
combination of IoT and Edge AI
• Focus on near real-time performance
• Need to consider users’ reaction time…
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 3
Cloud/Edge Offloading
4
Computationally Intensive Tasks
App modeled as DAG
Josip Zilic, Vincenzo de Maio, Atakan Aral, Ivona Brandic
Edge offloading for microservice architectures. EdgeSys@EuroSys 2022: 1-6
RUCON LiveLab Testbed
EDGE CLOUD
Vincenzo De Maio - Distributed Systems in the Post-Moore Era
Edge infrastructure for traffic safety
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 5
Example setup and deployment of edge nodes in the
context of InTraSafEd5G project.
Ivan Lujic, Vincenzo De Maio, Klaus Pollhammer, Ivan Bodrozic, Josip Lasic, Ivona Brandic:
Increasing Traffic Safety with Real-Time Edge Analytics and 5G. EdgeSys@EuroSys 2021: 19-24
Main Challenges
6
Computationaly Intensive Tasks
App modeled as DAG
RUCON LiveLab Testbed
PLACEMENT
PROVISIONING
RELIABILITY
ENERGY
TRUST
Vincenzo De Maio - Distributed Systems in the Post-Moore Era
Post-Moore’s Law Computing
• To improve performance of current
architectures, we need to reduce component
size…
• Component size: hitting the atom limit!
• Time to consider alternative (post-Moore’s Law)
forms of computing
• Quantum mechanics: interactions at the subatomic level
• Quantum Computing: development of computer based on the principles of quantum theory
• Qubits, superposition, entanglement…
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 7
Known Quantum Speedup
• Grover’s algorithm: 𝑂( 𝑛) vs 𝑂(𝑛)
• Shor’s algorithm: Polynomial vs Exponential
• Quantum ML
• Bayesian Inference: quadratic
• SVM: exponential
• Reinforcement Learning: quadratic
• “Machine Learning: Quantum vs Classical”, Tariq M. Khan et al., IEEE Access,
November 2020
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 8
Quantum Fundamentals
Qubits
• |𝛙⟩ = 𝛼0 0 + 𝛼1 1 ,
𝛼0, 𝛼1 ∈ ℂ
𝛼0
2
+ |𝛼1|2
= 1
• 𝟎 = 𝟏
𝟎
BLOCH SPHERE
• |𝛙⟩ = 𝛼0 0 + 𝑒𝑖φ
𝛼1 1 , 𝛼0, 𝛼1, φ ∈ ℝ
• θ, φ: spherical coordinates with radius = 1
• |𝛙⟩ = cos
𝜃
2
|0⟩ + 𝑒𝑖𝜑𝑠𝑖𝑛
𝜃
2
|1⟩
Probability of |𝛙⟩ = 0
Probability of |𝛙⟩ = 1
Quantum Computation
• Quantum register: combination of n qubits
• Classical register: 1 out of 2𝑛 values at a time
• Quantum register: 2𝑛
values AT THE SAME TIME. (Quantum Parallelism)
• Measurement returns a state 𝑖 with probability 𝛼𝑖
2
• Repeated execution
• Most probable result → final result of the algorithm
• Quantum algorithms goals:
• Achieve a distribution such that
• One correct result appears with high probability
• More than one correct result appear with high and similar probability
Example of single qubit operation
• Manipulation of Qubit is done by using specific operators (gates)
• 𝑋 =
0 1
1 0
, Y =
0 −𝑖
𝑖 0
, 𝑍 =
1 0
0 −1
(Pauli gates)
• 0 ∗ 𝑋 = 0
0 1
1 0
= 1
0
0 1
1 0
• 0∗1+1∗0
1∗1+0∗0
= 0
1
𝑞0 X
+
State of the art of Quantum Systems
• Noisy Intermediate Scale Quantum (NISQ) architectures
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 12
IBM Q Quantum System
at Semicon West
Quantum state
preparation
Measurement
Classic hardware
• Translation from classic input in
quantum state
• Quantum compilation (from source
code to circuit)
• Error correction
• Limited number of qubits available
• Higher execution time with respect to classic equivalent
Measurement
• Schrödinger’s cat
• Measuring the value of a qubit collapses the value in 0 or 1
respectively with probability 𝜶𝟎
𝟐 and |𝜶𝟏|𝟐
• Wavefunction collapse
• Different measurements -> different results!
Notes
• No-Cloning Theorem:
• It is IMPOSSIBLE to clone a qubit.
• Quantum Entanglement
• Bell’s state:
1
2
(|00⟩ + |11⟩)
• (EPR paradox)
• Computation not involving entangled qubits can be performed
with same efficiency on classical computing
• To achieve exponential quantum speedup, you MUST exploit
entanglement (Jozsa/Linden 2003)
• Applications to quantum teleportation / communication
Quantum Error Correction
• Challenges
• Redundancy doesn’t work (no cloning)
• Bit/phase flips
• Wavefunction collapse
• Main research lines
• Quantum Redundancy (expansion of Hilbert space)
• Stabilizer codes
• Surface codes
“Quantum Error Correction: An Introductory Guide”, Joschka Roffe
Hybrid Quantum Systems
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 16
Quantum tasks
Classic
tasks
Workflow
Management
System
Scientific
Workflow
User
Quantum
machine
Classic
HPC
Mapper
Quantum computing for Distributed
Scientific Applications
• Data intensive
• Natural 3D modelling of scientific problems
• N-body
• Particle physics
• Many computation can benefit from quantum speedup
• Approximate optimization
• Eigenvalue calculation
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 17
IDEA: Accelerate specific tasks by
means of quantum hardware
A Molecular Dynamics Use Case
• Analyzing trajectories of backbone 𝐶𝛼 atoms of amino-acids
segments
• Identifying collective variables capturing molecular motions
in a region of interest
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 18
Atom
segments
𝐷 =
0 ⋯ 𝐷𝐼𝐽
⋮ ⋱ ⋮
𝐷𝐼𝐽
𝑇
⋯ 0
Distance matrix
Read
trajectory file
User input
𝐷𝑣 = 𝜆𝑣
Find maximum
eigenvalue
Which of these can exploit
quantum advantage?
Application decomposition
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 19
Atom
segments
0 ⋯ 𝐷𝐼𝐽
⋮ ⋱ ⋮
𝐷𝐼𝐽
𝑇
⋯ 0
Distance matrix
Read
trajectory file
User input
𝐷𝑣 = 𝜆𝑣
Find largest
eigenvalue
End
Device
Classic
HPC
Quantum
machine
Distance Matrix Initialization
• CSWAP TEST
• Input: 𝜑 , |𝜓⟩, quantum states
• Outputs an estimate of | 𝜓 𝜑 |2
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 20
Example calculation of interatomic
distance
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 21
Select amino-
acids
segments
𝑑00 ⋯ 𝑑02
⋮ ⋱ ⋮
𝑑20 ⋯ 𝑑22
𝑎2 𝑎1 𝑎0
𝑏2 𝑏1 𝑏0
Amplitude
encoding
CSWAP TEST
𝜑
|𝜓⟩
Hybrid Testbed
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 22
Workflow
Management
System
User
Molecular
Dynamics
Workflow
Classic HPC
ibm_lagos
ibmq_jakarta ibmq_lima
ibmq_manila
Results for interatomic distance
• 100 pairs of random generated matrices
• Segment sizes: 1,2,4,8,16
• MSE between classic and quantum result
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 23
Node ID Average MSE Variance
ibmq_manila 0.2317 0.000199
ibmq_santiago 0.2832 0.000264
ibm_lagos 0.2249 0.000190
ibm_jakarta 0.2037 0.000149
Calculation of eigenvalues
• Variational Quantum Eigensolver (VQE)
• In quantum mechanics, a system of particles can be described as a
Hamiltonian representing the energy of the system.
• Finding minimum eigenvalue ≡ Finding Hamiltonian ground state
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 24
𝐻
Ψ(Θ)
Calculate expectation
value 𝜆𝜃 =
⟨𝜓 Θ 𝐻 𝜓 Θ ⟩
𝜆𝑚𝑖𝑛 ≤ 𝜆𝜃
𝐶(Θ) 𝑚𝑖𝑛𝐶(Θ)
Molecular system
Parametrized
quantum circuit
Mapping of VQE
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 25
Classic
Machine
Quantum
Machine
𝐻
𝜆𝜃 = ⟨𝜓 Θ 𝐻 𝜓 Θ ⟩
Θ
𝐶(Θ)
Optimizer
Hyperparameter setting in VQE
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 26
Optimizer?
Hardware?
PQC?
Cost
function?
Termination
condition?
Hamiltonian?
Parametrized Quantum Circuits
• Standard “well-known” circuits
• Entanglement
• Repetitions
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 27
SU2
Pauli Two Design
Real Amplitudes
Excitation Preserving
Optimizers
• Optimizers affect convergence rate and error
• We select three optimizers for our evaluation
• COBYLA
• SPSA
• GRADIENT DESCENT
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 28
PQC vs Quantum Hardware
• Width: amount of qubits required to represent input matrix (𝑛 ∙ 𝑛 =
log 𝑛)
• Error due to decoherence and quantum noise
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 29
PQC vs Entanglement
• Entanglement:
• LINEAR: 𝑞0 → 𝑞1 → … → 𝑞𝑛
• FULL: 𝑞0 → 𝑞1, 𝑞2, … , 𝑞𝑛 , 𝑞1 → 𝑞0, 𝑞2, … , 𝑞𝑛 , … , 𝑞𝑛 → 𝑞0, 𝑞1, … , 𝑞𝑛−1
• SCA: 𝑞0 → 𝑞2, 𝑞4 … , 𝑞𝑛
• CIRCULAR: 𝑞0 → 𝑞1 → … → 𝑞𝑛 → 𝑞0
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 30
PQC vs Repetitions
• Error due to decoherence and quantum noise increases with respect to
repetitions
• Error correction?
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 31
Results
• VQE calculation using different hyperparameters
• Benchmarking data collected on different machines
• Hyperparameters’ optimization is used to identify best hyperparameters set
for a target metric 𝑚, Π𝑚
∗
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 32
Remarks
• We provided a first step in the design of scientific
applications for hybrid classic/quantum systems
• Identified quantum-suitable parts
• Provided an example implementation
• Future work
• Consider different use cases
• Investigating impact of different quantum hardware
• (semiconductors, ion-traps, d-wave…)
• Error correction methods
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 33
Current Work
34
if not backend.configuration().simulator:
trans_dict = {'layout_method': 'sabre',
'routing_method': 'sabre'}
trans_circ = transpile(ansatz, backend,
optimization_level=3, **trans_dict)
vqe_inputs = {
'ansatz': trans_circ,
'shots': 8192,
'measurement_error_mitigation': True
}
options = {
'backend_name': backend.name(),
}
job = provider.runtime.run(program_id='vqe',
inputs=vqe_inputs, options=options)
MD Simulation
Classic Code Quantum Circuit
TRANSPILE
Vincenzo De Maio - Distributed Systems in the Post-Moore Era
DATA
• Vincenzo De Maio, Atakan Aral, Ivona Brandic: A Roadmap To Post-Moore Era for Distributed Systems. ACM ApPLIED@PODC
2022: 30-34
• Sandeep Suresh Cranganore, Vincenzo De Maio, Tu Mai Anh Do, Ivona Brandic, Ewa Deelman: Molecular Dynamics Workflow
Decomposition for Hybrid Classic/Quantum Systems. IEEE eScience 2022
Future development
• Integration of different applications
• Streaming data encoding
• Quantum software engineering
• …
Vincenzo De Maio - Distributed Systems in the Post-Moore Era 35
Questions?
Dr. Vincenzo De Maio
vincenzo@ec.tuwien.ac.at

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Distributed Systems in the Post-Moore Era.pptx

  • 1. Distributed Systems in the Post-Moore Era Dr. Vincenzo De Maio vincenzo@ec.tuwien.ac.at FWF START Prize 2015 http://rucon.ec.tuwien.ac.at/ TEWI KOLLOQUIUM Klagenfurt, 14th March 2023
  • 2. The IoT revolution • “How Much Data Do We Create Every Day?” – Bernard Marr, Forbes, 21th May 2018 • Smart devices produce 5 quintillion (5 × 1018 ) bytes of data daily. • In 5 years, we can expect the number of these gadgets to be more than 50 billion! • 90 ZB (90 × 1021 bytes) of this data will be from IoT devices in 2025 • Response time? 2 SMART AGRICULTURE E-HEALTH FITNESS TRACKING TRAFFIC SAFETY Vincenzo De Maio - Distributed Systems in the Post-Moore Era
  • 3. Traffic Safety • InTraSafEd5G Project • City of Vienna 5G Challenge • http://intrasafed.ec.tuwien.ac.at/ • Ensure traffic safety with the combination of IoT and Edge AI • Focus on near real-time performance • Need to consider users’ reaction time… Vincenzo De Maio - Distributed Systems in the Post-Moore Era 3
  • 4. Cloud/Edge Offloading 4 Computationally Intensive Tasks App modeled as DAG Josip Zilic, Vincenzo de Maio, Atakan Aral, Ivona Brandic Edge offloading for microservice architectures. EdgeSys@EuroSys 2022: 1-6 RUCON LiveLab Testbed EDGE CLOUD Vincenzo De Maio - Distributed Systems in the Post-Moore Era
  • 5. Edge infrastructure for traffic safety Vincenzo De Maio - Distributed Systems in the Post-Moore Era 5 Example setup and deployment of edge nodes in the context of InTraSafEd5G project. Ivan Lujic, Vincenzo De Maio, Klaus Pollhammer, Ivan Bodrozic, Josip Lasic, Ivona Brandic: Increasing Traffic Safety with Real-Time Edge Analytics and 5G. EdgeSys@EuroSys 2021: 19-24
  • 6. Main Challenges 6 Computationaly Intensive Tasks App modeled as DAG RUCON LiveLab Testbed PLACEMENT PROVISIONING RELIABILITY ENERGY TRUST Vincenzo De Maio - Distributed Systems in the Post-Moore Era
  • 7. Post-Moore’s Law Computing • To improve performance of current architectures, we need to reduce component size… • Component size: hitting the atom limit! • Time to consider alternative (post-Moore’s Law) forms of computing • Quantum mechanics: interactions at the subatomic level • Quantum Computing: development of computer based on the principles of quantum theory • Qubits, superposition, entanglement… Vincenzo De Maio - Distributed Systems in the Post-Moore Era 7
  • 8. Known Quantum Speedup • Grover’s algorithm: 𝑂( 𝑛) vs 𝑂(𝑛) • Shor’s algorithm: Polynomial vs Exponential • Quantum ML • Bayesian Inference: quadratic • SVM: exponential • Reinforcement Learning: quadratic • “Machine Learning: Quantum vs Classical”, Tariq M. Khan et al., IEEE Access, November 2020 Vincenzo De Maio - Distributed Systems in the Post-Moore Era 8
  • 9. Quantum Fundamentals Qubits • |𝛙⟩ = 𝛼0 0 + 𝛼1 1 , 𝛼0, 𝛼1 ∈ ℂ 𝛼0 2 + |𝛼1|2 = 1 • 𝟎 = 𝟏 𝟎 BLOCH SPHERE • |𝛙⟩ = 𝛼0 0 + 𝑒𝑖φ 𝛼1 1 , 𝛼0, 𝛼1, φ ∈ ℝ • θ, φ: spherical coordinates with radius = 1 • |𝛙⟩ = cos 𝜃 2 |0⟩ + 𝑒𝑖𝜑𝑠𝑖𝑛 𝜃 2 |1⟩ Probability of |𝛙⟩ = 0 Probability of |𝛙⟩ = 1
  • 10. Quantum Computation • Quantum register: combination of n qubits • Classical register: 1 out of 2𝑛 values at a time • Quantum register: 2𝑛 values AT THE SAME TIME. (Quantum Parallelism) • Measurement returns a state 𝑖 with probability 𝛼𝑖 2 • Repeated execution • Most probable result → final result of the algorithm • Quantum algorithms goals: • Achieve a distribution such that • One correct result appears with high probability • More than one correct result appear with high and similar probability
  • 11. Example of single qubit operation • Manipulation of Qubit is done by using specific operators (gates) • 𝑋 = 0 1 1 0 , Y = 0 −𝑖 𝑖 0 , 𝑍 = 1 0 0 −1 (Pauli gates) • 0 ∗ 𝑋 = 0 0 1 1 0 = 1 0 0 1 1 0 • 0∗1+1∗0 1∗1+0∗0 = 0 1 𝑞0 X +
  • 12. State of the art of Quantum Systems • Noisy Intermediate Scale Quantum (NISQ) architectures Vincenzo De Maio - Distributed Systems in the Post-Moore Era 12 IBM Q Quantum System at Semicon West Quantum state preparation Measurement Classic hardware • Translation from classic input in quantum state • Quantum compilation (from source code to circuit) • Error correction • Limited number of qubits available • Higher execution time with respect to classic equivalent
  • 13. Measurement • Schrödinger’s cat • Measuring the value of a qubit collapses the value in 0 or 1 respectively with probability 𝜶𝟎 𝟐 and |𝜶𝟏|𝟐 • Wavefunction collapse • Different measurements -> different results!
  • 14. Notes • No-Cloning Theorem: • It is IMPOSSIBLE to clone a qubit. • Quantum Entanglement • Bell’s state: 1 2 (|00⟩ + |11⟩) • (EPR paradox) • Computation not involving entangled qubits can be performed with same efficiency on classical computing • To achieve exponential quantum speedup, you MUST exploit entanglement (Jozsa/Linden 2003) • Applications to quantum teleportation / communication
  • 15. Quantum Error Correction • Challenges • Redundancy doesn’t work (no cloning) • Bit/phase flips • Wavefunction collapse • Main research lines • Quantum Redundancy (expansion of Hilbert space) • Stabilizer codes • Surface codes “Quantum Error Correction: An Introductory Guide”, Joschka Roffe
  • 16. Hybrid Quantum Systems Vincenzo De Maio - Distributed Systems in the Post-Moore Era 16 Quantum tasks Classic tasks Workflow Management System Scientific Workflow User Quantum machine Classic HPC Mapper
  • 17. Quantum computing for Distributed Scientific Applications • Data intensive • Natural 3D modelling of scientific problems • N-body • Particle physics • Many computation can benefit from quantum speedup • Approximate optimization • Eigenvalue calculation Vincenzo De Maio - Distributed Systems in the Post-Moore Era 17 IDEA: Accelerate specific tasks by means of quantum hardware
  • 18. A Molecular Dynamics Use Case • Analyzing trajectories of backbone 𝐶𝛼 atoms of amino-acids segments • Identifying collective variables capturing molecular motions in a region of interest Vincenzo De Maio - Distributed Systems in the Post-Moore Era 18 Atom segments 𝐷 = 0 ⋯ 𝐷𝐼𝐽 ⋮ ⋱ ⋮ 𝐷𝐼𝐽 𝑇 ⋯ 0 Distance matrix Read trajectory file User input 𝐷𝑣 = 𝜆𝑣 Find maximum eigenvalue Which of these can exploit quantum advantage?
  • 19. Application decomposition Vincenzo De Maio - Distributed Systems in the Post-Moore Era 19 Atom segments 0 ⋯ 𝐷𝐼𝐽 ⋮ ⋱ ⋮ 𝐷𝐼𝐽 𝑇 ⋯ 0 Distance matrix Read trajectory file User input 𝐷𝑣 = 𝜆𝑣 Find largest eigenvalue End Device Classic HPC Quantum machine
  • 20. Distance Matrix Initialization • CSWAP TEST • Input: 𝜑 , |𝜓⟩, quantum states • Outputs an estimate of | 𝜓 𝜑 |2 Vincenzo De Maio - Distributed Systems in the Post-Moore Era 20
  • 21. Example calculation of interatomic distance Vincenzo De Maio - Distributed Systems in the Post-Moore Era 21 Select amino- acids segments 𝑑00 ⋯ 𝑑02 ⋮ ⋱ ⋮ 𝑑20 ⋯ 𝑑22 𝑎2 𝑎1 𝑎0 𝑏2 𝑏1 𝑏0 Amplitude encoding CSWAP TEST 𝜑 |𝜓⟩
  • 22. Hybrid Testbed Vincenzo De Maio - Distributed Systems in the Post-Moore Era 22 Workflow Management System User Molecular Dynamics Workflow Classic HPC ibm_lagos ibmq_jakarta ibmq_lima ibmq_manila
  • 23. Results for interatomic distance • 100 pairs of random generated matrices • Segment sizes: 1,2,4,8,16 • MSE between classic and quantum result Vincenzo De Maio - Distributed Systems in the Post-Moore Era 23 Node ID Average MSE Variance ibmq_manila 0.2317 0.000199 ibmq_santiago 0.2832 0.000264 ibm_lagos 0.2249 0.000190 ibm_jakarta 0.2037 0.000149
  • 24. Calculation of eigenvalues • Variational Quantum Eigensolver (VQE) • In quantum mechanics, a system of particles can be described as a Hamiltonian representing the energy of the system. • Finding minimum eigenvalue ≡ Finding Hamiltonian ground state Vincenzo De Maio - Distributed Systems in the Post-Moore Era 24 𝐻 Ψ(Θ) Calculate expectation value 𝜆𝜃 = ⟨𝜓 Θ 𝐻 𝜓 Θ ⟩ 𝜆𝑚𝑖𝑛 ≤ 𝜆𝜃 𝐶(Θ) 𝑚𝑖𝑛𝐶(Θ) Molecular system Parametrized quantum circuit
  • 25. Mapping of VQE Vincenzo De Maio - Distributed Systems in the Post-Moore Era 25 Classic Machine Quantum Machine 𝐻 𝜆𝜃 = ⟨𝜓 Θ 𝐻 𝜓 Θ ⟩ Θ 𝐶(Θ) Optimizer
  • 26. Hyperparameter setting in VQE Vincenzo De Maio - Distributed Systems in the Post-Moore Era 26 Optimizer? Hardware? PQC? Cost function? Termination condition? Hamiltonian?
  • 27. Parametrized Quantum Circuits • Standard “well-known” circuits • Entanglement • Repetitions Vincenzo De Maio - Distributed Systems in the Post-Moore Era 27 SU2 Pauli Two Design Real Amplitudes Excitation Preserving
  • 28. Optimizers • Optimizers affect convergence rate and error • We select three optimizers for our evaluation • COBYLA • SPSA • GRADIENT DESCENT Vincenzo De Maio - Distributed Systems in the Post-Moore Era 28
  • 29. PQC vs Quantum Hardware • Width: amount of qubits required to represent input matrix (𝑛 ∙ 𝑛 = log 𝑛) • Error due to decoherence and quantum noise Vincenzo De Maio - Distributed Systems in the Post-Moore Era 29
  • 30. PQC vs Entanglement • Entanglement: • LINEAR: 𝑞0 → 𝑞1 → … → 𝑞𝑛 • FULL: 𝑞0 → 𝑞1, 𝑞2, … , 𝑞𝑛 , 𝑞1 → 𝑞0, 𝑞2, … , 𝑞𝑛 , … , 𝑞𝑛 → 𝑞0, 𝑞1, … , 𝑞𝑛−1 • SCA: 𝑞0 → 𝑞2, 𝑞4 … , 𝑞𝑛 • CIRCULAR: 𝑞0 → 𝑞1 → … → 𝑞𝑛 → 𝑞0 Vincenzo De Maio - Distributed Systems in the Post-Moore Era 30
  • 31. PQC vs Repetitions • Error due to decoherence and quantum noise increases with respect to repetitions • Error correction? Vincenzo De Maio - Distributed Systems in the Post-Moore Era 31
  • 32. Results • VQE calculation using different hyperparameters • Benchmarking data collected on different machines • Hyperparameters’ optimization is used to identify best hyperparameters set for a target metric 𝑚, Π𝑚 ∗ Vincenzo De Maio - Distributed Systems in the Post-Moore Era 32
  • 33. Remarks • We provided a first step in the design of scientific applications for hybrid classic/quantum systems • Identified quantum-suitable parts • Provided an example implementation • Future work • Consider different use cases • Investigating impact of different quantum hardware • (semiconductors, ion-traps, d-wave…) • Error correction methods Vincenzo De Maio - Distributed Systems in the Post-Moore Era 33
  • 34. Current Work 34 if not backend.configuration().simulator: trans_dict = {'layout_method': 'sabre', 'routing_method': 'sabre'} trans_circ = transpile(ansatz, backend, optimization_level=3, **trans_dict) vqe_inputs = { 'ansatz': trans_circ, 'shots': 8192, 'measurement_error_mitigation': True } options = { 'backend_name': backend.name(), } job = provider.runtime.run(program_id='vqe', inputs=vqe_inputs, options=options) MD Simulation Classic Code Quantum Circuit TRANSPILE Vincenzo De Maio - Distributed Systems in the Post-Moore Era DATA • Vincenzo De Maio, Atakan Aral, Ivona Brandic: A Roadmap To Post-Moore Era for Distributed Systems. ACM ApPLIED@PODC 2022: 30-34 • Sandeep Suresh Cranganore, Vincenzo De Maio, Tu Mai Anh Do, Ivona Brandic, Ewa Deelman: Molecular Dynamics Workflow Decomposition for Hybrid Classic/Quantum Systems. IEEE eScience 2022
  • 35. Future development • Integration of different applications • Streaming data encoding • Quantum software engineering • … Vincenzo De Maio - Distributed Systems in the Post-Moore Era 35
  • 36. Questions? Dr. Vincenzo De Maio vincenzo@ec.tuwien.ac.at

Editor's Notes

  1. We propose a fault-tolerant offloading method modeled as a Markov Decision Process (MDP) based on predictions per- formed through Support Vector Regression (SVR). SVR is used to estimate offloading service availability, which is used by MDP for offloading decisions. Our approach is implement- ed in a real-world test-bed and compared with the default Ku- bernetes scheduler augmented with hybrid fault-tolerance Edge offloading is widely used to support the execution of near real-time mobile applications. However, offloading on edge infrastructures can suffer from failures due to the ab- sence of supporting systems and environmental factors. We propose a fault-tolerant offloading method modeled as a Markov Decision Process (MDP) based on predictions per- formed through Support Vector Regression (SVR). SVR is used to estimate offloading service availability, which is used by MDP for offloading decisions. Our approach is implement- ed in a real-world test-bed and compared with the default Ku- bernetes scheduler augmented with hybrid fault-tolerance. We propose an edge offloading algorithm that employs Markov Decision Process (MDP) which performs proactive fault tolerance based on predictions obtained through Sup- port Vector Regression (SVR). The SVR algorithm predicts offloading service availability on remote sites and forwards those predictions to the MDP-based decision engine on a mo- bile device that synthesizes the offloading decision policy for task offloading. We select the SVR algorithm due to its pre- diction accuracy above 90% for failure time-series data [15] and its relatively small training dataset [6] w.r.t. deep neural networks. Also, MDPs allow to model edge offloading due to numerous offloading service alternatives and stochastic availability. The offloading framework is evaluated on an ex- perimental test-bed and compared to the baseline Kubernetes scheduler augmented with hybrid fault-tolerance.
  2. We propose a fault-tolerant offloading method modeled as a Markov Decision Process (MDP) based on predictions per- formed through Support Vector Regression (SVR). SVR is used to estimate offloading service availability, which is used by MDP for offloading decisions. Our approach is implement- ed in a real-world test-bed and compared with the default Ku- bernetes scheduler augmented with hybrid fault-tolerance Edge offloading is widely used to support the execution of near real-time mobile applications. However, offloading on edge infrastructures can suffer from failures due to the ab- sence of supporting systems and environmental factors. We propose a fault-tolerant offloading method modeled as a Markov Decision Process (MDP) based on predictions per- formed through Support Vector Regression (SVR). SVR is used to estimate offloading service availability, which is used by MDP for offloading decisions. Our approach is implement- ed in a real-world test-bed and compared with the default Ku- bernetes scheduler augmented with hybrid fault-tolerance. We propose an edge offloading algorithm that employs Markov Decision Process (MDP) which performs proactive fault tolerance based on predictions obtained through Sup- port Vector Regression (SVR). The SVR algorithm predicts offloading service availability on remote sites and forwards those predictions to the MDP-based decision engine on a mo- bile device that synthesizes the offloading decision policy for task offloading. We select the SVR algorithm due to its pre- diction accuracy above 90% for failure time-series data [15] and its relatively small training dataset [6] w.r.t. deep neural networks. Also, MDPs allow to model edge offloading due to numerous offloading service alternatives and stochastic availability. The offloading framework is evaluated on an ex- perimental test-bed and compared to the baseline Kubernetes scheduler augmented with hybrid fault-tolerance.