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![• Classical computers are
inefficient at solving particular
computational problems
• Classical computers must expand
and consume more resources to
solve those problems
• Limits to expansion and
consumption mean that some
problems will remain intractable
Why is this a problem?
[1] https://www.microsoft.com/en-us/research/blog/problems-will-solve-quantum-computer/
[2] N Jones, Nature 561, 163-166 (2018)
A classically intractable problem: Engineering chemical reactions
for efficient fertiliser production
[1]
[2]
Computing is
projected to
consume a
significant portion
of the world’s
energy](https://image.slidesharecdn.com/cspapresentation-quantumcomputing-200911063601/85/Demystifying-Quantum-Computing-4-320.jpg)
![• Approaches to universal quantum
computing
• Circuit-based (gate array)
• Measurement-based (one-way)
• Adiabatic
• Topological
Types of quantum computing
Adiabatic quantum computing
Circuit-based quantum computing
Measurement-based quantum
computing
[1] M Fingerhuth et al PLoS ONE https://doi.org/10.1371/journal.pone.0208561. [2] https://medium.com/@jonathan_hui/qc-programming-with-quantum-gates-
8996b667d256 [3] https://medium.com/@quantum_wa/quantum-annealing-cdb129e96601
[1]
[2] [3]](https://image.slidesharecdn.com/cspapresentation-quantumcomputing-200911063601/85/Demystifying-Quantum-Computing-16-320.jpg)
![• Physical constraints
• Operation (initialisation, gate and
readout) errors
• Operation speeds
• Decoherence
• Conflicts between gates
• Limited qubit connectivity
• Finite set of primitive gates
(owing to costs in time and
memory)
• No QRAM (encoding is part of
operation)
The reality of quantum computing
hardware
Optimal control & error
correction protocols
Optimal scheduling &
routing
Optimal gate
decomposition
Mitigation approach
Cluster of 5 diamond spin-qubits
Efficient encoding
methods
Quantum compiling
[1]
[1] https://www.ibm.com/blogs/research/2019/09/quantum-computation-center/](https://image.slidesharecdn.com/cspapresentation-quantumcomputing-200911063601/85/Demystifying-Quantum-Computing-19-320.jpg)
![Application demonstrations
Understanding the chemical processes in
fertiliser production [1]
Simulation of protein folding [2] Simulation for battery design for cars [3]
Investment portfolio optimisation [4]
Financial risk analysis [5]
[1] https://www.microsoft.com/en-us/research/blog/problems-will-solve-quantum-computer/
[2] A Robert et al arXiv:1908.02163v1 (2019). [3] JE Rice et al arXiv:2001.01120v1 (2020)
[4] M Hodson et al arXiv:1911.05296v1 (2019). [5] S Woerner and DJ Egger npj Quantum Information 5, 15 (2019).](https://image.slidesharecdn.com/cspapresentation-quantumcomputing-200911063601/85/Demystifying-Quantum-Computing-22-320.jpg)
![• Quantum computing will become increasing
hybridised with classical computing
The advent of the QPU accelerator
• Near-term specialist applications will
demand
• integration of classical and quantum
hardware
• hardware-software co-development
• New hardware will enable massively
parallelised, distributed and mobile
applications
Future vision
[1] https://ai.googleblog.com/2018/03/a-preview-of-bristlecone-googles-new.html
[1]](https://image.slidesharecdn.com/cspapresentation-quantumcomputing-200911063601/85/Demystifying-Quantum-Computing-23-320.jpg)
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Demystifying Quantum Computing
- 1. Dr Marcus Doherty Co-founderand Chief Scientific Officer www.quantumbrilliance.com CSPA presentation August 2020 Quantum Computing: Demystifying the seemingly impenetrable, improbable and impractical
- 2. “We are notprepared for the end of Moore's Law” Moore’s Law is ending Source: J. Shalf, Phil. Trans. Roy. Soc. A, https://doi.org/10.1098/rsta.2019.0061
- 3. A temporary extension Source:J. Shalf, Phil. Trans. Roy. Soc. A, https://doi.org/10.1098/rsta.2019.0061
- 4. • Classical computersare inefficient at solving particular computational problems • Classical computers must expand and consume more resources to solve those problems • Limits to expansion and consumption mean that some problems will remain intractable Why is this a problem? [1] https://www.microsoft.com/en-us/research/blog/problems-will-solve-quantum-computer/ [2] N Jones, Nature 561, 163-166 (2018) A classically intractable problem: Engineering chemical reactions for efficient fertiliser production [1] [2] Computing is projected to consume a significant portion of the world’s energy
- 5. Quantum is asolution to take us beyond the limits of the transistor 30 thousand Classical processors faster than ONE Quantum processor Does so by exploiting additional physical properties that are available at the microscopic scale to increase efficiency Google quantum supremacy demonstration: F Arute, Nature 574, 505 (2019).
- 6. What can theydo? Source: https://www.quantum-bits.org/?p=2059 Computational complexity classes Quantum computing applications
- 7. Where are the opportunities forSoftware Professionals? What are its applications? How does Quantum Computing work? Some important Quantum Physics Why Quantum Computing?
- 8. Some important quantumphysics Source: MW Doherty, https://researchcentre.army.gov.au/library/land-power-forum/quantum-technology-introduction
- 9. Some important quantumphysics N S Detection screen Stern-Gerlach experiment Electron spin Magnetic field gradient
- 10. Some important quantumphysics N S Detection screen Quantum observation Classical prediction Stern-Gerlach experiment Magnetic field gradient Superposition state
- 11. Some important quantumphysics Source: MW Doherty, https://researchcentre.army.gov.au/library/land-power-forum/quantum-technology-introduction
- 12. Some important quantumphysics General operating principle Random qubit state Measurement 1 0 or Initialisation Control Basic components Qubit Measurement system Computer interface Control system Initialisation system Source: MW Doherty, https://researchcentre.army.gov.au/library/land-power-forum/quantum-technology-introduction
- 13. Where are the opportunities forSoftware Professionals? What are its applications? How does Quantum Computing work? Some important Quantum Physics Why Quantum Computing?
- 14. • DiVincenzo's criteria: •A scalable physical system with well characterized qubits • The ability to initialize the state of the qubits to a simple fiducial state • Long relevant decoherence times • A "universal" set of quantum gates • A qubit-specific measurement capability Ingredients of a quantum computer Quantum Brilliance’s diamond quantum computing architecture
- 15. Types of quantumcomputing
- 16. • Approaches touniversal quantum computing • Circuit-based (gate array) • Measurement-based (one-way) • Adiabatic • Topological Types of quantum computing Adiabatic quantum computing Circuit-based quantum computing Measurement-based quantum computing [1] M Fingerhuth et al PLoS ONE https://doi.org/10.1371/journal.pone.0208561. [2] https://medium.com/@jonathan_hui/qc-programming-with-quantum-gates- 8996b667d256 [3] https://medium.com/@quantum_wa/quantum-annealing-cdb129e96601 [1] [2] [3]
- 17. Operating principles ofcircuit-based quantum computing 1 0 0 0 𝑎0 𝑎1 𝑎2 𝑎3 | ۧ00 | ۧ01 | ۧ10 | ۧ11 : 𝑐00 𝑐01 𝑐10 𝑐11 𝑐02 𝑐03 𝑐12 𝑐13 𝑐20 𝑐21 𝑐30 𝑐31 𝑐22 𝑐23 𝑐32 𝑐33 𝑏0 𝑏1 𝑏2 𝑏3 : |𝑏0|2 |𝑏1|2 |𝑏2|2 |𝑏3|2 1 0 0 0 , 0 1 0 0 , 0 1 0 0 … Initialisation Input/ Encoding Algorithm operation Readout Output/ decoding Probability Repeat 0 1 0 0 • 5 steps: • Initialisation of the qubit register • Data encoded as a 2n vector of continuous complex numbers • Algorithms implemented via a 2n x 2n unitary transformation • Register state readout and processes repeated to build statistics of a 2n vector of real probabilities • Data decoding by a chosen operation on probabilities o Unitary transformation constructed from a product of unitary operators acting on one or two qubits o These unitary operators are selected from a universal set (eg S, H, T, CNOT)
- 18. • Comparison ofclassical and quantum operation Origins of quantum advantage 1 0 0 0 0 1 0 0 | ۧ00 | ۧ01 | ۧ10 | ۧ11 : 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 Initialisation Input/ Encoding Algorithm operation Readout Output/ decoding 0 0 1 0 1 0 0 0 𝑎0 𝑎1 𝑎2 𝑎3 | ۧ00 | ۧ01 | ۧ10 | ۧ11 : 𝑐00 𝑐01 𝑐10 𝑐11 𝑐02 𝑐03 𝑐12 𝑐13 𝑐20 𝑐21 𝑐30 𝑐31 𝑐22 𝑐23 𝑐32 𝑐33 𝑏0 𝑏1 𝑏2 𝑏3 : |𝑏0|2 |𝑏1|2 |𝑏2|2 |𝑏3|2 1 0 0 0 , 0 1 0 0 , 0 1 0 0 … Initialisation Input/ Encoding Algorithm operation Readout Output/ decoding Probability Repeat 0 1 0 0 Denser encoding: 2n more information Denser operations: 2n higher dimensionality of operations Non-determinism: 2n more repetitions before output • To gain speed up, quantum algorithms must exploit denser encoding and dimensionality, whilst minimising the cost of non-determinism • Achieved by engineering a narrow readout probability distribution
- 19. • Physical constraints •Operation (initialisation, gate and readout) errors • Operation speeds • Decoherence • Conflicts between gates • Limited qubit connectivity • Finite set of primitive gates (owing to costs in time and memory) • No QRAM (encoding is part of operation) The reality of quantum computing hardware Optimal control & error correction protocols Optimal scheduling & routing Optimal gate decomposition Mitigation approach Cluster of 5 diamond spin-qubits Efficient encoding methods Quantum compiling [1] [1] https://www.ibm.com/blogs/research/2019/09/quantum-computation-center/
- 20. Where are the opportunities forSoftware Professionals? What are its applications? How does Quantum Computing work? Some important Quantum Physics Why Quantum Computing?
- 21. QRAM not required •Shor’s Algorithm • Quantum Support Vector Machine • Quantum Semi- definite Programming + more QRAM required • Quantum Fourier Transform • Phase estimation • Grover’s Algorithm • Quantum Principal Component Analysis + more Variational • Variational Quantum Eigensolver • Quantum Approximate Optimisation Algorithm • Variational Quantum Factoring + more Quantum algorithms Clear quantum advantage Clear quantum advantage Implementable on NISQ devices Lots of qubits and large circuit depth required Without QRAM, state preparation routines kill speed-up Quantum advantage often unprovable/ unknown Taxonomy Applications • Quantum chemistry for pharmacology, materials science and chemical engineering • Optimisation in finance, engineering, manufacturing and routing/ process design • Statistical analysis and sampling • Quantum Machine Learning • Image and signal processing • Searching unstructured databases + more
- 22. Application demonstrations Understanding thechemical processes in fertiliser production [1] Simulation of protein folding [2] Simulation for battery design for cars [3] Investment portfolio optimisation [4] Financial risk analysis [5] [1] https://www.microsoft.com/en-us/research/blog/problems-will-solve-quantum-computer/ [2] A Robert et al arXiv:1908.02163v1 (2019). [3] JE Rice et al arXiv:2001.01120v1 (2020) [4] M Hodson et al arXiv:1911.05296v1 (2019). [5] S Woerner and DJ Egger npj Quantum Information 5, 15 (2019).
- 23. • Quantum computingwill become increasing hybridised with classical computing The advent of the QPU accelerator • Near-term specialist applications will demand • integration of classical and quantum hardware • hardware-software co-development • New hardware will enable massively parallelised, distributed and mobile applications Future vision [1] https://ai.googleblog.com/2018/03/a-preview-of-bristlecone-googles-new.html [1]
- 24.
- 25. 2020.04.10_LMV Ubiquitous Quantum Computing Mobile, distributedcomputing Parallelised (super)computing Mainframe Source: https://www.ibm.com/quantum-computing/
- 26. 2020.04.10_LMV Size, weight andpower: the keys to quantum advantage Size/ Weight/ Power (“SWaP”) + Cost Performance Outperform x1 CPU Outperform x1 Supercomputer Low SWaP → Accelerated Pathway to Quantum Applications
- 27. Where are the opportunities forSoftware Professionals? What are its applications? How does Quantum Computing work? Some important Quantum Physics Why Quantum Computing?
- 28. The quantum softwarestack Parallelisation & co-processing Low-level quantum compiling High-level quantum compiling Quantum machine control Distribution and synchronization of tasks between classical and quantum processors Error correction protocols Optimal quantum control techniques Efficient encoding/decoding Operation decomposition, scheduling and routing Fast and precise implementation of machine operations Real-time feedback control Applications & interface Full use case demonstration and validation Development required at all levels of the stack Demands the skills and knowledge of all types of Software Professionals
- 29. • Partnering inthe development and use case demonstration of massively-parallelised, distributed and mobile applications • Partnering to demonstrate early quantum advantage by beating a low SWaP CPUs/GPUs • Solving your problems in finance, logistics, machine learning, image processing or signal processing • Helping you integrate quantum computers into High- Performance Computing systems Opportunities at Quantum Brilliance We can provide priority access to our • Quantum emulator • Quantum hardware • Applications team
- 30. Where are the opportunities forSoftware Professionals? What are its applications? How does Quantum Computing work? Some important Quantum Physics Why Quantum Computing?