1. Quantum Computing
Lecture 1: Basic Introduction
Mountain View CA, July 28, 2020
Slides: http://slideshare.net/LaBlogga
“The laws of physics present no barrier to reducing the
size of computers until bits are the size of atoms”
— Richard P. Feynman (1985)
Melanie Swan
2. 28 July 2020
Quantum Computing
Theoretical Model of Quantum Reality
Quantum reality is information-theoretic and computable
Lecture 1: Quantum Computing basics (hardware)
Lecture 2: Advanced concepts (control software between
macroscale reality and quantum microstates)
Lecture 3: Application (B/CI neuronanorobot network)
1
3. 28 July 2020
Quantum Computing
Agenda
What is a Qubit?
How are Qubits made?
Qubit methods technical deep dive
Quantum Programming
Applications
The Future: Quantum Photonics
Conclusion
2
Quantum Computing
1. Basic Introduction
4. 28 July 2020
Quantum Computing
Feynman: Universal Quantum Computer
3
Sources: Feynman, R.P. (1985). Quantum Mechanical Computers. Foundations of Physics. 16(6):507-31.
Feynman, R.P. (1982). Simulating physics with computers. International Journal of Theor. Physics. 21(6):467-88.
“The laws of physics present no barrier to reducing
the size of computers until bits are the size of atoms
and quantum behavior holds sway” (1985)
Vision: build a “universal quantum simulator” in the
structure of nature (1982)
Simulate field theories with lattice works of spins
5. 28 July 2020
Quantum Computing 4
(abstract)
Computational infrastructure is more powerful
when it is in the same shape as the underlying
3D structure of physical reality
(concrete)
Quantum Computing Tipping Points:
universal quantum computing chips
exotic superconducting materials deployment
quantum optics: global quantum photonic
telecommunications networks
Thesis
6. 28 July 2020
Quantum Computing
Quantum Scale
5
QCD: Quantum Chromodynamics
“Quantum” = anything at the scale of
atomic and subatomic particles
Theme: ability to manipulate physical
reality at increasingly smaller scales
Subatomic particles
Matter particles: fermions (quarks)
Force particles: bosons (gluons)
Scale Entities Physical Theory
1 1 x101 m Humans Newtonian mechanics
2 1 x10-9 m Atoms, ions,
photons
Quantum mechanics
(nanotechnology)
3 1 x10-15 m Subatomic particles QCD/gauge theories
4 1 x10-35 m Planck length Planck scale
Atoms Quantum objects:
atoms, ions, photons
7. 28 July 2020
Quantum Computing
Quantum: many exponential speed-ups
1. Bit (0 or 1)
2. Qubit (0 and 1 in superposition)
3. Qudit (more than 2 values in superposition)
Microchip generates two entangled qudits each with 10
states, for 100 dimensions total, for more than six
entangled qubits could generate (Imany, 2019 )
4. Optics (time and frequency multiplexing)
Existing telecommunications infrastructure
Global network not standalone computers in labs
5. Optics (superposition of inputs and gates)
6
Classical
Computing
Quantum
Computing
Source: Imany et al. (2019). High-dimensional optical quantum logic in large operational spaces. npj Quantum Information. 5(59):1-10.
8. 28 July 2020
Quantum Computing 7
What is Quantum Computing?
Quantum Computing is using quantum-mechanical
properties (SEI: superposition, entanglement, and
interference) to perform computation with
2n scaling (e.g. 9-qubit system tests 512 states (29)
9. 28 July 2020
Quantum Computing
Quantum smartphone ship date?
Technology is notoriously difficult to predict
I think there is a world market for maybe five computers
- Thomas J. Watson, CEO, IBM, 1943
8
Source: Strohmeyer, R. (2008). The 7 Worst Tech Predictions of All Time. PCWorld.
D-Wave Systems
10-feet tall, $15m
Current: Ytterbium-
171 isotopes at 1
Kelvin (-458°F)
Actual room-
temperature
superconductor: ??
10. 28 July 2020
Quantum Computing
Quantum Computing impact
Why is it important?
Immanent as substantial new computing paradigm
Immediate: upgrade to new global cryptography standards
Ongoing: substantial step-ups in processing power
When is it coming?
Maybe within 10 years, early commercial systems shipping now
Do all problems become solvable?
No, one-tier improvement in problem solving complexity
How can I try it?
1-minute per month free cloud access D-Wave Systems, IBM
Program and test algorithms
9
11. 28 July 2020
Quantum Computing
Computational Complexity and Quantum Computing
10
Computational complexity: amount (time and space) of
computing resources required to solve a problem
QC: one-tier improvement in computational complexity
Canonical Traveling Salesperson Problem: check twice as many
cities in half the time using a quantum computer
Solve the next tier of designated problem difficulty with the
current tier’s computational resource (in time and space)
NP becomes solvable in P, EXP becomes solvable in NP
Example: factoring large numbers becomes time-reasonable
P: polynomial time (e.g. solvable in human-reasonable amount of time); NP: non-polynomial (not solvable in human-reasonable
amount of time); EXP: exponential (requires exponential time/space to solve)
Computational
Complexity
12. 28 July 2020
Quantum Computing
Google: Quantum Advantage (October 23, 2019)
First quantum computer to solve a problem
classical computers cannot solve timely
53-qubit Sycamore chip (one damaged qubit)
Task: random circuit sampling (provable randomness)
Sampling versus one answer (i.e. Shor’s factoring, Grover’s search)
Google: Sycamore repeats a random circuit sampling process
a million times in 200 seconds (stores circuits in RAM)
Claim: the most powerful classical computer (supercomputer)
would take 10,000 years to do the same task
IBM counterclaim: no, the calculation could be performed in
2.5 days (write circuits to hard disk and then sample)
Write circuits to 250 petabytes of hard disk (Summit Oak Ridge
National Lab supercomputer) and check with vector matrix
multiplication
11
Source: Arute et al. Quantum supremacy using a programmable superconducting processor. Nature. 574:505-11, and
https://www.scottaaronson.com/blog/?p=4372
13. 28 July 2020
Quantum Computing
Agenda
What is a Qubit?
How are Qubits made?
Qubit methods technical deep dive
Quantum Programming
Applications
The Future: Quantum Photonics
Conclusion
12
Quantum Computing
1. Basic Introduction
14. 28 July 2020
Quantum Computing
A qubit (quantum bit) is the basic unit of
quantum information, the quantum version
of the classical binary bit
13
What is a Qubit?
Bit always exists
in a single binary
state (0 or 1)
Qubit exists in a state of superposition, at
every location with some probability, until
collapsed into a measurement (0 or 1)
Classical Bit Quantum Bit (Qubit)
Source: https://www.newsweek.com/quantum-computing-research-computer-flagship-eu-452167
15. 28 July 2020
Quantum Computing
Qudit (quantum information digit)
Qudits: quantum information digits that can exist in
more than two states
A qubit exists in a superposition of 0 and 1 before being
collapsed to a measurement at the end of the computation
A qutrit exists in the 0, 1, and 2 states until collapsed for
measurement (triplet is useful for quantum error correction)
7 and 10 qudits tested
4 optical qudits achieved the processing power of 20 qubits
Motivation: generalize known quantum computing
techniques to higher level systems
14
Sources: Qudits: Fernando Parisio; Michael Kues. “It from Bit” Wheeler, J.A. (1990). Information, Physics, Quantum: The Search for
Links. In Proc. 3rd Int. Symp. Foundations of Quantum Mechanics, Tokyo, 1989, pp.354-368.
Qutrit stabilizer
code on a torus
It from Bit -> It from Qubit -> It from Qudit
The Wheeler Progression
16. 28 July 2020
Quantum Computing
Agenda
What is a Qubit?
How are Qubits made?
Qubit methods technical deep dive
Quantum Programming
Applications
The Future: Quantum Photonics
Conclusion
15
Quantum Computing
1. Basic Introduction
17. 28 July 2020
Quantum Computing
Any stable two-level quantum-mechanical
system might be used as a qubit
If can obtain 0s and 1s usable in computation
16
How are Qubits made?
18. 28 July 2020
Quantum Computing
1. Superconductors
2. Photonics
3. Trapped ions
17
Source: Economist, Architecture Race for Quantum Computers, 20 June 2015.
Top 3 Qubit Generation Methods (2015)
19. 28 July 2020
Quantum Computing
Top 3 Qubit Generation Methods (2020)
18
1. Superconductors
Commercial systems (on-premises and cloud-based)
IBM & Rigetti: controllable gate model superconductors
(~19 qubits) for all computational problems
D-Wave Systems: less-controllable quantum annealing
machines (2048 qubits) for optimization problems
2. Photonics
3. Trapped ions
Shipping
Research
20. 28 July 2020
Quantum Computing
D-Wave Systems
Quantum Annealing
Solve optimization problems as low energy landscape
Setup: qubits exist across the landscape in superpositions of
0/1 (quantum wave function)
Like a fog blanketing the problem space
Annealing cycle: runs and the fog layer condenses to one
point as the global minimum of the landscape
Qubit spins flip back and forth until settling into the lowest-
energy state of the system
Readout: lowest-energy state is optimal answer
Spin glass analogy (flexible spins funnel to lowest energy)
Holographic annealing
Use AdS/CFT correspondence to map boundary-bulk energy
operators to readout solution in one fewer dimensions
19
Image Source: Qolynes et al (2014) Frustration in biomolecules
21. 28 July 2020
Quantum Computing
Commercial Status by Platform
20
Source: Synthesized from QCWare
Organization Qubit Method # Qubits Status
1 IBM (Almaden CA) Superconducting (gate model) 19 (50) Available
2 D-Wave Systems (Vancouver BC) Superconducting (quantum annealing) 2048 Available
3 Rigetti Computing (Berkeley CA) Superconducting (gate model) 19 Available
4 Google (Mountain View CA) Superconducting (gate model) 53 (72) Built, unreleased
5 Intel/Delft (Netherlands) Superconducting 49 Built, unreleased
6 Quantum Circuits (New Haven CT) Superconducting Unknown Research
7 IonQ (College Park MD) Trapped Ions 23 Built, unreleased
8 Alpine Quantum Tech (Innsbruck) Trapped Ions Unknown Research
9 Microsoft (Santa Barbara CA) Majorana Fermions Unknown Research
10 Nokia Bell Labs (Princeton NJ) FQH State Unknown Research
11 Xanadu Photonics (Toronto ON) Photonics Unknown Research
12 PsiQuantum (Palo Alto CA) Photonics Unknown Research
Tipping point: universal quantum computing chips
22. 28 July 2020
Quantum Computing
Agenda
What is a Qubit?
How are Qubits made?
Qubit methods technical deep dive
Quantum Programming
Applications
The Future: Quantum Photonics
Conclusion
21
Quantum Computing
1. Basic Introduction
23. 28 July 2020
Quantum Computing
Physical Qubit Generation Method #1
Superconducting Circuits
22
Source: http://news.mit.edu/2014/cheaper-superconducting-computer-chips-1017
Idea: extend semiconductor product line
Use existing global fab infrastructure
Produce superconducting chips
Superconductors: materials with zero
electrical resistance when cooled below a
certain critical temperature
More than half of the periodic table elements
Electrons travel unimpeded (no energy dissipation)
20% of electricity is lost due to resistance
At critical temperature, two electrons (usually
repelling) form a weak bond (a Cooper pair) that can
tunnel through metal with no resistance
Superconducting circuit
Superconducting chip
24. 28 July 2020
Quantum Computing
Key enabling technology: Materials advance
“Room-temperature” Superconductors
23
Implication: cool with liquid nitrogen not helium
“Desktop” computing without bulky cryogenic equipment
Initial superconducting materials (1986): copper oxides
Bismuth strontium calcium and yttrium barium copper oxide
New wider range of materials (2008)
Metal-based compounds of iron, aluminum, copper, niobium
Experimental high-pressure materials (2015)
Hydrogen sulfide and lanthanum superhydride
Superconducting Material Critical Temperature Discovery
1 Ordinary superconducting materials Below 30 K -303 °C 1911
2 High-temperature superconducting materials 138 K -135 °C 1986
3 Room-temperature superconducting materials 203 K -70 °C 2015
4 High room-temperature superconducting
materials
260 K -13 °C 2019
25. 28 July 2020
Quantum Computing
Superconducting Circuits
24
Josephson junction: nonlinear superconducting
inductors create qubit energy levels
The nonlinearity of the Josephson inductance breaks
the degeneracy of the energy level spacings, allowing
system to be restricted to only the 2-qubit states
Josephson junctions needed to produce qubits,
otherwise superconducting loop is just a circuit
Linear inductors in a traditional circuit are replaced with
the Josephson junction, a nonlinear element that
produces energy levels with different spacings from
each other that can be used as a qubit
Superconducting loop is a SQUID (superconducting
quantum interference device) magnetometer (a
device for measuring magnetic fields)
Josephson: Nobel
Prize in Physics
(1973) for work
predicting the
tunneling behavior
of superconducting
Cooper pairs
26. 28 July 2020
Quantum Computing
Superconducting Circuits: Rigetti
25
Single Josephson junction qubit
on a sapphire substrate
Electrical circuit with oscillating
current forms the qubits and is and
controlled by electromagnetic fields
Substrate embedded in a copper
waveguide cavity
Waveguide coupled to qubit
transitions to perform computation
Chip: Alternating fixed and
tunable transmon qubits
19Q (one qubit not tunable)
Source: Otterbach, J.S., et al. (2017). Unsupervised machine learning on a hybrid quantum computer. arXiv: 1712.05771v1
27. 28 July 2020
Quantum Computing
Superconducting Circuits: Google
26
Qubits are electrical oscillators constructed
from aluminum (niobium is also used)
Superconducting at 1 K (−272°C)
The oscillator qubits store small amounts of
electrical energy
Oscillator in the 0 state has zero energy
Oscillator in the 1 state has a single quantum of energy
Oscillator resonance frequency
6 gigahertz (300 millikelvin)
Sets the energy differential between the 0 and 1 states
Low enough frequency to build with off-the-shelf
components
High enough frequency so ambient thermal energy does
not scramble the oscillation and introduce errors
Superconducting
microwave circuit
28. 28 July 2020
Quantum Computing
Physical Qubit Generation Method #2
Quantum Photonics
27
Image Source: PSI Quantum
Photon movement
Quantum-mechanical objects
Atom, ion, photon
Optical circuits do not require error correction
Global communications networks built on
photonic transfer
Quantum photonics (general)
Single photons represent qubits
Realized in computing chips or in free space
Compute with entangled states of multiple
photons (photonic clusters)
Single photons are sent through the chip or free
space for the computation and then measured
with photon detectors at the other end
Quantum photonic processor
Quantum photonic wafer
Quantum photonic array
29. 28 July 2020
Quantum Computing
Continuous Qubit Optical Interfaces
28
Source: Vuckovic, J. (2020). Stanford Optimized Quantum Photonics; Sapra, N.V., Yang, K.Y., Vercruysse, D., et al. (2020). On-chip
integrated laser-driven particle accelerator. Science. 367(6473):79-83
All-optical platform from the beginning
Homogeneous qubits with optical interfaces
Method: exploit color center defects (Fabre effect)
Color centers in diamond (silicon and tin vacancy)
Color centers in silicon carbide (manufacture silicon
vacancy in 4H poly tech type (thin film))
Exploit energy level differentials due to missing
atoms in the lattice structure
The wavelength between two color centers depends on
which atom in the lattice is missing and can be used for
computation
30. 28 July 2020
Quantum Computing
Quantum Photonics
29
Diamond center defects method
Introduce impurities to diamond crystal lattice
Implant ion to create nitrogen vacancy
Nitrogen vacancy produces the Farbe center
(color center), a defect in a crystal lattice
occupied by an unpaired electron
The unpaired electron creates an effective
spin which can be manipulated as a qubit
Quantum state can be initialized, manipulated,
and measured at room temperature
Uses the same physics and math as for
Josephson junctions in microwave chips
But, coherence time limited to spin time
Related work:
Accelerator-on-a-chip
(Stanford Nanoscale and
Quantum Photonics Lab)
Source: Vuckovic, J. (2020). Stanford Optimized Quantum Photonics; Sapra, N.V., Yang, K.Y., Vercruysse, D., et al. (2020). On-chip
integrated laser-driven particle accelerator. Science. 367(6473):79-83
31. 28 July 2020
Quantum Computing
Physical Qubit Generation Method #3
Trapped Ions
30
Source: Images: IonQ, College Park MD
Silicon chips store Ytterbium ions in
electromagnetic traps
Manipulate in computation with lasers and
electromagnetic fields
Ions (atoms stripped of electrons)
Easier to compute with positively or
negatively charged ions
Ytterbium ions do not need supercooling,
have a long coherence time, and require
less error correction
32. 28 July 2020
Quantum Computing
Trapped Ions
31
Source: IonQ, College Park MD
1. Silicon chip with 100 electrodes confines
and controls ions in an ultrahigh-vacuum
Electrodes underneath the ions apply electrical
potentials to hold the charged particles together
in a linear array
2. Lasers initialize the qubits, entangle them
through coupling, and produce quantum
logic gates to execute the computation
3. At the end of the computation, another
laser causes ions to fluoresce if they are
in a certain qubit state
Fluorescence collected to measure each qubit
and compute the result of the computation
Ions trapped in array
Trapped-ion quantum
processor
33. 28 July 2020
Quantum Computing
Physical Qubit Generation Method #4
Topological Qubits: Majorana Fermions
32
Topological qubits
Qubits made from particles on topological
superconductors and electrically controlled in
computation based on movement trajectories
Majorana fermions (particle + anti-particle pairs)
Novel quantum phases arising in condensed
matter with Cooper pairing states (i.e. quantum
computable states) on superconductor edges
Majorana fermions move in trajectories
resembling a multi-stranded braid
Use braid wave functions as quantum logic gates
34. 28 July 2020
Quantum Computing
Agenda
What is a Qubit?
How are Qubits made?
Qubit methods technical deep dive
Quantum Programming
Applications
The Future: Quantum Photonics
Conclusion
33
Quantum Computing
1. Basic Introduction
35. 28 July 2020
Quantum Computing
DiVincenzo Criteria for Universal Computing
34
Quantum computing standards for gate array computing
1: demonstrate a reliable system for making qubits
2-5: perform accurate computation
Qubit formation (criterion #1)
1. A scalable system of well-characterized qubits
Qubit control for computation (criteria #2-5)
2. Qubits that can be initialized with fidelity (to the zero state)
3. Qubits with long-enough coherence time for calculation
4. A universal set of quantum gates
5. Capability to measure any specific qubit in the ending result
Source: DiVincenzo, D.P. (2000). The physical implementation of quantum computation. Fortschrit. Phys. 48(9–11):771–83.
36. 28 July 2020
Quantum Computing
Hardware for Qubit Generation and Control
35
Source: Synthesized from QCWare
Qubit Type Qubit formation
(DiVincenzo criterion #1)
Qubit control for computation
(DiVincenzo criteria #2-5)
1 Superconducting
circuits
Electrical circuit with oscillating current Electromagnetic fields and microwave
pulses
2 Photonic circuits Single photons (or squeezed states) in
silicon waveguides
Marshalled cluster state of multi-
dimensional entangled qubits
3 Diamond center
defects
Defect has an effective spin; the two-
levels of the spin define a qubit
Microwave fields and lasers
4 Trapped ions Ion (atom stripped of one electron) Ions stored in electromagnetic traps
and manipulated with lasers
5 Majorana fermions Topological superconductors Electrically-controlled along non-
abelian “braiding” path
6 Neutral atoms Electronic states of atoms trapped by
laser-formed optical lattice
Controlled by lasers
7 Quantum dots Electron spins in a semiconductor
nanostructure
Microwave pulses
Race to build first universal gate quantum computer
Easy to generate qubits, difficult to compute with fidelity
37. 28 July 2020
Quantum Computing
Quantum Programming
Standard gates
Hadamard gate: acts on one qubit to
put it in a superposition
CNOT gate: acts on two qubits to flip one
Toffoli gate: acts on three or more qubits to implement the six
Boolean operators (AND, conditional AND, OR, conditional OR,
exclusive OR, and NOT)
Computing paradigms
Classical computing relies on electrical conductivity
Boolean algebra (true/false, and/or) to manipulate bits
Quantum computing relies on quantum mechanics
Linear algebra to manipulate matrices of complex numbers (i.e. the
amplitudes of possible states)
36
38. 28 July 2020
Quantum Computing
Standardized Tools
37
Bernstein-Vazirani algorithm (1997)
“Hello, World!” of quantum: extract specific bits from a string
Variational quantum eigensolver (VQE) (Peruzzo,
2014)
Find the eigenvalues of a matrix; An eigensolver is a program
designed to calculate solutions to 3D problems
Quantum approximate optimization algorithm (QAOA)
(Farhi, 2014)
Combinatorial optimization problems (Traveling Salesman
Problem, find a “good” solution (acceptable answer) in
polynomial time (a reasonable amount of time); max-cut
partition function, solve as energy landscape minimization
39. 28 July 2020
Quantum Computing
Goal: Standard Gate Array Computing
38
2n scaling: 9-qubit system (29) represents 512 states
Source: D-Wave Systems, A Machine of a Different Kind, Quantum Computing, 2019
40. 28 July 2020
Quantum Computing
Quantum Computing Roadmap
39
Long-term: Universal quantum computing
Universal computation devices using fault-tolerant
quantum information processors
Error correction required (system noise overwhelms
coherent wave activity of qubit particles)
Available now: NISQ devices (noisy intermediate-
scale quantum)
Error correction not required
Applications in optimization, simulation, machine
learning, and cryptography
Source: Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum. 2(79):1-20.
41. 28 July 2020
Quantum Computing 40
Sources: Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum 2(79):1–20.
https://amitray.com/roadmap-for-1000-qubits-fault-tolerant-quantum-computers/
Quantum Computing Roadmap
Long-term applications
Shor’s factoring algorithm (could break current
cryptography standard (RSA))
Grover’s search algorithm (faster search through large
data sets)
42. 28 July 2020
Quantum Computing
Agenda
What is a Qubit?
How are Qubits made?
Qubit methods technical deep dive
Quantum Programming
Applications
The Future: Quantum Photonics
Conclusion
41
Quantum Computing
1. Basic Introduction
43. 28 July 2020
Quantum Computing
Quantum Computing Applications
Cryptography and security
Certifiable randomness (entropy) and quantum statistics
Quantum machine learning
Optimization, nitrogen sequestration
Simulation
Exotic materials, molecular dynamics, drug discovery
Emulation and pathology resolution
Quantum computing for the brain
42
44. 28 July 2020
Quantum Computing
Quantum Cryptography
Quantum computing implicated in eventually being able
to break existing cryptographic standards (2048-bit RSA)
2019 US National Academies of Sciences report
“unlikely within 10 years” however methods improving
Solution: US NIST developing next-generation standards
Lattice cryptography (complex 3D arrangements of atoms)
Instead of the difficulty of factoring large numbers (RSA 2048) or
any other number theory-based methods (e.g. discrete log)
Overall mathematical shift to group theory (lattices) from number
theory (factoring)
43
Source: Grumbling, E. & Horowitz, M. (2019). Quantum Computing: Progress and Prospects. Washington, DC: US National Academies of
Sciences, p 157.
45. 28 July 2020
Quantum Computing 44
NIST Next-generation Cryptography
NIST: 26 of 69 algorithms advance to
post-quantum crypto semifinal (Jan 2019)
Public-key encryption (17)
Digital signature schemes (9)
Approaches: lattice-based,
code-based, multivariate
Lattice-based: target the Learning with
Errors (LWE) problem with module or ring
formulation (MLWE or RLWE)
Code-based: error-correcting codes (Low
Density Parity Check (LDPC) codes)
Multivariate: field equations (hidden fields
and small fields) and algebraic equations
Source: NISTIR 8240: Status Report on the First Round of the NIST Post-Quantum Cryptography Standardization Process, January
2019, https://doi.org/10.6028/NIST.IR.8240.
46. 28 July 2020
Quantum Computing
Nature’s Quantum Security Features
45
Source: Swan et al. (2020). Quantum Computing. London: World Scientific.
Reality is information-theoretic
Computational complexity class of quantum
information (BQP/QSZK) has access to
Security features naturally built into quantum
mechanical domains
Principle Security Feature
1 No-cloning theorem Cannot copy quantum information
2 No-measurement principle Cannot measure quantum information without damaging it
(eavesdropping is immediately detectable)
3 Quantum statistics Provable randomness: distributions could only have been quantum-
generated (implications for quantum cryptography)
4 Quantum error correction Error correction via ancilla (larger state of entangled qubits)
5 BQP/QSZK computational
complexity
Quantum information domains compute quickly enough to perform
their own computational verification (zero-knowledge proofs)
47. 28 July 2020
Quantum Computing
Killer App: Quantum Machine Learning
46
Machine Learning and Quantum Computing
Statistical methods with probabilistic output
Sigmoidal Function 3D Hilbert Space
0
1
0
1
Machine Learning Quantum Computing
Source: Image: machine learning object recognition: Anandkumar (2014). Tensor models for machine learning.
48. 28 July 2020
Quantum Computing
Quantum Optical Neural Networks
47
Classical neural network architecture
Hidden layers are rectified linear units (ReLUs) and the output neuron
uses a sigmoid activation function to map the output into the range (0, 1)
Quantum optical neural network architecture
Inputs are single photon Fock states. The single-site nonlinearities are
given a Kerr-type interaction applying a phase quadratic in the number of
photons. Readout is given by photon-number-resolving detectors, which
measure the photon number at each output mode
Source. Steinbrecher, G.R. et al., (2019). Quantum Optical Neural Networks. npj Quantum Information. 5(60):1-9.
Quantum Optical Neural NetworkClassical Neural Network
49. 28 July 2020
Quantum Computing
Quantum Optimization Use Cases (D-Wave)
48
Sources: Hetzner, C. (2019). VW, Canadian tech company D-Wave team on quantum computing. Automotive News Canada; Fassler, J.
(2018). Hey Amazon, Kroger’s new delivery partner operates almost entirely on robots. New Food Economy.
UK online grocer Ocado’s automated warehouses:
1100 robots, 250,00 items, 3m instructions
Optimization algorithm coordinates hundreds of robots
passing within 5 millimeters of each other at speeds of 4
meters per second, fulfilling 65,000 orders per week
Volkswagen: 418-taxi network in Beijing
Optimize travel time, implement resulting traffic
management system in Lisbon and beyond
AdTech for web browser promotion placement
50. 28 July 2020
Quantum Computing
Simulating Chemistry
Molecular dynamics
Can simulate millions of atoms at present
Need quantum computing to capture quantum-
mechanical interactions between electrons
49
Sources: Univ. of Illinois at Chicago/Argonne National Lab/Univ. of Southern California; Kandala et al. (2017). Nature. 549, 242
7-qubit superconducting
circuit (false color) to
simulate a beryllium
hydride molecule (IBM)
51. 28 July 2020
Quantum Computing
Chips: CPU -> GPU -> TPU -> QPU
GPU (graphics processing unit)
3D graphics cards for fast matrix multiplication
TPU (tensor processing unit) (Edge TPU 2018)
Flow through matrix multiplications without having to
store interim values in memory
QPU (quantum processing unit) (Sycamore 2020)
Solve problems quadratically or polynomially faster
exploiting SEI Properties (superposition, entanglement,
interference)
50
TPU processing cluster and
Sycamore quantum
superconducting chip
Tipping point:
universal quantum
computing chips
52. 28 July 2020
Quantum Computing
Agenda
What is a Qubit?
How are Qubits made?
Qubit methods technical deep dive
Quantum Programming
Applications
The Future: Quantum Photonics
Conclusion
51
Quantum Computing
1. Basic Introduction
53. 28 July 2020
Quantum Computing
Quantum Optical Networks
52
Quantum photonics could be at the center of
future global communications networks just as
optical networking is today
Many ways to make qubits for computing on
standalone machines
For a larger architecture of networked
machines, electrical signals must be
converted to optical signals
Photonics: core global telecoms network
technology
Future: “Cisco for optical routers”
Quantum photonics: next-gen telecoms
Image Source: Walther, P. (2018). Photonic Quantum Computing. Vienna Center for Quantum Science and Technology.
Integrated quantum
optical switch
55. 28 July 2020
Quantum Computing
Scalable Global Quantum Networks
54
Two methods currently in development
Microwave superconducting platform interfaced to optical
networks with electrical-optical interconnects
Optical platform with continuous qubit optical interfaces
Photonic Integrated Circuits (PICs)
Source: Vuckovic, J. (2020). Stanford Optimized Quantum Photonics; Sapra, N.V., Yang, K.Y., Vercruysse, D., et al. (2020). On-chip
integrated laser-driven particle accelerator. Science. 367(6473):79-83
Quantum Photonic
Processors
All-optical Platform
Superconducting
Processors
Optical-Electrical
Interconnects
Driver: quantum computing Drivers: 5G, data center, 100GbE
All OpticalOptical-Electrical
Chips
Global
Comms
Networks
56. 28 July 2020
Quantum Computing
Quantum Photonic Spacetime Multiplexing
55
Standard quantum computing speed-up
Space accelerated by testing states of 3D space
Superposition of inputs
Optical quantum computing speed-up
Time accelerated by testing permutations of gate order
Superposition of gate order and inputs
Sources: Procopio et al. 2015. Experimental Superposition of Orders of Quantum Gates. Nature Communications. 6(7913):1-6;
Walther, P. (2018). Photonic Quantum Computing. Vienna Center for Quantum Science and Technology.
Quantum Photonic Gate Superposition
Parallel to time-space
manipulation in global
fiberoptic
communications
TDM/WDM: time-
division wave-division
multiplexing
57. 28 July 2020
Quantum Computing
Agenda
What is a Qubit?
How are Qubits made?
Qubit methods technical deep dive
Quantum Programming
Applications
The Future: Quantum Photonics
Conclusion
56
Quantum Computing
1. Basic Introduction
58. 28 July 2020
Quantum Computing
Risks and Limitations
Implementation stalls
Qubits are more sensitive to environmental noise than bits
Error correction stalls
Unable to move past contemporary 50-70 qubit machines to
million-qubit machines
Materials discovery stalls
Cannot find actual room-temperature superconductors
Limitations of underlying physical theories
Quantum mechanics
Need beyond-probability methods that emphasize spectra,
entanglement, entropy (irreversibility), and field flux
Technology cycle is too early
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59. 28 July 2020
Quantum Computing 58
Conclusion
High-dimensionality is a central theme in
science and technology development
Not just 3D but higher-dimensionality
Nature’s built-in quantum security features
No cloning, no measurement, zero-knowledge
proofs, quantum statistics & error correction
Killer app: quantum machine learning
Statistical methods with probabilistic output
Apps in general:
Cryptography, superconducting materials
simulation, quantum computing for the brain
The future: quantum optical networks
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Quantum Computing 59
(abstract)
Computational infrastructure is more powerful
when it is in the same shape as the underlying
3D structure of physical reality
(concrete)
Quantum Computing Tipping Points:
universal quantum computing chips
exotic superconducting materials deployment
quantum optics: global quantum photonic
telecommunications networks
Thesis
61. Quantum Computing
Lecture 1: Basic Introduction
Mountain View CA, July 28, 2020
Slides: http://slideshare.net/LaBlogga
Thank you!
Questions?
Melanie Swan