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: Speculative application (B/CI neuronanorobot network)

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

54. 28 July 2020
Quantum Computing
Scalable Global Quantum Networks
53
 Quantum internet
 Security, privacy, scalability
 Quantum key distribution, quantum routers, quantum repeaters,
quantum simulators, quantum components, quantum memory
1. Homogeneous scalable qubits (standalone machines)
2. Efficient optical interfaces (networked machines)
 Quantum transducers convert microwave to optical
 Optical interfaces (optical superconducting)
 Microwave interfaces (Josephson junction superconducting)
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

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
57

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

60. 28 July 2020
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