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ISSCC 2017

Evening EventQuantum Engineering:Hype, Spin or Reality

Quantum Engineering: Hype, Spin, or Reality?© 2017 IEEE International Solid-State Circuits Conference 1 of 11

Quantum Engineering: Hype, Spin, or Reality?

Moderator: Edoardo CharbonEPFL, TU Delft, Intel Corp.



Quantum engineering is more than quantum computingQuantum sensorsQuantum ITQuantum internetQuantum security




‘Quantum’ means (among others) that strange quantum mechanical effects, such as superposition and entanglement, apply





QE deals with devices, circuits, architectures used/useful in quantum based systems





QE deals with devices, circuits, architectures used/useful in quantum based systems

This also includes classical systems operating in unusual conditions, but retaining the advantages of the economy of scale.



Should we rethink computing and electronics at large?



Should we rethink computing and electronics at large? What are the challenges ahead? Are we prepared?



Should we rethink computing and electronics at large? What are the challenges ahead? Are we prepared? Will QE be the next wave in research? Will there be

products based on QE?




products based on QE? Will Moore’s Law compete or enable QE?


The Panelists

James Clarke, Intel Corp., Hillsboro, OR Ken Shepard, Columbia Univ., NY Lieven Vandersypen, TU Delft, The Netherlands Francesco Regazzoni, University of Lugano, Switzerland Andrea Morello, UNSW, Australia Peter McMahon, Stanford University, CA

Historical perspective: Quantum entanglement© 2017 IEEE International Solid-State Circuits Conference 1 of 3

Historical perspective: Quantum entanglement

Spooky action at a distance?

EPR paradox

1900-1960

Quantum communicationQuantum computing

Entanglement is useful!

1990- 2000

Test of “Bell’s inequalities”

Entanglement exists!

1960-1990

+


Historical perspective: Quantum computing

Exploratory experiments

1995-2005

Let’s make it happen,together!

2015- . . .

Is large-scale quantum computing feasible?

2005-2015

+01 10

15=3x5


A complex, multidisciplinary puzzle,requiring an integrated multidisciplinary effort

NanofabricationMaterials scienceQuantum physics

Circuit design

Mathematics

Softwareengineering

Computerarchitecture

Computer science

Quantum Computer and Cryptography:

Threats, Challenges, and Opportunities

Francesco RegazzoniALaRI, University of Lugano, Switzerland

Francesco Regazzoni - ALaRI USI 6 February 2017, ISSCC, San Francisco, California P. 1

© 2017 IEEE International Solid-State Circuits Conference

Quantum Engineering: Hype, spin or reality? 1 of 15

Let’s fix a common language...

What is Quantum Engineering (securityperspective)?






Quantum resistant cryptography IS NOTsynonymous of quantum computers






Quantum resistant cryptography IS NOTsynonymous of quantum computers

Computing power of quantum computer is thecause

Quantum resistant cryptography is theconsequence




Why Security Community Care?

Shor’s Algorithm...

Our Public Key infrastructure is gone!




Why Security Community Care?

Shor’s Algorithm...

Our Public Key infrastructure is gone!

Securing IoT and CPSs

IoT and CPSs will still be based on classicalcomputers

We need quantum secure algorithms running onclassical computer




Spot the difference...





Source http://qutech.nl/ Source www.pexels.com






Not everybody was believing on it, buteverybody wanted to be the first one doing it!




BUT.... spot commonalities...

Walk on the Moon

Cordless tool

Smoke detector

Ear thermometer

Telemedicine

Protective paint

Scratch-resistant glasses

Memory foam

Water filter

Swimsuit

Freeze-dried food

Joystick

Satellite TV

Fast-acting dental braces

....





Walk on the Moon

Cordless tool

Smoke detector

Ear thermometer

Telemedicine

Protective paint


Memory foam

Water filter

Swimsuit

Freeze-dried food

Joystick

Satellite TV


....

All of them were invented to support spacemissions!





Walk on the Moon

Cordless tool

Smoke detector

Ear thermometer

Telemedicine

Protective paint


Memory foam

Water filter

Swimsuit

Freeze-dried food

Joystick

Satellite TV


....

What can it be the outcome of quantumengineering?





Walk on the Moon

Cordless tool

Smoke detector

Ear thermometer

Telemedicine

Protective paint


Memory foam

Water filter

Swimsuit

Freeze-dried food

Joystick

Satellite TV


....

Quantum Engineering

A New Public Key Infrastructure!

...




Thank you!

Quantum engineering is a great opportunity forimproving our secure infrastructure.

Thank you for your attention!

mail: [email protected]




Quantum Engineering:Hype, spin or reality?

Andrea Morello – ISSCC – 6 Feb 2017

School of Electrical Engineering & Telecommunications



Quantum computing: Challenges and solutions

Decoherence Materials, isotopes, qubit encodings

Errors, imperfections Quantum error correction

|0

|1

Prohibitive thresholds

10Surface codes, topological codes

10W.G. Unruh, Phys. Rev. A 51, 992 (1995) J.T. Muhonen et al., Nature Nano. 9, 981 (2014)R. Landauer, Phys. Lett. A 217, 188 (1996) P.W. Shor, Phys. Rev. A 52, R2493 (1996)L.C.L. Hollenberg et al., Phys. Rev. B 74, 045311 (2006) R. Raussendorf et al., Phys. Rev. Lett. 98, 190504 (2007)



How to move forward

• Stay humble

• Engage academia with industry

• Be patientNEC, 1999 UCSB, 2014

15 years

• Teach Quantum Engineering

• Engage the public with quantum science; debunk “quantum weirdness”

• Stay positive



Position on Prospects forQuantum Information

TechnologyPeter L. McMahonStanford University

andYoshihisa Yamamoto

Stanford University / Japan Science and Technology Agency

E-mail: [email protected] 2017 Panel Discussion “Quantum Engineering: Hype, Spin or Reality?”

This presentation primarily addresses computing applications; it touches on communication applications, and does not address sensing and metrology applications.



• Very few algorithms with exponential speedups for practical applications (despite 30+ years of searching)

• Shor’s algorithm (whose practical value is lost if post-quantum cryptography is widely deployed)

• Radar scattering cross-section calculation [Cla13]• Sparse electrical network analysis [Wan13] • Quantum Recommendation Systems [Ker16]• Quantum simulation (chemistry) [Kas11]

• The speedups are not guaranteed• Proven speedups are over the best-known classical algorithms, but

it is possible that better classical algorithms are developed (with this generally being more likely the more recent the discovery of the quantum algorithm)

• Overhead for error correction is very high• ~100-million (108) physical qubits (realizing ~6000 logical qubits)

needed to make a QC that can perform Shor factoring on a 1024-bit number [Jon12, Fow12]

• With the best-known algorithms and hardware designs, even a ~100-million-physical-qubit machine could only perform quantum chemistry simulations on few-atom molecules (such as alanine) [Jon12]

• Fully-quantum simulations of large biomolecules and biomolecule-drug interactions would require a substantially larger machine, and have runtimes that are not yet shown to be reasonable (<years)

• The largest current experimental prototypes have only ~10high-quality, fully-controllable physical qubits

• Scaling the hardware is an extraordinary engineering challenge

• Need to maintain quantum coherence, universal operations, etc. while scaling to millions of qubits, each of which may require multiple control wires with GHz-frequency signals

• Need fast real-time classical signal processing (for error correction)• Both these are major challenges for the circuits community

• Useful quantum simulation is possible with 100 logical qubits [Bau16]

• Algorithm development has proceeded slowly, but has not stalled – three of the most promising practical applications were only discovered in the past ~5 years [Cla13, Wan13, Ker16], and substantial progress on quantum simulation algorithms has also been made recently

• Possible use of short-depth circuits that don’t require large-overhead error correction [Tem16]

• Quantum error correction is an active area of study, and it is possible that fault-tolerant QC designs with lower overheads will be discovered

• Multiple qubit technologies now exist that can very plausibly be scaled up given enough time and money, and result in the creation of a functioning large-scale QC

• Superconducting qubits (e.g., [Bar14], [Ste16])• Gate-defined quantum dots in silicon (e.g., [Mau12], [Vel15])• Donor spins in silicon (e.g., [Lau15])• Trapped ions (e.g., [Mon13], [Mon16])• Photonics qubits (e.g., [Rud16])• There has been rapid progress in both physics and materials

research, such that a comprehensive review of physical systems for quantum computing from 2010 now feels quite dated [Lad10]

Reasons to be hopeful Reasons to be pessimisticCircuit-Model Quantum Computing



• No theoretically-proven speedup for any problem of practical interest

• No experimentally-proven speedup for any problem at all (when comparing against the best-known classical solver for the same task, running on a single CPU core) (despite media reports to the contrary!)

• QAs are only even potentially useful for a limited class of optimization problems (unconstrained quadratic binary optimization constrained discrete optimization)

• Not all discrete optimization problems can be efficiently mapped to Ising form. For example, the Traveling Salesman Problem for Mcities maps to an Ising problem with M2 spins a 10,000-city problem would require 100-million (108) spins to be represented!

• Limited physical connectivity results in substantial overheads when embedding problems (e.g., [Lec15] requires ~N2/2 physical qubits to represent an Ising problem with N spins)

• E.g., directly solving a 2,000-vertex (arbitrary connectivity) MAX-CUT problem requires a QA with ~1,000,000 physical qubits (with currently-known realistic QA architectures)

• This is in addition to the mapping overhead, so solving a 10,000-city TSP problem may require a QA with ~5-quadrillion (5 x 1015) qubits

• The classical heuristic solver competition is very strong (Moore’s Law and 50+ years of algorithms development)

• QA is a heuristic and should be compared against the best classical heuristic solvers

• In practice these heuristic solvers are very effective on systems up to ~1,000 fully-connected spins (requiring ~10-100 seconds to reach ground states, i.e., optimal solutions)

• Even for problems with ~10,000 fully-connected spins, runtimes of minutes on a single CPU core yield excellent approximate solutions

• Finding a use case where a QA speedup is sufficiently large and important that it is worth replacing a $0.02/hour classical CPU (Amazon Web Services pricing) with a much-more-expensive-per-hour QA is even more difficult

• 2000-low-coherence-qubit prototypes are available [Kin17]• Quantum annealing is quite tolerant to errors in a way that is not the

case for general circuit-model quantum computation, so QA prototypes can be constructed from low-coherence qubits (although it is conjectured that lower-coherence does result in reduced system performance, and ultimately high-coherence qubits might be needed to achieve a speedup)

• There is experimental evidence [Den16] that quantum tunneling might result in a speedup for some problems once sufficiently advanced prototypes are built (albeit of a constant-factor nature rather than polynomial [Alb16])

• Many interesting and useful discrete optimization problems map with fairly low overhead to the general form that quantum annealers can solve (Ising form) a quantum annealer that delivers a large speedup could have immediate practical use in many problems of commercial relevance [Koc14, Luc14]

Quantum Annealing (“Using quantum effects to get a computational advantage without having to build a fully-fledged circuit-model quantum computer”)

Reasons to be hopeful Reasons to be pessimistic



Quantum Repeaters (“Transfer/distribution of quantum states”) and Cryptography (“Secure private key distribution, aka QKD”)

• Long-distance QKD requires at least one of [McM15]:• Free-space links (typically with satellites), which are susceptible to

denial-of-service attacks• Secured classical relay stations every ~100-1000 km, which is

typically expensive, impractical, or both• Unsecured quantum repeater stations every ~100-1000 km, and the

best fault-tolerant quantum repeater designs are essentially quantum computers (running fully-fledged quantum error correcting codes) with optical interfaces likely very expensive and impractical, and many years from realization

• Achieving high key-transfer-rates is difficult, and it is unclear in which scenarios it would be practically beneficial to use a QKD system rather than just trusted, armed couriers carrying hard drives containing private keys

• Although QKD theoretically offers perfect security under the assumption that quantum mechanics is correct, practical realizations do not perfectly implement the QKD protocols, and consequently are susceptible to hacking [Lo14]

• Discovered uses for quantum repeaters besides QKD are, so far, very niche

• BB84-based QKD is relatively simple to realize (versus QC technology), and has been available commercially for ~10 years

• Quantum repeaters may have uses in areas besides enabling long-distance QKD, such as optical interferometry for astronomy [Got12] and stable/accurate atomic clock networks [Kom14]

Reasons to be hopeful Reasons to be pessimistic



See [McM16] and [Ina16] for details.

Optical Parametric Oscillator (OPO) Pulses

pump pulse

approximate measurement

FPGA

optical signal generation

OPO pulse

optical fiber ringcoupler coupler

OPA

measured pulse

feedback pulse

FPGA Measurement-Feedback Circuit

Time (s)

Ising

Ener

gy

CIM SA

MAX-CUT problem on complete graph with N=2000 vertices

There are many open questions (both theoretically and experimentally) about this approach, but it may be a path towards constructing a novel, efficient, physics-based optimization machine.

Coherent Ising Machines: a new approach to physics-based computing inspired by quantum annealing



Circuit-Model QCs Quantum Annealers Coherent Ising Machines

Computationrotation of state vectors in closedsystems

adiabatic evolution of system Hamiltonians

phase transition in quantum oscillator networks

Information carrier spin-1/2 particles spin-1/2 particles coherent waves

Principle quantum interference quantum tunneling OPO lasing dynamics

Dissipation must be suppressed by QEC may need QEC?

error correction not needed

Temperature cryogenic cryogenic room temperature

System sizeConnectivity

5-9 qubits (IBM/Google)<10

2000 qubits (D-Wave)~3,500

2000 bits (NTT/Stanford)4 x 106

Comparison of (some) non-traditional computing approaches



• S. Aaronson. “Read the fine print.” Nature Physics 11, 291-293 (2015)

• T. Albash and D.A. Lidar. “Adiabatic Quantum Computing.” arXiv:1611.04471 (2016)

• R. Barends, et al. “Superconducting quantum circuits at the surface code threshold for fault tolerance.” Nature 508, 500-503 (2014)

• B. Bauer, et al. “Hybrid Quantum-Classical Approach to Correlated Materials.” Phys. Rev. X 6, 031045 (2016)

• B.D. Clader, B.C. Jacobs and C.R. Sprouse. “Preconditioned Quantum Linear System Algorithm.” Phys. Rev. Lett. 110, 250504 (2013)

• V. Denchev, et al. “What is the Computational Value of Finite-Range Tunneling?” Phys. Rev. X 6, 031015 (2016)

• A.G. Fowler, et al. “Surface codes: Towards practical large-scale quantum computation.” Phys. Rev. A 86, 032324 (2012)

• D. Gottesman, et al. “Longer-Baseline Telescopes Using Quantum Repeaters.” Phys. Rev. Lett. 109, 070503 (2012)

• T. Inagaki, et al. “A coherent Ising machine for 2000-node optimization problems.” Science 354, 6312, 603-606 (2016)

• N.C. Jones, et al. “Layered Architecture for Quantum Computing” Phys. Rev. X2, 031007 (2012)

• I. Kassal, et al. “Simulating Chemistry Using Quantum Computers.” Ann. Rev. Phys. Chem. 62, 185-207 (2011)

• I. Kerenidis and A. Prakash. “Quantum Recommendation Systems.” arXiv:1603.08675 (2016)

• J. King, et al. “Quantum Annealing amid Local Ruggedness and Global Frustration.” arXiv:1701.04579 (2017)

• G. Kochenberger, et al. “The unconstrained binary quadratic programming problem: a survey.” J. Combin. Opt. 28, 1, 58-81 (2014)

• P. Komar, et al. “A quantum network of clocks.” Nat. Phys. 10, 582-587 (2014)

• T.D. Ladd, et al. “Quantum computers.” Nature 464, 45-53 (2010)

• A. Laucht, et al. “Electrically controlling single-spin qubits in a continuous microwave field.” Sci. Adv. 1, 3, e1500022 (2015)

• W. Lechner, et al. “A quantum annealing architecture with all-to-all connectivity from local interactions.” Sci. Adv. 1, 9, e1500838 (2015)

• H.-K. Lo, et al. “Secure quantum key distribution.” Nat. Phot. 8, 595-604 (2014)

• A. Lucas. “Ising formulations of many NP problems.” Frontiers in Physics 2, 5 (2014)

• B.M. Maune, et al. “Coherent singlet-triplet oscillations in a silicon-based double quantum dot.” Nature 481, 344-347 (2012)

• P.L. McMahon and K. De Greve. “Towards Quantum Repeaters with Solid-State Qubits: Spin-Photon Entanglement Generation using Self-Assembled Quantum Dots.” in Engineering the Atom-Photon Interaction (Springer-Verlag, 2015)

• P.L. McMahon*, A. Marandi*, et al. “A fully programmable 100-spin coherent Ising machine with all-to-all connections.” Science 354, 6312, 614-617 (2016)

• C. Monroe and J. Kim. “Scaling the Ion Trap Quantum Processor.” Science 339, 6124, 1164-1169 (2013)

• T. Monz, et al. “Realization of a scalable Shor algorithm.” Science 351, 6277, 1068-1070 (2016)

• T. Rudolph. “Why I am optimistic about the silicon-photonic route to quantum computing.” arXiv:1607.08535 (2016)

• M. Steffen, et al. “Progress, status, and prospects of superconducting qubits for quantum computing.” 46th IEEE European Solid-State Device Research Conference (ESSDERC) (2016)

• K. Temme, S. Bravyi and J.M. Gambetta. “Error mitigation for short depth quantum circuits.” arXiv:1612.02058 (2016)

• M. Veldhorst, et al. “A two-qubit logic gate in silicon.” Nature 526, 410-414 (2015)

• G. Wang. “Efficient Quantum Algorithms for Analyzing Large Sparse Electrical Networks.” arXiv:1311.1851 (2013)

Reference



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