Quantum Business in Japanese market

Solve problem that human beings cannot solve

Introduction

Yuichiro MinatoCEO of MDR Inc.

Graduated from the university of TokyoKengo Kuma & associates (Architecture)

2008- MDR Inc.2017- Associate program manager on ImPACT project

MDR OverviewCompany Name MDR Inc.

Location Hongo2-40-14-3F, Bunkyo-ku, Tokyo, Japan

Established 2008

Suppliers National Labs, National Univ., Domestic major companies

Capital $2,030,000

Business Quantum Computer Fullstack

Employee 14 (+5advisor)

Finance / (Marunouchi, Financial Center of Tokyo)

R&D / Business Division (Hongo, Near Univ. of Tokyo)

Full-stack development team from software to hardwareEngineering and theory mainly from Univ. of Tokyo and Finance team

Univ. of Tokyo dep. of EngineeringProject manager at Japanese cabinet office quantum computing project

CEO

Yuichiro Minato Daisuke Saida

Application/Middleware Superconducting qubit

Goldman SachsMorgan StanleyColumbia UniversityUniv of Tokyo dep. of Engineering

Mitsubishi UFJ BankABC FinanceChuo Univ.

Finance Finance ManagementManagerCFO

Yoichi Takebayashi Hitoya NakamuraExecutive

Shinji Ishihara

ToshibaUniv. of Tokyo dep. of Engineering(Ph.D)

Tokyo Institute of TechnologyToshibaPwCC / IBMSony Global Solutions

MDR Awards, projects and mediaDomestic and worldwide

Start-up Showcase FinalistNVIDIA Inception Partner Microsoft for Startups

QNNcloudInno Vation (ministry of internal affairs and communications scope project)

MUFG Digital Accelerator second prize

Q-LEAPFlagship Project

Riken、Univ. of Tokyo、AICS、Toshiba、MDR、NEC、NTT、QunaSys

IPASJapan Patent Office

AQC2016@googleLA

AQC2017@Tokyo

AQC2018@NasaAmes

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QNNcloud

Hardware on superconducting and Application on both QA and Universal

https://quantumcomputingreport.com/

Flux Transmon

Clients

Bank

Insurance

Automotive

Heavy Industry

Trading

Telecom Career

Material company

Ministries

Universities,National Labs Over 20 Clients on QC

Applications

・Quantum Simulations

・Combinatorial Optimization

・Quantum Machine Learning

Business Area

・Finance

・Automotive

・Material and Drug discovery

Around 2,000 of QC Developers community in Japan

over 300-500 people in one event

2000 users offline1200 users online on slack

over 100 events in a year

OSS Python SDK for quantum computing

https://github.com/Blueqat/Blueqat

from blueqat.opt import Optc = Opt().add([[1,1],[1,1]]).add("(q0+q1)^2")

#annealingprint(c.run())[0, 0]

#D-Waveprint(c.dw())[0, 0]

#qaoaprint(c.qaoa().most_common(5))#=>(((0, 0), 0.7639901896866), ((1, 0), 0.10321404014639714), ((0, 1), 0.10321404014639707), ((1, 1), 0.029581730020605202))

from blueqat import vqefrom blueqat.pauli import qubo_bit as q

hamiltonian = -3*q(0)-3*q(1)-3*q(2)-3*q(3)-3*q(4)+2*q(0)*q(1)+2*q(0)*q(2)+2*q(0)*q(3)+2*q(0)*q(4)+2*q(1)*q(2)+2*q(1)*q(3)+2*q(1)*q(4)+2*q(2)*q(3)+2*q(2)*q(4)+2*q(3)*q(4)step = 2

result = vqe.Vqe(vqe.QaoaAnsatz(hamiltonian, step)).run()print(result.most_common(12))

---------------------

from blueqat.pauli import *

hamiltonian1 = (1.23 * Z[0] + 4.56 * X[1] * Z[2]) ** 2hamiltonian2 = (2.46 * Y[0] + 5.55 * Z[1] * X[2] * X[1]) ** 2hamiltonian = hamiltonian1 + hamiltonian2print(hamiltonian)

Ising model tutorial for

beginners on ipython

MUFG Bank, Ltd. and MDR made a contract on research on quantum computer・Portfolio Optimization

・Risk management

・Monte Carlo Simulations

・Crypto Security

For Banking Company

Routing optimization and AGVsFor Automotive Company

Nothing new we are always solving very standard problems.

Quantum Chemistry(Universal)First Principle Calculation on chemistry.

from blueqat import *

from openfermion import *

from openfermionblueqat import*

import numpy as np

x = [];e=[];fullci=[]

for bond_len in np.arange(0.2,2.5,0.1):

m = get_molecule("{:.2}".format(bond_len))

h =

bravyi_kitaev(get_fermion_operator(m.get_molecular_hamil

tonian()))

runner = vqe.Vqe(UCCAnsatz(h,6,Circuit().x[0]))

result = runner.run()

x.append(bond_len)

e.append(runner.ansatz.get_energy(result.circuit,runner.

sampler))

fullci.append(m.fci_energy)

%matplotlib inline

import matplotlib.pyplot as plt

plt.plot(x,fullci)

plt.plot(x,e,"o")

Quantum ClassicalQuantum CircuitQuantum Circuit

Quantum Circuit

opt

params

Variational Method

For Material Company

Gaming

[[x o x] [o x o] [o x o]]

SUDOKU TIC TAC TOE

a = Opt().add("10*(q0+q1+q2+q3+q4+q5+q6+q7+q8-4)^2-(q0+q1+q2)^2-(q3+q4+q5)^2-(q6+q7+q8)^2-(q0+q3+q6)^2-(q1+q4+q7)^2-(q2+q5+q8)^2",N=9).add(np.diag([-100,0,0,100,-100,0,100,-100,100])).run()

print(np.reshape(a,(3,3)))

TILING

a = opt.opt()a.qubo =opt.optm("(1-(q0+q1+q2+q3+q4+q5))^2+(1-(q6+q7+q8+q9+q10+q11+q12+q13+q14+q15+q16+q17+q18+q19+q20+q21))^2 +(1-(q22+q23+q24+q25+q26+q27+q28+q29+q30+q31+q32+q33+q34+q35+q36+q37))^2+(1-(q0+q3+q6+q14+q16+q22+q30+q32))^2+(1-(q1+q3+q7+q8+q14+q15+q16+q17+q23+q24+q30+q31+q32+q33))^2+(1-(q2+q3+q9+q15+q17+q25+q31+q33))^2+(1-(q0+q4+q6+q8+q10+q16+q18+q20+q22+q24+q26+q32+q34+q36))^2+(1-(q1+q4+q6+q7+q8+q9+q11+q12+q14+q17+q18+q19+q20+q21+q22+q23+q24+q25+q27+q28+q30+q33+q34+q35+q36+q37))^2+(1-(q2+q4+q7+q9+q13+q15+q19+q21+q23+q25+q29+q31+q35+q37))^2+(1-(q0+q5+q10+q12+q20+q26+q28+q36))^2+(1-(q1+q5+q10+q11+q12+q13+q18+q21+q26+q27+q28+q29+q34+q37))^2+(1-(q2+q5+q11+q13+q19+q27+q29+q35))^2",38)res = a.sa()print(res)print(np.where(np.array(res)==1)[0])

Finding Hadamard Matrices

by a Quantum Annealing Machine

Andriyan B. Suksmono, School of Electrical Eng. and Informatics, ITB, Bandung, Indonesia

Yuichiro MinatoMDR Inc., Tokyo, Japan

19

1. Introduction• Hadamard matrix (H-matrix)

• Definition: an orthogonal binary {-1,1} matrix• Applications: orthogonal codes used in CDMA, ECC

(Error Correction Code) with maximal error correction capability, employed in Mariner-9

• Scientific/Math: H-matrix conjecture is a ~100 years old unsolved problem

• Why finding a H-matrix is hard?• For an M-order matrix, there are [2^(M2)] ~ exp (M2) binary matrices

• H-matrix conjecture predicts, there is a H-matrix for every M=4k, k positive integer. How to find it?

• Brute force, worst-case condition: one should check all binary matrices, an O[exp(M2)] problem --> a hard problem.

• Proposed Solution: USE A QUANTUM COMPUTER ! Mariner-9 employed Hadamard’s ECC to protect Mars’s images sent to Earth

CDMA Communication System employs

Walsh-Hadamard Orthogonal Code

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2. Methods: (a) Use symbolic computing to formulate many-terms 2-body Hamiltonian

PROBLEMFind H ≅ min E{s

i}

s-domain k-bodyEnergy function

Ek(si)

Hamiltonian FormulationH

2({σ

iz})

q-domain k-bodyEnergy function

Ek(qi)

q-domain 2-bodyEnergy function

E2(qi)

s-domain 2-body Energy function

E2(si)

Alg.1

Alg.2

Alg.3

Alg.4

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3. Simulations: (a) Finding H-matrices• Finding 2-order H-matrix:

• NSWEEP=1000, Results: Energy and Configurations

Solutions

solution Minimum Energy:

E = -16.00

• Finding 4-order H-matrix:

H-matrixis found !

E=[-16.00 , -15.62, -15.62, -15.71, -15.71 -15.71, -15.71, -15.71, -16.00, -15.62 ]T

Simulate using neal

ExtractIsing Coeffs.

22

4. DW2000Q:(a) Finding H-matrices

• Finding 4-order H-matrix• Finding 2-order H-matrix

Minimum energy E= -322.91

default annealing Schedule

Minimum energy E = -13.52

manual qubits assignments in Chimera

embedding using SAPI

NREADS=1000

23

Conclusions and Further Directions1. Finding H-matrices (H-SEARCH) is a hard problem which potentially can

be solved by a quantum computer.

2. Construction of the Hamiltonians of H-SEARCH and its related problems needs manipulation of equation with large number of terms. We have managed this problem by symbolic computing.

3. We have implement H-SEARCH in a quantum annealer DW2000Q. Although only up to 4-order H-matrix, future generation of quantum annealers capable to implement higher orders due to increasing number of qubits and connectivity beyond Chimera.

4. In the forthcoming research, we will address the issue of scaling/speed-up and implementation of H-SEARCH in quantum gate model.

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About Business

Japanese Domestic market

Quite familiar to the quantum computer that almost all of the famous company is now

started to working on quantum computing project inside the company.

・Annealing Machine -> D-Wave, Fujitsu, Hitachi, NTT, etc…

・Universal Gate Machine -> IBM, Microsoft, Google cirq, Rigetti, etc…

A lot of choice for users at the moment for QC.

Thinking as a PoC project

Most of companies thinking a quantum computing project as PoC (Proof of Concept)

project.

The point is if QC is faster than the classical computer.

If the cost is lower than the classical computer.

If the QC can solve the classical one cannot solve.

Demand on QC is not strong

Some of the company has long-term vision for the QC projects but this is mostly

because the responsible person in the company is from physics or machine learning.

There are a lot of physics person in Japan.

BUT actually demand on “optimization problem” is very strong because Robotic Process

Automation is a trend now in Japan.

Cost & term for QC

For our business clients pay around $1,000 - $20,000 for one project.

Minimum 3month, the longest project is around 3 years to 5 years.

To do as a business we need a eco system and make the situation that QC get much

more result than the classical computer.

>>Still unclear on business but keep working on PoC

Japanese culture

Japanese language is almost necessary in Japanese market for documents and

tutorials.

Beginners never read english document.

Required Japanese support.

QC on everyday life

Optimization of taste

Combination of discrete algorithm without cost function & ising model, we optimizing

taste and preference of human behavior.

Conclusion

・PoC is now a trend

・The future vision is unclear

・Japanese people require Japanese language support

・People recently not expect on the good result

・There are many choice in Japan for qc platforms

・Combinatorial optimization problem is widely accepted

・Number of startup is around 5 to 7 (Most of them are Annealing).

Thank you for listening

Solve problem that human beings cannot solve