>> ALL RIGHT. LET'S GET STARTED. 
WELCOME EVERYONE. THIS IS THE FIRST 
OF SEVERAL SESSIONS ON QUANTUM COMPUTING. 
THIS IS VERY EXCITING TIME TO BE 
WORKING IN THAT FIELD. WE ARE KIND 
OF AT A JUNCTURE WHERE WE ARE EXPECTING 
TECHNOLOGICAL BREAKTHROUGHS VERY 
SOON AND ALLOW US TO SCALE UP THE 
SYSTEMS. THE FOCUS TODAY WILL BE 
ON THINGS THAT YOU CAN DO AS A DEVELOPER 
ALREADY NOW, IF YOU WANT TO GET 
ENGAGED WITH QUANTUM COMPUTING. 
YOU WILL HEAR ABOUT THE SOFTWARE 
THAT YOU CAN ALREADY PLAY WITH TODAY. 
ALSO EXCITING AS WE WILL HAVE TWO 
FOLKS HERE BESIDES ME, MY NAME IS 
MARTIN ROETTELER. I'M THE LEAD OF 
DEVELOPMENT AND TWO FOLKS REPRESENTING 
THE USER. AND FURSMAN IS HERE FROM 
THE COMPANY THAT IS USING OUR Q 
SHARP LIBRARIES AND HEAR ABOUT THESE 
EXPERIENCES. ALSO HAVE, WHAT IS 
VERY EXCITING, YOU CAN DO, YOU CAN 
GET INSPIRED BY QUANTUM MECHANICAL 
THINKING AND IMPROVE CLASSICAL ALGORITHMS. 
CLASSICAL AUTHORIZATION PROBLEMS. 
IN A SENSE, ZERO KUBERNETES AND 
HELMUT HERE LATER, TO TELL YOU ABOUT 
THOSE POSSIBILITIES. IT IS EXCITING 
BECAUSE YOU CAN REACH CUSTOMERS 
AND MAKE A TREMENDOUS IMPACT ON 
THE BUSINESS TODAY. I WANT TO GIVE 
YOU THAT QUICK SHOUT-OUT, THAT BUILD 
THAT WILL BE SIX SESSIONS IN TOTAL 
THAT WILL INVOLVE QUANTUM. YOU CAN 
HEAR ABOUT EXCITING NEW STUFF THAT 
WE OFFER Q SHARP NOTEBOOK, ZERO 
INSTALL EXPERIENCE. YOU CAN GET 
STARTED WITH Q SHARP WITHOUT INSTALLING 
ANY, THERE IS BASIC BACKGROUND OF 
QUANTUM COMPUTING AND SEVERAL SESSIONS 
ABOUT THAT. SESSIONS ABOUT THE TOOLS, 
INTEGRATE VISUAL STUDIO AND STUDIO 
CODE AND MORE STUFF ABOUT PYTHON. 
A LOT OF STUFF TO EXPLORE THIS WEEK 
. I WANT TO START WITH BIRD'S EYE 
VIEW. WHY EVEN CARE? NOT A UNIVERSAL 
TOOL THAT WILL SPEED UP EVERYTHING. 
IT IS CONTRARY TO DOMAIN SPECIFIC 
APPLICATIONS THAT ARE KIND OF RARE 
BUT WE CARE ABOUT THEM. BECAUSE 
THEY ARE COMPILATIONALLY VERY HARD. 
YOU FIND THEM IN COMPUTATIONAL CHEMISTRY. 
THE PROBLEMS THAT YOU WOULD LIKE 
TO SIMPLY UNDERSTAND NATURE IN A 
SENSE. UNDERSTAND PROBLEMS ABOUT 
THE ENERGY LEVELS OF A MOLECULE. 
AND PROBLEMS ABOUT CATALYSTS, HOW 
TO DESIGN CATALYSTS THAT HELPS THE 
CHEMICAL REACTIONS. AND CLASSICAL 
COMPUTERS REACH LIMITS THERE, MAYBE 
THE CLASSICAL METHOD IS FAST BUT 
GIVES. >> VERY INPRECISE AND INACCURATE 
ANSWER. OR PRECISE BUT DON'T SCALE 
WELL WITH THE SIZE OF THE SYSTEM. 
THERE ARE PROBLEMS LIKE THAT ARISING 
FOR INSTANCE CHEMISTRY. FERTILIZER 
DOESN'T SOUND LIKE THE MOST APPEALING 
OF THING, BUT THE DESIGN OF FERTILIZER, 
THE TYPE OF PROBLEMS ARISE, FERTILIZER 
IS MADE FROM SUBSTANCES WHICH ARE 
INITIALLY KIND OF MADE FROM HARVESTING 
NITROGEN FROM THE AIR, TURNING IT 
INTO COMPOUND FORM SUCH AS AMMONIA 
AND FURTHER PROCESS AND THAT FIRST 
STEP IN THE PROCESS IS DONE USING 
A PROCESS THAT IS KIND OF KNOWN 
FOR HUNDREDS OF YEARS. ABOUT A HUNDRED 
YEARS. BUT IT IS VERY INEFFICIENT. 
WE WOULD LIKE TO FIND A BETTER PROCESS. 
WE KNOW IT IS POSSIBLE TO DO IT 
IN KIND OF ANOTHER WAY, NATURE DOES 
THAT IN CERTAIN PLANTS. BUT WE HAVE 
TO DO IT AT HIGH PRESSURE, HIGH 
TEMPERATURES, AND JUST WASTES A 
LOT OF ENERGY. WHEN PEOPLE ESTIMATE 
ABOUT 2 OF GLOBAL ENERGY OUTPUT 
GOES INTO THAT ONE PROCESS OF MAKING 
AMMONIA. IF YOU FIEND A BETTER WAY, 
TREMENDOUS IMPACT ON HUMAN KIND. 
SO SIMILAR APPLICATIONS, WE ANTICIPATE 
IN, FOR INSTANCE, CAPTURING CARBON 
FROM THE AIR. THERE ARE PROCESSES 
THAT DO IT ALREADY NOW BUT VERY 
INEFFICIENT. IF THERE WAS A BETTER 
WAY, THAT WOULD HAVE TREMENDOUS 
IMPACT. QUANTUM COMPUTERS HAVE THE 
GREAT PROMISE OF BEING A TOOL TO 
HELP US WITH THAT. MATERIAL SCIENCE 
SIMILARLY A LOT OF APPLICATIONS 
AND THE, ONE OF THE PROMISING AREAS 
FOR QUANTUM COMPUTERS ALSO MACHINE 
LEARNING APPLICATIONS. WE DON'T 
HAVE, WE HAVE NOT REALLY FOUND A 
KILLER APP YET. WE HAVE SOME HUNCHES 
OF THINGS THAT QUANTUM COMPUTER 
COULD HELP US THERE. FOR INSTANCE, 
WE KNOW THAT QUANTUM COMPUTER IS 
GOOD AT COMPUTING CORRELATIONS, 
AND COMPUTING FREE TRANSFORMS AN 
AMPLIFY CERTAIN PROGRAMMABILITYS. 
HISTORICALLY, THE FIRST APPLICATION 
REALLY WERE IN AREA CLOSER TO TYPOGRAPHY. 
VERY MATHEMATICAL PROBLEM SUCH AS 
FACTORING OF NUMBERS. THIS IS PRETTY 
BIG NUMBER. IT HAS 2, 000 BIT, ROUGHLY 
600 DES MEL PLACES. ONE OF THE RSA 
NUMBERS. THE PROBLEM OF FACTORING, 
OR THE HARDNESS OF FACTORING IS 
REALLY UNDERLYING A LOT OF CRYPTO 
VIEWS THESE DAYS. TURNS OUT ON A 
QUANTUM COMPUTER, YOU CAN FACTOR 
NUMBERS RELATIVELY EASY. YOU STILL 
HAVE TO BUILD A MACHINE THAT IS 
QUITE SIZABLE. FACTOR FOR INSTANCE, 
2, 000 BIT NUMBER, YOU NEED 4, 000, 
ROUGHLY 4, 000 QUANTUM BIT, Q BITS, 
AND THOSE Q BITS HAVE TO BE REALLY 
GOOD. CORRECTED. IF YOU LOOK AT 
THE HEADLINE NUMBERS, YOU MIGHT 
FIND IN THE PRESS, THOSE ARE NOT 
CUBICS. WE NEED AROUND 4, 000, HAVING 
4, 000 WOULD ALLOW US TO FACTOR 
IN NUMBER, INSTEAD OF A BILLION 
YEARS WHICH IS THE BEST CLASSICAL 
ALGORITHM THAT DO IT IN THAT TIME, 
WE COULD DO IT UNDER SOME ASSUMPTION 
OF THE CLOCK SPEED. IN A SECOND 
OR SO. AND THE SAME THING IS TRUE 
FOR OTHER METHOD, MIGHT HAVE HEARD 
ABOUT TYPOGRAPHY, OUR TEAM IN REDMOND 
ANALYZED THOSE AND FOUND SIMILAR 
COST ESTIMATES IN TERMS OF CUBICS 
YOU WOULD NEED. AND THE NUMBER OF 
OPERATIONS YOU NEED. AND ALSO BREAK 
THAT. THAT IS INDICATION THAT USED 
IN A LOT OF BLOCKCHAIN TECHNOLOGIES. 
WE HAVE TO CHANGE THAT TECHNOLOGY 
TO ANOTHER TECHNOLOGY. THE GOOD 
NEWS IS THAT THERE ARE PROPOSALS 
HOW TO REPLACE CRYPTOGRAPHY WITH 
QUANTUM SAFE FORM. A BIG SHOUT-OUT 
TO THE GROUP THAT BRIAN RUNS AT 
MSR. RIGHT NOW, THERE IS AN EFFORT 
GOING ON BY THE U. S. GOVERNMENT, 
SO THEY HAVE A CALL FOR NEW CRYPTO 
SYSTEMS AND MICROSOFT HAS SEVERAL 
CONTAINERS IN THAT RACE. IT IS INTERESTING 
FIELD LIKE HOW CRYPTO SHIFTS TO 
ITS QUANTUM. FEAR NOT, AS THERE 
WILL BE NEW KINDS OF CRYPTO, EVEN 
WHEN QUANTUM COMPUTER IS AROUND 
. CRYPTO IS NOT A GOOD BUSINESS 
CASE. HOW MANY CRYPTO APPLICATIONS 
WILL YOU BE ABLE TO SELL? MAYBE 
A FEW, MAYBE SOME GOVERNMENT AGENCIES 
CARE ABOUT IT. IT IS NOT REAL BUSINESS 
CASE TO MAKE A LOT OF MONEY FROM 
IT. SO TO UNDERSTAND THAT A LITTLE 
BIT BETTER, LET'S TRACK BACK AND 
THINK ABOUT LIKE WHAT CAN WE LEARN 
FROM NATURE REALLY? SO KIDS ARE 
GREAT WITH THAT. KIDS ASK US QUESTIONS 
LIKE, WHY IS GRASS GREEN? LEAVES 
GREEN? WHY IS THE SKY BLUE? WE DON'T 
THINK ABOUT IT MUCH, WHY? BUT REALLY 
THERE ARE REASONS FOR THAT, THAT 
ARE FUNDAMENTALLY QUANTUM MECHANICAL. 
UNDERSTANDING THE SKY, YOU HAVE 
TO UNDERSTAND SCATTERING OF LIGHT. 
AND SO ON. SO REALLY WHAT WE TRY 
TO DO IN QUANTUM COMPUTING WE LEARN 
FROM NATURE AND HARVEST THAT AND 
TURN IT INTO APPLICATIONS. AND THE 
PRIME EXAMPLE IS, QUANTUM CHEMISTRY. 
SO IN A NUTSHELL, WHAT GOES ON IS, 
YOU HAVE A SYSTEM, LIKE A MOLECULE, 
NOW YOU TRY TO MODEL THAT AND CHEMISTS 
HAVE BEEN DOING THAT FOREVER. IT 
IS A TYPICAL WAY TO MODEL IT. IT 
IS TO IGNORE MOST OF THE STRUCTURE 
OF A MOLECULE IN JUST FOCUSING ON 
THE ELECTRONIC STRUCTURE. THEN YOU 
ARRIVE AT THE CERTAIN MATRIX, CALL 
IT HEMO IT ONA. UNDERSTAND THE CHEMISTRY 
STRUCTURE. UNDERSTAND. TYPICALLY 
ONLY THE LOWEST ONE. INFORMS US 
A LOT ABOUT THE PROPERTIES OF THAT 
MOLECULE. AND QUANTUM COMPUTER IS 
REALLY GOOD AT THAT. WHY? BECAUSE 
IT IS A CONTROLLED QUANTUM MECHANICAL 
SYSTEM. IF YOU HAVE ANOTHER QUANTUM 
MECHANICAL SYSTEM, YOU CAN MAP IT 
TO YOUR CONTROL SYSTEM AND STUDY 
THE TIME EVOLUTION OF THAT SYSTEM 
WHICH IN TURN INFORMS YOU ABOUT 
THE SYSTEM THAT YOU CARE ABOUT. 
BUT IT IS NOT AS SIMPLE AS THAT. 
THAT HAS BEEN KNOWN FOR QUITE SOMETIME 
THAT SOME TIME REVOLUTION IS SOMETHING 
QUANTUM REVOLUTION IS GOOD AT. THERE 
IS A CATCH, EVEN IF YOU HAVE AN 
EFFICIENT WAY IN A SLOWDOWN, THAT 
CAN QUITE LIKELY KILL YOU IF THE 
DEGREE OF IT IS TOO HIGH. THE FIRST 
ALGORITHM IN THAT SENSE WERE ACTUALLY 
THE SCALING OF ENTER 11. ENTER THE 
PROBLEM SIZE, THE NUMBER OF SPATIAL 
FUNCTIONS MUCH SUCH AN ALGORITHM, 
YOU DID A MOLECULE THAT PEOPLE ARE 
REALLY CARE ABOUT, SUCH AS FOR NITROGENIZE, 
RELATED TO THE CATALYST PROBLEM 
OF HARVESTING NITROGEN. THEN STILL 
END UP WITH A RUN TIME OF BILLION 
OF YEARS. IT IS KIND OF SLOW. FOR 
PEOPLE IN THE TEAM, MICROSOFT, HERE 
IN REDMOND AND SEVERAL IN THE AUDIENCE, 
THERE WERE OTHER PEOPLE, DAVE, AND 
SEVERAL OTHERS, WORKED ON CHIPPING 
AWAY SLOWLY THE COMPLEXITY OF THAT 
ALGORITHM. AND FINALLY BROUGHT IT 
DOWN TO SOMETHING THAT COULD ACTUALLY 
BE DONE IN A, SAY, REALISTIC QUANTUM 
COMPUTER HAVING A REALISTIC CLOCK 
SPEED. THE POINT HERE IN THAT SLIDE 
IS, IN QUANTUM WORLD, WE HAVE SIMILAR 
THINGS GOING ON LIKE IN A CLASSICAL 
WORLD. WE NEED TO HAVE QUANTUM ALGORITHMIC 
THINKING. WE NEED TO BE AWARE OF 
THE RUN TIME. JUST HAVING POLYNOMA 
IS NOT ENOUGH. WE NEED TO DEVELOP 
TECHNIQUES THAT REDUCE THE COMPLEXITY. 
ULTIMATELY, WE WANT TO PROGRAM THESE 
ALGORITHMS. WE WANT TO KIND OF BE 
ABLE TO, EVEN NOW, WANT TO BE ABLE 
TO WRITE THE PROGRAMS THAT WE CAN 
LATER RUN WHEN THE QUANTUM COMPUTER 
SCALES UP TO THAT SIZE. WE WANT 
TO HAVE THE PROGRAMS READY, UNDERSTAND 
THEM BEFORE, SO THAT WE CAN FIND 
KIND OF BOTTLENECKS WE WANT TO PROFILE 
THEM. AND IDENTIFY GOOD ALGORITHMS. 
SO THAT IS PART OF THAT SLIDE. REALLY 
THAT MOLECULE IS ONE PEOPLE CARE 
ABOUT A LOT IN, LIKE NITROGEN FIXATION. 
IT IS PART OF A REALLY, REALLY BIG 
MOLECULE. CALLED THE ACTIVE SIDE 
IS JUST VISUALIZED HERE, MUCH, MUCH 
SMALLER THAN THE ENTIRE COMPLEX. 
AND THIS, THIS TEAM HAS WORK THAT 
WAS PUBLISHED A FEW YEARS AGO. WE 
FOUND IN A FEW HOUR, LARGE SCALE, 
WE COULD SOLVE THAT PROBLEM. SO 
AT MICROSOFT, WE TRY TO BUILD AN 
ENTIRE END TO END STACK FOR QUANTUM 
COMPUTING. SO IT STARTS WITH THE 
KIND OF APPLICATION LAYER, SOFTWARE 
LAYER, WHERE YOU EXPRESS THE HIGH 
LEVEL ALGORITHMS. THEN IT GOES ALL 
THE WAY TO QUANTUM DEVICE AND IT 
NEEDS TO HAVE ANOTHER LAYER THAT 
IS THE GLUE BETWEEN THESE TWO EXTREMES. 
IT IS A LOT OF CLASSICAL CONTROL 
THAT NEEDS TO HAPPEN. WE HAVE THIS 
END TO END STACK VIEW THAT WE WANT 
TO REALLY DEVELOP ALL OF THE COMPLEMENTS. 
IN THIS TALK, I'LL FOCUS ON THE 
TOP LEVEL COMPONENT AND PRESENT 
YOU SOFTWARE. SO THE SOFTWARE IS 
BASED ON THE LANGUAGE CALLED Q SHARP. 
AND LIKE C SHARP AND F SHARP, IT 
DRAWS ON SOME MULTIPLE PARADIGMS 
OF PROGRAMMING. IT HAS FUNCTIONAL 
FLAVORS TO IT. IT ALLOWS YOU TO 
EXPRESS MANY IDEAS THAT YOU MIGHT 
HAVE FOR QUANTUM ALGORITHMS AT A 
VERY HIGH LEVEL. THAT IS ONE OF 
THE ASPIRATIONS ALLOWING HIGH LEVEL 
PROGRAMMING WHERE YOU DON'T HAVE 
TO WORRY ABOUT BIT LEVEL OPERATION 
SO MUCH. NOBODY REALLY LIKES TO 
DEAL WITH ASSEMBLER, UNLESS YOU 
CARE ABOUT PERFORMANCE, WHICH WE 
CAN ALSO DO BUT MOST OF THE TIME 
YOU JUST WANT THE IDEA EXPRESSED. 
ALSO WE CARE ABOUT PROBLEMS AT SCALE. 
WE DON'T WANT TO JUST LOOK AT TOY 
PROBLEMS, OR LIKE, KIND OF JUST 
NOT REALISTIC PROBLEMS. WE REALLY 
WANT TO STUDY THE PROBLEMS AT SCALE 
THAT WOULD HAVE AN IMPACT AND MOVE 
THE NEEDLE. THAT DRAWS US TO CERTAIN 
DESIGN IN THE LANGUAGE, KIND OF 
A REUSABLE DESIGN AND BE TARGET 
LARGE SCALE. WE CAN HANDLE ANY SCALE. 
LIBRARIES, THEY ARE OPEN SOURCE. 
WE CAN, PEOPLE CAN CONTRIBUTE. PEOPLE 
CAN EXPERIENCE THE ENTIRE LIBRARIES. 
WE TRIED TO GIVE SOME GUIDANCE WHAT 
WE ARE GOING TO DO NEXT WITH LIBRARIES 
AND YOU CAN PLAY WITH FUNDAMENTAL 
LIBRARYS. IN A MOMENT, YOU CAN SEE 
ONE OF THE MAIN WORK HORSES, CHEMISTRY 
LAB. WE HAVE VISUAL STUDIO. THE 
DOCS. WE INFLUENCE THE DOCS TEAM. 
BECAUSE A LOT OF THAT STUFF IS MATHEMATICAL. 
IN MATH, IT IS LANGUAGE CALLED LATEC, 
THAT SOME OF YOU MAY HAVE HEARD. 
DOC TEAM SUPPORTS MATH JACKS RENDERING 
WHICH IS COOL. THIS WHOLE THING 
RUNS ON DOC NET CORD. INITIAL RELEASE, 
FIRST RELEASE, THEY WERE FRAMEWORK. 
THEN WE SWITCH CORE, MULTI PLATFORM 
OF CORE. RUNS ON WINDOW, MAC AND 
LINUX. ROUGHLY THE VERY HIGH LEVEL 
PICTURE OF HOW A Q SHARP PROGRAM 
LOOKS LIKE IS LIKE THIS. REALLY 
THERE IS A DRIVER WHICH TYPICALLY 
IS WRITTEN IN C SHARP. IT COULD 
BE WRITTEN IN F SHARP AS WELL. AND 
THEN THE, THAT EXPRESSES THE CLASSICAL 
COMPUTATIONS HERE THAT YOU MIGHT 
DO ON THE SIDE. THE QUANT YUM MECHANICAL 
ONES ARE THE TWO KINDS. WE DISTINGUISH 
BETWEEN OPERATIONS AND FUNCTIONS. 
OPERATIONS JUST HAVE SIDE EFFECT 
ON THE QUANTUM DATA AND OPERATE 
UPDATED. FUNCTIONS ARE THINGS THAT 
YOU WANT TO DO ON THE SIDE BUT THEY 
ARE NOT MEANT TO BE COMPLICATED 
OPERATIONS. JUST SMALL SIDE OPERATIONS. 
SIDE COMPUTATIONS. THE IDEA IS THAT 
THOSE THINGS SHOULD MAP TO SOMETHING 
THAT YOU COULD DO WITH ONE THAT 
SITS CLOSE TO THE QUANTUM PROCESSOR. 
THOSE ARE DONE BY THE CONTROL. PART 
OF THAT. THAT IS THE ROUGH IDEA. 
AND WE CAN TARGET THEN ULTIMATELY 
WE WANT TO TARGET THE HARDWARE. 
IN THE MEANTIME, TARGET SIMULATORS 
AND WE HAVE KIND OF SIMULATORS WORK 
RECENTLY FAST UP TO 30 CUBICS OR 
SO. THEN YOU REALLY SUFFER FROM 
PAIN AND EXPEDIENTIAL OVERHEAD OF 
SIMULATING QUANTUM SYSTEM. AND EXPEDIENTIAL 
NUMBER OF CUBICS. YOU CAN ALREADY 
USE A PROFILER. IT IS COMPLETELY 
SCALABLE. JUST COUNTS HOW MANY CUBIC, 
AND GATES I HAVE AND OTHER METRICS 
SUCH AS THE DEPTH, THE CIRCUIT DELAY. 
THINGS YOU CAN DO TODAY. YOU CAN 
DO IT AT SCALE. YOU CAN WRITE REALLY 
LARGE SCALE ALGORITHMS AND BASICALLY 
PUSH A BUTTON AND YOU GET THAT PROFILING 
INFORMATION. IT IS REALLY FOR MATH, 
REALLY COOL. AND YOU DON'T HAVE 
TO REWRITE YOUR CODE. YOU SAVE YOUR, 
PROTECT YOUR INVESTMENT MUCH THE 
CODE YOU WRITE ONCE AN EXPLORE TODAY, 
YOU ULTIMATELY WILL BE ABLE TO RUN 
ON THE QUANTUM DEVICE. AND BIG NEWS 
OF TODAY, SOME OF YOU MIGHT HAVE 
SEEN IT. SO WE HAD ALREADY THE LIBRARIES 
AND THE SAMPLES WERE OPEN SOURCE, 
WE NOW ANNOUNCED TODAY THAT WE ARE 
GOING TO OPEN SOURCE THE Q SHARP 
COME PILER AS WELL. THE TEAM WILL 
OPEN SOURCE THAT AS WELL. THE SIMULATORS 
WILL BE OPEN SOURCED. LIBRARIES 
SAMPLES AND SO-CALLED CARTOUS IS 
OPEN SOURCE. IT IS A COOL CONCEPT. 
SELF-PACED TUTORIALS GOING FROM 
SIMPLE THINGS TO MEDIUM COMPLEXITY 
THINGS. AND THEY ALLOW YOU TO GET 
STARTED WITH Q SHARP REALLY FAST. 
SO IT IS REALLY A COMMUNITY THING. 
SHARE YOUR IDEAS, COLLABORATE AND 
ENGAGE AND DRIVE FOR THE QUANTUM 
REVOLUTION. OKAY. IF YOU WANT TO 
LEARN MORE, PLEASE EXPLORE OUR PRESENCE 
ON GITHUB AND THE DOC PAGES AND 
THIS MAIN QUANTUM PAGE. NOW I WANT 
TO SHIFT GEARS AND MENTION THAT 
WE HAVE A CHEMISTRY LIBRARY. THAT 
CHEMISTRY LIBRARY ALLOW US TO CAPTURE 
THE PROBLEMS WE MENTIONED EARLIER, 
LIKE THE NITROGEN MAZE AND THE CATALYST 
PROBLEM, FOR INSTANCE. YOU CAN MODEL 
THAT, FOR THAT, YOU NEED TO TYPICALLY 
A CHEMISTRY MODELING SOFTWARE. THESE 
SOFTWARES ARE KIND OF COMPLICATED. 
WE PARTNERED UP WITH INDUSTRIAL, 
WITH THE GOVERNMENT, SORRY, THE 
GOVERNMENT RESEARCH LAB CALLED PNNL. 
PACIFIC NORTHWEST LABS, IN WASHINGTON 
STATE. THEY HAVE A SOFTWARE THAT 
IS KIND OF A SOFTWARE CONSORTIUM 
PART OF, CALLED NW CHEM. THAT ALLOWS 
CHEMISTRY MODELING. COMPLEX SOFTWARE. 
WE BUILD INTERFACE SOFTWARE TO GENERATE 
QUANTUM SOFTWARE THAT WE EXECUTE 
IN OUR FRAMEWORK THAT WILL EVENTUALLY. 
>> , EVENTUALLY ALLOW YOU TO UNDERSTAND 
AND POSSIBLY UPDATING YOUR CHEMISTRY 
MODELS. SO THAT, THIS LIBRARY ALLOWS 
YOU TO DO THAT. WE NOW ARE OFFERING 
TO THE WORLD AND PEOPLE ARE USING 
IT STARTING TO EXPLORE IT. AND I'M 
VERY EXCITED TO ANNOUNCE OUR NEXT 
SPEAKER ANDREW FURSMAN FROM ONE 
QUEBEC. IT IS A START-UP BASED IN 
VANCOUVER CANADA. OUR SPEAKER TOOK 
THE WATER PLANE DOWN FROM VANCOUVER 
TO HERE. WHAT IS COOL IS, THEY ACTUALLY 
EVALUATING THAT LIBRARY. KICKING 
THE TIRES. TELLING US THE THINGS 
THAT WORK WELL. AND THE THINGS THAT 
DON'T WORK SO WELL. AS PART OF THAT 
LEARNING EXPERIENCE, WE TRY TO MAKE 
THE LIBRARY BETTER AND HAVE A GREAT 
OFFERING FOR OUR CUSTOMERS. SO ANDREW 
IS THE CEO OF THE COMPANY ONE CUBIC. 
HE WORKED IN THE VENTURE CAPITALIST 
SPACE FOR A LONG TIME. HE HAS START-UPS 
THAT WORK IN TELESECTOR, SATELLITE, 
FINANCIAL SECTOR AND HE LEADS THE 
COMPANY. HE WILL SAY A LITTLE BIT 
ABOUT THE COMPANY. JUST GIVE HIM 
A WARM WELCOME. [ APPLAUSE ] >> 
THANKS VERY MUCH. THANK YOU. >> 
ALL RIGHT. THANK YOU VERY MUCH FOR 
HAVING ME. YEAH, I WILL TELL YOU 
A LITTLE BIT ABOUT ONE CUBIC TO 
GIVE YOU CONTEXT. WE ARE ABOUT 6-YEAR-OLD 
COMPANY. JUST OVER A HUNDRED PEOPLE. 
WE RAISED ENOUGH MONEY THAT WE CAN 
ACTUALLY FOCUS ON SOME OF THE SLIGHTLY 
LONGER TERM TECHNOLOGIES BUT NOT 
SO MUCH THAT WE DON'T CARE AT ALL 
ABOUT RIGHT NOW. SO I THINK THAT 
IT IS KIND OF A PERFECT SEGUE FOR 
THE KIND OF DIFFERENT DEVELOPMENTAL 
TOOLS THAT WE ARE TALKING ABOUT 
TODAY. WE STARTED OFF DOING A LOT 
OF IP DEVELOPMENT. AND NOW WE ARE 
MOVING MORE INTO COMMERCIALIZATION. 
AND OUR REAL INTEREST IS IN SITTING 
HERE BETWEEN THE DIFFICULT INDUSTRIAL 
PROBLEMS THAT WERE ELUDED TO PREVIOUSLY 
AND THE DIFFERENT HARDWARE DEVELOPMENT 
SYSTEMS AND THE TOOLS THAT ARE AVAILABLE 
TO BE ABLE TO TACKLE THESE PROBLEMS 
THAT ARE NOW BEING DEVELOPED. AND 
WE HAVE REALLY, REALLY LIKED WHAT 
MICROSOFT HAS BEEN BRINGING OUT. 
IT HAS ALLOWED US TO DO A LOT OF 
THE WORK MUCH SOONER THAN MANY PEOPLE 
HAVE EXPECTED. WE ARE INTERESTED 
IN TALKING A LITTLE BIT ABOUT QUANTUM 
COMPUTING. NOT JUST BECAUSE OF THE 
FACT THAT WE THINK THAT IT IS INTERESTING, 
BUT BECAUSE SOME PEOPLE WAY ABOVE 
OUR PAY GRADES THINK IT IS ESPECIALLY 
INTERESTING. SORT OF COMMANDED TO 
CARE ABOUT QUANTUM COMPUTING ABOUT 
ONE OF THE FEW DEVELOPMENTS THAT 
WILL REALLY BE CHANGING WHAT IS 
HAPPENING OVER THE NEXT FEW YEARS. 
AND THIS IS ACTUALLY BEEN A GREAT 
BOOST AS YOU HAVE SEEN MANY PEOPLE 
STARTING TO TALK ABOUT THIS MORE 
PUBLICLY, BUT I THINK THAT THERE 
IS STILL A PRETTY SIZABLE GAP BETWEEN 
THE WORD QUANTUM COMPUTING AND WHAT 
PEOPLE REALLY ARE DOING WITH QUANTUM 
COMPUTERS AND WHAT THEY HOPE TO 
DO WITH THESE TECHNOLOGIES AS THEY 
COME ON BOARD. FOR ME, THE EASIEST 
WAY TO UNDERSTAND THE LITTLE WAY 
ABOUT WHAT WE ARE TRYING TO DO WITH 
QUANTUM COMPUTING AND WHY WE CARE 
ABOUT IT, LOOK AT THESE PHOTOS. 
WHAT I THINK IS ESPECIALLY INTERESTING 
ABOUT THIS IS NOT JUST THE KIND 
OF LOOKS LIKE A MICRO BREWERY. BUT 
MORE OF THE FACT THAT ANYBODY WHO 
IS WORKING IN A LABORATORY THAT 
IS ANYTHING FROM A MICRO BREWERY 
UP TO KIND OF CUTTING EDGE CHEMISTRY 
WORK RIGHT NOW, PROBABLY RECOGNIZES 
THIS. THEY RECOGNIZE IT BECAUSE 
THIS ISN'T THAT DIFFERENT FROM HOW 
WE DO CHEMISTRY TODAY. AND THE REASON 
THAT IS A BAD THING, IT IS BECAUSE 
ALMOST EVERY OTHER INDUSTRY THAT 
WE CAN THINK ABOUT HAS REALLY TRANSITIONED 
TO SOMETHING MORE LIKE THIS. THE 
WAY THAT I LIKE TO SORT OF DRAW 
AN ANALOGY IS, IT IS NOT A VERY 
GOOD IDEA FOR BEAUING -- BOEING 
TO MAKE A BUNCH OF PLANES ANTHRO 
OFF CLIFFS TO SEE WHICH ONE FLIES. 
THAT IS THE IS STATE WE ARE IN WITH 
A LOT OF CHEMISTRY RIGHT NOW. THE 
REASON WE ARE IN THAT STATE, BECAUSE 
COMPUTERS ARE NOT PARTICULARLY WELL 
SUITED FOR THIS TASK. WE HAPPEN 
TO BE JOINED IN THE AUDIENCE TODAY 
BY MA IT IES, GREAT QUOTE, IF WE 
WANT TO ACTUALLY DO THE WORK WE 
DO IN THE LABS IN CHEMISTRY, WITH 
OUR COMPUTERS, IT IS NO PROBLEM 
AS LONG AS WE HAVE A PROBLEM THAT 
IS ROUGHLY THE SIZE OF OUR GALAXY. 
BUT SINCE I DON'T OWN THAT MACHINE 
JUST YET, AND BECAUSE WE DO NEED 
TO MAKE A PROFIT EVENTUALLY WITH 
THE COMPANY, YOU KNOW, WE ARE REALLY 
INTERESTED IN TRYING TO UNDERSTAND 
IF THERE ARE BETTER WAYS FORWARD. 
AND THERE REALLY ARE BETTER WAYS 
FORWARD. THERE ARE THREE MAJOR THINGS 
THAT I THINK ARE OF INTEREST. FIRST 
IS QUANTUM INSPIRED OPTIMIZATION 
OPERATIONS. WE'LL HEAR MORE ABOUT 
THAT IN A MOMENT. BASICALLY, RECAST 
YOUR PROBLEM IN A FORM THAT MAKES 
IT AMENABLE TO QUANTUM PROCESSING 
IS ALREADY HELPFUL ESPECIALLY IF 
YOU THEN HAVE DEVICES THAT ARE DESIGNED 
TO COMPUTE TO SOLVE THOSE SORTS 
OF PROBLEMS DIRECTLY. THE OTHER 
THING THAT YOU REALLY WANT IS A 
PATH TOWARDS THE SCALABLE QUANTUM 
COMPUTER. AND FINALLY YOU WOULD 
LOVE TO HAVE A DEVELOPMENT ENVIRONMENT 
THAT ALLOWS YOU TO TAKE ADVANTAGE 
OF BOTH OF THESE THINGS. THE NICE 
THING IS, WITH MICROSOFT, YOU HAVE 
A ONE STOP SHOP FOR THAT. THEIR 
PROJECT CATAPULT IS REALLY GREAT 
QUANTUM INSPIRED PLATFORM THAT WE 
WILL BE ABLE TO USE IN ORDER TO 
DEVELOP APPLICATIONS THAT CAN RUN 
TODAY SOLVING SOME OF THE DIFFICULT 
PROBLEMS MUCH THEN OF COURSE, THE 
HOLY GRAIL OF ALL OF THIS IS THE 
SCALABLE TOPOLOGICAL PROCESSOR THAT 
THEY ARE WORKING ON. SPECIFICALLY, 
THE Q SHARP ENVIRONMENT IS A GREAT 
WAY TO BUILD APPLICATIONS THAT WILL 
EVENTUALLY RUN ON THE SCALABLE PROCESSORS 
THAT WE CAN UTILIZE THE CODING ENVIRONMENT 
AND THE SEM LATER IN ORDER TO PROVE 
THOSE THINGS OUT TODAY. ONE CUBIC, 
WE SPEND A LOT OF OUR TIME THINKING 
ABOUT CHEMISTRY. NEAR TERM APPLICATIONS. 
ONE OF THE THINGS THAT WE DO AS 
WE PARTNER UP WITH LARGE CHEMISTRY 
COMPANIES IN ORDER TO WORK ON THE 
PROBLEMS THAT WE THINK THAT ARE 
GOING TO BE THE FIRST THINGS THAT 
GET OFF LOADED FROM IN THE LAB TO 
IN COMPUTER MANY AND SO ONE OF THE 
TYPES OF PROBLEMS THAT EVERYBODY 
IS REALLY EXCITED ABOUT, IT IS THE 
DEVELOPMENT OF NEW TYPES OF CATALYSTS, 
BASICALLY SAYING, IF YOU ARE TRYING 
TO DO SOMETHING LIKE DRY PAINT, 
WHAT CAN YOU TOSS INTO THAT PAINT 
TO MAKE THE PAINT DRY FASTER? AND 
EVEN THOUGH THAT SOUNDS LIKE A MOST 
BORING PROBLEM YOU COULD POSSIBLY 
IMAGINE, WATCHING PAINT DRY IS ACTUALLY 
OF SUPER HIGH VALUE PROBLEM. AND 
SOMETHING THAT WE WILL BE ABLE TO 
DO REALLY, REALLY EARLY ON WITH 
THESE QUANTUM DEVICES TO BE ABLE 
TO SIMULATE WHICH THINGS MIGHT IMPROVE 
THAT PROCESS. SO WE HAVE ACTUALLY 
PUT TOGETHER A QUICK DEMO. THIS 
ISN'T EXACTLY THAT PROBLEM BUT THIS 
IDEA OF THE SIMILARET RICK STRETCH 
OF A HYDROGEN RING IS LIKE A PROBLEM 
THAT HAS ALL OF THE SAME SORTS OF 
FEATURES THAT YOU WOULD EXPRESS 
WHEN YOU ARE TRYING TO SOLVE THE 
PROBLEM OF EXPERIMENTING, TO FIND 
NEW TYPES OF CATALYSTS. IDEALLY, 
WHAT YOU WANT TO DO IS BE ABLE TO 
KNOW IF YOU ARE GOING TO MAKE THOSE 
RINGS BE DIFFERENT SIZES, THEN YOU 
WANT TO BE ABLE TO UNDERSTAND WHAT 
IS THE ESSENTIALLY THE LOWEST ENERGY 
STATE AND WHAT SOURCE OF CHARACTERISTICS 
WILL THESE DIFFERENT THINGS HAVE. 
AND TO KAL -- CALCULATE THAT, YOU 
ARE ABLE TO FIRE UP Q SHARP AND 
LOAD UP SOME OF THE CHEMISTRY THINGS 
WE TALKED ABOUT BEFORE. HERE WE 
HAVE INTERFACED SOMETHING LIKE QUANTUM 
SOLVER, BUT ESSENTIALLY IT IS REALLY, 
REALLY INTERESTING THAT ALL OF THIS 
IS PUT TOGETHER IN PACKAGES THAT 
EXIST RIGHT NOW. AND THAT Q SHARP 
ENVIRONMENT IS GREAT FOR BEING ABLE 
TO SPEAK NATIVELY IN THE LANGUAGE 
OF THE QUANTUM PROCESSOR. BUT ON 
TOP OF THAT, YOU CAN THEN ACCESS 
YOUR Q SHARP FROM A FAMILIAR LANGUAGE 
LIKE PYTHON. SO THIS, FOR EXAMPLE, 
IS A WAY FOR US TO BE ABLE TO CALL 
THAT CODE RIGHT OUT OF OUR FRIENDLY 
PYTHON ENVIRONMENT FROM ONE OF OUR 
JUPITER NOTEBOOKS. IN THE END, YOU 
CAN ACTUALLY RUN A LITTLE PROGRAM. 
SO WHAT I LIKE ABOUT THIS PAGE HERE, 
IT IS IF YOU LOOK AT THAT DARK BLACK 
LINE DOWN THE MIDDLE, THAT IS THE GROUND TRUTH 
OF WHAT THE REAL ENERGY STATE OF 
THIS SYSTEM IS. IF YOU LOOK AT THE 
BLUE DOTTED LEAN AND THE GRAY DOTTED 
LINE, THIS IS SORT OF THE ESTIMATED 
WAYS THAT WE WOULD USE TO TRY TO 
USE A CLASSICAL COMPUTER TO BE ABLE 
TO SIMULATE THIS RIGHT NOW. AND 
UNFORTUNATELY, THEY ARE JUST NOT 
ACCURATE ENOUGH TO BE USABLE. BUT 
WITHOUT EVEN HAVING A QUANTUM COMPUTER 
AVAILABLE TODAY, BY FRAMING THE 
PROBLEM IN A WAY THAT SORT OF THINKING 
ABOUT THIS AS A QUANTUM CHEMISTRY 
PROBLEM FOR QUANTUM COMPUTERS, BY 
UTILIZING THE SOFTWARE TOOLS THAT 
MICROSOFT HAS PROVIDED, AND BY ALLOWING 
US TO USE THESE SIMULATOR, WE HAVE 
BEEN ABLE TO WRITE SOFTWARE THAT 
WE CAN PUT TOGETHER, RUN THROUGH 
THE PIPELINE THAT WE DISCUSSED AND 
ALL OF THE DATA POINTS THAT CAME 
OUT, THESE RED X'S HERE THAT REALLY, 
REALLY DO A GREAT JOB OF SUMMARIZING 
THAT CURVE. SO THIS IS EXACTLY THE 
KIND OF STUFF THAT WE CAN ALREADY 
DO TODAY BUT WHAT IS INTERESTING 
AS THESE MACHINES GET LARGER AND 
AS WE ARE ABLE TO DO BIGGER AND 
BIGGER SIMULATIONS, WE ARE ABLE 
TO SCALE THESE UP TO BE MORE AND 
MORE INTERESTING SIMULATIONS THAT 
ALLOW US TO BUILD COMPLETELY NEW 
THINGS AND HAVE A TOTALLY DIFFERENT 
RELATIONSHIP WITH THE BUILT WORLD 
BECAUSE WE ARE ABLE TO KNOW THE 
PROPERTIES OF DIFFERENT CHEMICALS 
THAT WE HAVEN'T PRODUCED BEFORE. 
WHEN I THINK ABOUT WHAT ONE CUBICS 
IS REALLY WORKING ON, THAT IS REALLY 
THE OLD STAR, THIS IS THE THING 
THAT WE WANT TO DO IN THE FUTURE. 
AND AT THE SAME TIME, WE ARE ABLE 
TO DO A LOT OF WORK TODAY BY PARTNERING 
UP WITH AZURE. FOR EXAMPLE, WE ARE 
ABOUT TO RELEASE A QUANTUM INSPIRED 
PLATFORM TO BE ABLE TO ADDRESS A 
WHOLE BUNCH OF PROBLEMS THAT WE 
ARE HE GOING TO HEAR ABOUT NEXT. 
THE WAY THAT I LIKE TO THINK ABOUT 
IT IS, IF YOU ARE LOOKING TO BE 
ABLE TO UNDERSTAND WHAT WE ARE GOING 
TO BE DOING WITH QUANTUM COMPUTERS 
IN THE NEAR FUTURE OR IF YOU ARE 
INTERESTED IN BEING ABLE TO HARNESS 
ALL OF THE THINKING THAT GOES INTO 
BUILDING THE DEVICES TO SOLVE REALLY 
DIFFICULT PROBLEMS TODAY, YOU CAN 
DO ALL OF THAT THROUGH MICROSOFT 
AND THROUGH MICROSOFT AZURE. AND 
TO HEAR A LITTLE BIT MORE ABOUT 
THE QUANTUM INSPIRED THINGS AT MICROSOFT, 
WELCOME TO THE STAGE, OUR FRIEND 
AND COLLEAGUE DR. HELMUT KATZGRABER. 
HE WILL TAKE THE LONG WAY AROUND 
TO COME UP HERE. AND ENTERTAIN YOU 
THE REST OF THE PROGRAM. >> THANK 
YOU, ANDREW. >> THANKS A LOT. [ 
APPLAUSE ] >> THANK YOU ALL FOR 
STILL STAYING HERE IN THIS LATE 
TIME OF THE DAY. SO WHAT I LIKE 
TO DO IS SHIFT GEARS A LITTLE BIT, 
TALK ABOUT WHAT WE CALL QUANTUM 
INSPIRED OPTIMIZATION. AS YOU SEE, 
IT IS OPTIMIZATION WITH A QUANTUM 
TWIST TO IT. NOW BEFORE I TELL YOU 
A LITTLE BIT MORE ABOUT QUANTUM 
INSPIRED OPT MY NATION. I WOULD 
LIKE TO SAY MORE ABOUT QUANTUM HARDWARE 
AND PULL SOME MISCONCEPTIONS OUT 
OF THE ROOM. THEIR SO-CALLED DIGITAL 
AND ANALOGUE QUANTUM COMPUTING DEVICES 
AND VERY DIFFERENT IN THEIR NATURE. 
DIGITAL DEVICES TEND TO NOWADAYS 
TYPICALLY HAVE A SMALL INJURY CUBIC 
COUNT. PROGRAMMABLE, FOR EXAMPLE, 
BY A Q SHARP. IN SOME SENSE, THE 
SAME WAY YOU WOULD PROGRAM A REGULAR 
COMPUTER. PROGRAMMING LANGUAGE. 
IMPLEMENT A PROBLEM. SOLVE IT ON 
THE DEVICE. EITHER OUT OF IONS, 
SUPER CONDUCTING FLEX CUBICS OR 
LIKE IN MICROSOFT CASE, TOPO LOGICAL 
CUBICS. IN PROJECTS, YOU HAVE QUANTUM 
YIELDING MACHINES. SPECIAL PURPOSE 
DEVICES. THINK OF THEM AS A MACHINE 
THAT DECIDE TO DO ONE PARTICULAR 
TASS, IN THIS CASE, OPTIMIZATION. 
THEY ARE ANALOGUE. MEANING THAT 
YOU KIND OF GET TO LOOK AT THE PROBLEM. 
THERE IS ALWAYS ERRORS ATTACHED 
TO THIS. AND THEY ARE MADE OUT OF 
SUPER CONDUCTING FLEX CUBICS. WHAT 
I WANT TO TALK ABOUT TODAY, BASICALLY 
A THIRD PARADIGM THAT IS NOT QUITE 
QUANTUM BUT USE SOME OF THE ADVANTAGES 
OF QUANTUM ON CURRENT HARDWARE. 
THIS IS WHAT WE LIKE TO CALL QUANTUM 
INSPIRED. AND WE RUN ON STANDARD 
SEAMLESS HARDWARE OR ACCELERATED 
ON FPJS. THE IDEA HERE TO EMULATE 
QUANTUM PROCESS ON CURRENT HARDWARE 
AND SOLVE CURRENT INDUSTRY CHALLENGES 
MORE EFFICIENTLY. NOW THE FOCUS 
OF THIS TALK WILL BE OPTIMIZATION. 
AND PARTICULAR OPTIMIZATION. WHY? 
THE ANSWER IS SIMPLE, OPTIMIZATION 
PLAY AS VERY CRUCIAL ROLE IN EVERYBODY'S 
LIFE. IF YOU WANT TO SHIP SOMETHING 
ACROSS THE WORLD, IF YOU WANT TO 
DO A SCHEDULING PROBLEM, IF YOU 
WANT TO DO SEND A ROCKET IN SPACE, 
YOU NEED TO SOLVE VERY HARD OPTIMIZATION 
PROBLEMS. THERE IS A LOT OF VALUE 
TO THIS. AND A LOT OF CHALLENGES. 
QUANTUM OPTIMIZATION MAINLY FOCUSED 
ON BINARY PROCESS. TAKE ZERO ONE, 
OR PLUS MEENOUS ONE. THE REASON 
IT IS VERY SIMPLE. A HUGE INDUSTRIAL 
VALUE. I WILL SHOW YOU A BROAD SELECTION 
OF THIS PROBLEM IN THE NEXT TWO 
SLIDES. THEY HAVE A SIMPLE MATHEMATICAL 
FORMALIZATION. BECAUSE OF THIS, 
DON'T WORRY, NOT SHOW YOU ANY EQUATIONS, 
THEY ARE VERY WELL SUITED FOR QUANTUM 
OPTIMIZERS. I AM GOING TO TRY TO 
GIVE YOU A BIT OF A FLAVOR HOW THIS 
QUANTUM OPTIMIZATION WORKS. BASICALLY, 
BY GOING BACK A FEW THOUSAND YEARS 
TO THE ALY IT HIC ERROR. PEOPLE 
TAKE CHUNK OF METAL, SLOW IT DOWN 
SLOWLY, THIS PROCESS. NOW BACK IN 
THE EARLY 80s, TIME AT INTEL, CAME 
UP WITH A CLEVER IDEA. WHY NOT EMULATE 
THIS PROCESS ON A COMPUTER. CALLED 
THIS PROCEDURE SIMULATED AMELING. 
BASICALLY LOOKING AT CHIP LAYOUT 
FORCE THE MICRO PROCESSORS AT THE 
TIME. THE IDEA IS SIMPLE. YOU BASICALLY 
START WITH A CONFIGURATION THROUGH 
OPTIMIZATION. IT CAN BE ANYTHING. 
OKAY. THE IMAGINATION IS YOUR LIMIT. 
AND YOU ATTACH A TEMPERATURE, THINK 
OF IT REALLY AS A TEMPERATURE IN 
THE SENSE OF WHAT BOILS WATER. SO 
YOU START FROM THE VERY HIGH TEMPERATURE. 
WHERE YOUR VARIABLES FOR YOUR SYSTEM, 
THINK AGAIN, THE WATER IS BOILING. 
AND THEN YOU REDUCE THE TEMPERATURE 
ACCORDING TO A SCHEDULE. UNTIL YOU 
REACH SOME TARGET VALUE. AND IF 
YOU DO THIS SLOW ENOUGH, YOU MIGHT 
FIND THE OPTIMUM OR THE SOLUTION 
TO THE PROBLEM. OKAY. IN THE CASE 
OF WATER AGAIN, IF YOU COOL IT SLOW 
ENOUGH, YOU MIGHT GET A WONDERFULLY 
PERFECT ICE CUBE WITHOUT ANY IMPERFECTS 
IN IT. QUANTUM AMELING, COUNTERPART 
TO SIMULATE, ALL YOU HAVE TO DO 
IS REPLACE THE QUANTUM FLUCUATES, 
REPLACE THE THERMAL FLUCUATES, QUANTUM 
FLUCTUATIONS, IF I NOW PRESS CLICK 
ON THIS, THIS ALGORITHM WILL CHANGE 
EVER SO SLIGHTLY. WHAT IS TEMPERATURE 
BEFORE, IS NOW TRANSFER. AND BASICALLY 
INSTEAD OF DOING [INDISCERNIBLE], 
SO YOU RUN SIMULATED ON A REGULAR 
COMPUTER, YOU DO QUANTUM UPDATES. 
IF YOU WANT TO DO THIS IN HARDWARE, 
WHAT YOU DO IS YOU USE SO-CALLED 
SUPER CONDUCTING FLEX CUBICS, SUPER 
CONDUCTING METAL RINGS. AS YOU KNOW, 
IF YOU HAVE A CURRENT, WHO DOES 
REMEMBER HOW THE LAW WORKS? NOBODY 
TOOK PHYSICS HERE? A COUPLE HANDS. 
ACCORDING TO THE LAW, WHEN YOU HAVE 
A RING OR A CURRENT, GOING COUNTERCLOCKWISE, 
YOU HAVE A RING, COUNTERCLOCKWISE, 
FLEX POINTING DOWN. STATE ZERO IN 
ONE. YOU CAN'T HAVE SUPER POSITIONS 
OF ZERO AND ONE. THIS GIVES YOU 
YOUR FLEX CUBICS. OKAY. NOW WHAT 
YOU THEN DO IS, YOU STRING THIS 
SUPER CONDUCTING LOOPS TOGETHER 
BY A SOME KIND OF INTERACTION MATRIX. 
IN THIS IS WHAT YOU USE TO CODE 
YOUR PROBLEM. BINARY MATHEMATICAL 
VARIABLES. PRESENT THEM ON THE HARDWARE. 
APPLY A STRONG TRANSVERSE FIELD. 
YOU REVIEW THE FLUCTUATIONS SLOWLY. 
IF THERE IS SOME KIND OF BUMP OR 
BARRIER IN YOUR CROSS FUNCTION LANDSCAPE, 
THE QUANTUM SYSTEM MAY BE ABLE TO 
TUNNEL THROUGH THE BARRIER AND FIND 
THE OPTIMUM MORE EFFICIENTLY WITH 
THOROUGHLY ASSISTED ALGORITHM. GOOD. 
NOW THIS ONE IS GREAT. AND YOU ARE 
USING QUANTUM MECHANICS TO SOLVE 
THE PROBLEM. WHAT IS REAL FOR CURRENT 
QUANTUM MACHINES? THE REALITY IS 
A LITTLE BIT BLEAK. IN THAT THERE 
IS A FEW CHALLENGES TO OVERCOME. 
THE FIRST ONE IS, WHAT WE CALL THE 
EMBEDDED OVERHEAD. SUPPOSE YOU WANT 
TO STUDY A PROBLEM. THINK OF IT 
AS A VERTEX COVER PROBLEM, A MATCHING 
PROBLEM, WHATEVER. SOMETHING THAT 
LEAVES ON THIS TIPPOLOGY HERE. WHAT 
YOU SEE ON THE LEFT HAND SIDE. NOW 
YOU SEE THIS IS YOUR ACTUAL PROBLEM 
WITH YOUR LOGICAL VARIABLES THAT 
YOU WANT TO TREAT. BUT BECAUSE AS 
YOU SAW BEFORE, WE HAVE A HARD WIRE 
CHIP THAT LIVES ON THE SQUARE GRID. 
WE NEED TO TAKE THIS LOGICAL VARIABLE 
ONE AND REPLICATE IT FOUR TIMES. 
SUCH YOU CAN FULFILL ALL OF THE 
INTERACTIONS AT VARIABLE ONE HAS 
WITH THE NEIGHBORS. AND SO YOU NEED 
TO STRONGLY COUPLE THIS PHYSICAL 
VARIABLES HERE, TO CREATE ONE LOGICAL 
VARIABLE ON THE LEFT HAND SIDE. 
YOU SEE IT RIGHT AWAY IN THIS SIMPLE 
EXAMPLE, YOU HAVE ABOUT A 50 OVERHEAD 
IN THE NUMBER OF VARIABLES THAT 
YOU NEED TO SOLVE THE PROBLEM. THIS 
CAN GET VERY, VERY EXPENSIVE. AS 
I SAID BEFORE, YOU HAVE ANALOGUE 
POSITION, TYPICALLY 5-6 BITS POSITION 
AS YOU ALL KNOW, THAT IS NOT VERY 
MUCH FOR A COMPUTER. AND YOU ARE 
LOCKED IN WITH AN ALGORITHMIC ENGINE 
THAT IS QUANTUM, AND STRONG TRANSVERSE 
FIELD, REDUCE THE TARGET VALUE. 
YOU ARE UNABLE TO HANDLE COMPLEX 
CROSS FUNCTIONS FOR THOSE WHO COME 
FROM OR, THINGS OF MIXED INTEJER 
PROBLEMS. TO DATE, COMFORTABLE IN 
SAYING THIS, NO REAL COMPLICATION, 
SOMETHING THAT MATTERS IN INDUSTRY, 
THE ALGORITHMS THAT I WORK ON, NOT 
OUTPERFORMED THE MACHINES. THIS 
IS WHY WE ARE SO INTERESTED IN THIS 
QUANTUM INSPIRED TECHNIQUES. HAVING 
THIS CLAIM OF SPEED UP, IS REALLY 
WHAT FUELED MY INTEREST IN THIS 
PROBLEM AND CREATED A FRUITFUL COMPETITION 
THAT MADE US DEVELOP NEW METHODS 
THAT CAN SOLVE THESE PROBLEMS THAT 
WERE CONSIDERED TO BE VERY, VERY 
HARD MUCH MORE EFFICIENTLY THAN 
BEFORE. MORE IMPORTANTLY, IF YOU 
RUN THIS ON A REGULAR CPU, WE HAVE 
ABSOLUTELY NO INTRINSIC LIMITATION 
WHICH IS GREAT NEWS. FINALLY, IF 
YOU TRIED QUANTUM ON A PROBLEM, 
WE CAN DO IT FASTER, CHEAPER AND 
BETTER. IF WE JUST EMULATE THE QUANTUM 
PROCESSES ON CLASSICAL HARDWARE. 
NOW I HAVE BEEN USING THIS WORD 
QUANTUM INSPIRED OPTIMIZATION. BUT 
I YET TO TELL YOU WHAT IT ACTUALLY 
MEANS. IT IS LOOSELY DEFINED. IF 
YOU LOOK IN THE DICTIONARY, INSPIRED 
MEANS TO INSERT INFLUENCE ON SOMETHING. 
OKAY. HERE WHEN I SAY VERY LOOSELY, 
DEFINED, YOU HAVE THE WHOLE RANGE 
IN THIS SPECTRUM. ON ONE SIDE, YOU 
ARE TRYING TO REALLY EMULATE THE 
QUANTUM PROCESS. FOR EXAMPLE, BY 
A QUANTUM OF THE CARD, OR QUANTUM 
ON COMPUTER. FOR EXAMPLE, IF YOU 
HAVE THIS ROUGH CROSS FUNCTION LANDSCAPE 
THAT YOU SEE HERE, YOU WANT TO SOLVE 
SIMULATE, THEN YOU HAVE TO HAVE 
A LARGE ACTIVATION ENERGY TO OVERCOME 
THIS BARRIER. AND SUCH A MOVE WILL 
BE VERY UNLIKELY IN YOUR SIMULATION. 
IF YOU WERE TO DO THIS WITH PURE 
QUANTUM MONTE CARLO, THEN YOU MIGHT 
POTENTIALLY TUNNEL THROUGH THE BARRIER 
AND FIND THE OPTIMUM FASTER. IN 
QUANTUM INSPIRED OPTIMIZATION WE 
ARE TRYING TO TAKE THE BEST WE CAN 
OUT OF QUANTUM AND IMPLICATE IN 
CLASSICAL HARDWARE AND TRY TO DEPICT 
THIS BY HAVING IMPROVEMENT OVER 
CLASSICAL MAYBE NOT THE FULL BENEFIT 
OVER QUANTUM. THIS IS ONE EXTREME. 
THE OTHER EXTREME IS ONE THAT I 
AM MOST INTERESTED IN. WHICH IS 
USE ALGORITHMS THAT ARE COMMONPLACE 
IN PHYSICS AND THAT WE PHYSICISTS 
HAVE USED OVER DECADES TO SOLVE 
REALLY HARD PROBLEMS IN INDUSTRY. 
AND TO SHOW YOU HOW WELL WE WORK, 
LET ME SHOW YOU A CASE STUDY. SOMETHING 
THAT IS BACK IN 2016. SOME OF YOU 
MIGHT HAVE HEARD OF A SOUTH COMPETITION, 
THE COMPETITION, BASICALLY, SAT 
IS A PROBLEM, SOLVABILITY PROBLEM 
WITH HUGE INDUSTRIAL VALUE. A LOT 
OF THE PROBLEMS IN OPTIMIZATION 
GET MAPPED INTO THESE FORMULAS. 
IF YOU WANT A PROOF OF SOMETHING 
IS P OR NP, THEN YOU END UP MAPPING 
IT A THREE SET FORMULA. IF IT MAPS, 
THEN IT IS HARD TO SOLVE. SO IN 
MAPS, THE GOAL, THE MAXIMUM NUMBER 
OF CLAUSES, EQUATE TO TRUE IN SUCH 
A BRILLIANT FORMULA. FOUR FORMULAS. 
THEY CAN BE NEGATED WHICH I DENOTE 
WITH OVERBAR. AND THEY ARE CONNECTED 
BY A LOGICAL ORS. THEN THE CLAUSES 
OF THREE EACH ARE CONNECT BID LOGICAL 
ANDS. AND THE QUESTION NOW IS, CAN 
YOU FIND ASSIGNMENT TO THESE VARIABLES 
SUCH A THAT YOU CAN MAXIMIZE THE 
NUMBER OF CLAUSES THAT ARE TRUE. 
WHEN YOU HAVE JUST FOUR VARIABLES 
AND TWO CLAUSES, IT IS PRETTY EASY. 
SIT DOWN WITH PAPER AND PENCIL. 
WHEN YOU HAVE ABOUT A HUNDRED OF 
THOSE, YOU ARE GOING TO BE DOING 
MATH FOR THE REST OF THE AGE OF 
THE UNIVERSE. NOW THERE IS A COMPETITION 
THAT HAPPENS EVERY YEAR BY THE ASSOCIATION 
WHERE PEOPLE HAVE BEEN SENDING IN 
ALGO RISMS ONE YEAR, WHY NOT SEND 
PHYSICS ALGORITHM TO THE COMPETITION. 
WE DID. WE ENDED UP WINNING ON THE 
FIRST MISSION. THIS THING HERE, 
WE CALL BEAURAL-IS, NOTHING BUT 
A INDUSTRIAL PROBLEM. NOT ONLY THAT, 
SECOND TEAM, FOLKS THAT ARE IN MICROSOFT, 
STEVEN JORDAN AND BRAD, WHO SUBMITTED 
[INDISCERNIBLE]. THEY LANDED FIFTH 
PLACE. OR DIDN'T MAKE IT TO THE 
FINAL ROUND. LIFE IS TOUGH SOMETIMES. 
NOW WHEN SHOULD YOU USE QUANTUM 
INSPIRED OPTIMIZATION OR QIO? WELL, 
THERE ARE CERTAIN USE CASES. SUPPOSE 
A GIVEN OPTIMIZATION PROBLEM IN 
FRONT OF YOU. ONE OF THEM IS, IF 
YOU WANT TO HAVE A FIXED SOLUTION 
QUALITY YOU WANT TO FIND A SOLUTION 
FASTER. OKAY. THIS IS DIFFICULT 
PROBLEM WHEN YOU SAY I WANT TO MAKE 
SURE THAT I HAVE AT LEAST SO MUCH 
QUALITY IN MY SOLUTION BUT I WANT 
YOU TO TELL ME THIS SOONER. AND 
IF YOU SAY I'M HAPPY ENOUGH, 90 
OF THE PROBLEM SOLVED BUT ONLY GIVE 
YOU ONE SECOND TO DO THIS. FOR A 
FIXED SOLUTION TIME, FIND A BETTER 
SOLUTION. THIS IS VERY IMPORTANT 
IN THINGS LIKE UNIT COMMITMENT PROBLEMS. 
WHEN TRYING TO OPTIMIZE A POWER 
GRID, PRE-COMPUTE THE POTENTIAL 
LOAD EVERY SO MANY HOURS MUCH OF 
COURSE, IF YOU HAVE A FIXED AMOUNT 
OF TIME YOU WANT TO FIND THE BEST 
SOLUTION POSSIBLE. AND THEN FINALLY, 
YOU WANT TO BE ABLE TO SOLVE MORE 
COMPLEX VERSIONS OF THE PROBLEM 
WITH FIXED EFFORT IN THE SENSE THAT 
WHAT IF THERE IS CONSTRAINTS, IF, 
I'M LOOKING AT A ROUTING PROBLEM, 
AND NOT ONE TO FACTOR IN THAT THERE 
ARE MORE TRUCKS PARKED IN OVER HERE 
THAN OVER THERE. WHAT ARE THE CHARACTERISTICS 
THAT OPTIMIZATION PROBLEM SHOULD 
HAVE FOR QUANTUM INSPIRED OBVIOUSLY 
IF IT LOOKS LIKE THE ONE UP THERE, 
IT IS NOT GOOD NEWS, ANY GRADE WILL 
FIND YOU THE OPTIMUM RIGHT AWAY. 
YOU WANT SOMETHING GNARLY, LIKE 
THIS LANDSCAPE. SOMETHING WITH A 
LOT OF BUMPS, SOMETHING LOCAL SEARCH 
ALGORITHM WILL GET STUCK RIGHT AWAY. 
YOU ALSO WANT THE LARGE NUMBER OF 
VARIABLES. SOMETHING WITH 20 VARIABLES 
YOU CAN EXACT NUMMERRATE ALL EXACT 
SOLUTIONS. IT DEFEATS THE PURPOSE. 
FINALLY YOU WANT THE CROSS FUNCTION 
TO BE EVALUATED QUICKLY. WHY? BECAUSE 
THE EVALUATION OF THE CROSS FUNCTION 
LIES AT CORE OF THE ALGORITHM. NOW 
I WANT TO EMPHASIZE THAT THESE METHODS, 
IN OTHER WORDS, YOU DON'T GET A 
GUARANTEED SOLUTION. HAVING SAID 
THAT, OFF 1 IMPROVEMENT CAN BE HUGE 
IF YOU SCALE UP TO INDUSTRIAL SCALES. 
A HUGE COLLECTION OF PROBLEMS THAT 
CAN BENEFIT FROM THIS QUANTUM INSPIRED 
MATH. STARTING WITH CIRCUIT FULL 
DIAGNOSIS TO VERIFICATION AND VALIDATION, 
WHICH ARE VERY IMPORTANT IF YOU 
WANT TO, FOR EXAMPLE, BUILD COMPONENTS 
OR BUILD A JET THAT FLIES. TO CHEMICAL 
OIL AND GAS, MATERIAL DISCOVERY, 
I'LL SHOW YOU SOME RESULTS OF THAT 
LATER. UNIT COMMITMENT AS I SAID, 
POWER GRIDS, SIGNAL PROCESS, MACHINE 
LEARNING TO A LESSER EXPENT TENT 
BECAUSE THE LANDSCAPES IN THIS PROBLEM 
TEND TO BE MORE CON VEC, THEY ARE 
EASIER. DROP SHOP SCHEDULING AND 
TOOL OPTIMIZATION. IF YOU HAVE A 
PLAN TO BUILD A VEHICLE, HOW TO 
DISTRIBUTE THE MACHINES ON TO DIFFERENT 
TEAM FORCE DIFFERENT WORKERS TO 
MAKE THIS HAPPEN FASTER. AND FINALLY, 
OF COURSE, TRAFFIC CAR SHARING AND 
LOGISTICS. WE'LL SHOW YOU LATER, 
WE ALSO HAVE FAR MORE EXOTIC APPLICATIONS 
SUCH AS OPTIMIZING SEQUENCES. NOW 
IF YOU WANT TO COMPARE QUANTUM VERSUS 
QUANTUM INSPIRED OPTIM IZATION, 
THEN FIRST WE HAVE TO SET THE PLAYING 
FIELD, WHAT DO WE MEAN BY DISRUPTIVE 
TECHNOLOGY? IT SHOULD BE FASTER 
THAN ANY ALGORITHM OUT THERE RIGHT 
NOW. OR IT SHOULD HAVE BETTER PERFORMANCE 
ON SOME SPECIFIC TASKS. SO WHAT 
IS FASTER? AGAIN, HERE, THERE ARE 
TWO CASES OF FASTER. LET'S ASSUME 
THAT YOU ARE LOOKING AT A PROBLEM 
THAT HAS A SPECIFIC NUMBER OF VARIABLES. 
AND YOU KEEP INCREASING THE NUMBER 
OF VARIABLES AND YOU LOOK AT THE 
TIME IT TAKES YOU TO SOLVE THE PROBLEM. 
TYPICALLY THE THINGS SCALE. THEY 
ARE BAD NEWS FOR ANY KIND OF ALGORITHM. 
SO WE'LL GIVE YOU ROUGHLY A STRICT 
LINE. FASTER, WHICH IS THE IDEAL 
CASE, CAN MEAN THAT YOU GET A BETTER 
SCALING. IN OTHER WORDS, THE SLOPE 
OF THE CURVE IS SLIGHTLY MORE FLAT. 
BUT FASTER CAN ALSO MEAN A HUGE 
UPSET. IF THIS IS A BILLION, THEN 
IT IS DEFINITELY WORTH IT. EVEN 
THOUGH THE SAME SCALING EXISTS. 
WHAT IS BETTER PERFORMANCE, WELL, 
FOR EXAMPLE, DOES IT GIVE YOU MORE 
VARIETY POOL OF SOLUTIONS? WILL 
IT HELP YOU IN ADDING ADDITIONAL 
CONSTRAINTS? THE MOST IMPORTANT 
THING THAT I WANT YOU TO TAKE AWAY 
HERE IS THAT WHEN YOU SEE ANY CLAIMS 
OF SPEED-UP IN THE NEWS, YOU HAVE 
TO BE VERY CAREFUL. BECAUSE IT STRONGLY 
DEPENDS ON THE BENCHMARK YOU USE. 
A SIMPLE EXAMPLE AS A FOLLOWING. 
YOU WANT TO RAISE A FORMAL OF ONE 
VEHICLE AGAINST A RALLY CAR. FORMAL 
ONE WEEK, FASTER. IF YOU ARE DRIVING 
ON DIRT, IT IS PRETTY CLEAR WHICH 
ONE IS GOING TO WIN. OKAY. ALWAYS 
BALANCE THESE THINGS OUT. LET ME 
SHOW YOU AN EXAMPLE IN 2016, INTERESTING 
RESULTS USING THE QUANTUM AMELIER. 
IT IS A TIME TO SOLVE THE PROBLEM. 
FUNCTION OF VARIABLES. USING THE 
MACHINE LABELED HERE WITH QA. SIMULATING 
THE MACHINE ON CLASSICAL MACHINES 
LABELED WITH QUANTUM MONTE CARLO 
AGAINST SIMULATED, THE THERMAL COUNTERPART 
THAT I SHOWED YOU BEFORE. WHEN YOU 
LOOK AT THIS DATA, YOU CAN SEE RIGHT 
AWAY THAT THE QUANTUM APPROACH IS 
SCALE BETTER, THE SLOPE IS FLATTER. 
AND NOT ONLY THAT, BUT THERE IS 
A HUGE OFFSET OF EIGHT ORDERS OF 
MAGE MAGNITUDE HERE. WHEN YOU LOOK 
AT THIS, IT SOUNDS REALLY IMPRESSIVE 
FOR QUANTUM OPTIMIZATION. SO AGAIN, 
WE SAID, SOMEBODY PLEASE HOLD MY 
BEER. LET'S SEE WHAT OUR ALGORITHMS 
CAN DO. I DON'T WANT YOU TO TRY 
TO FIGURE OUT WHAT IS WHAT. JUST 
LOOK AT THE SPAGHETTI ON THE SCREEN. 
AGAIN HERE, YOU HAVE QUANTUM MONTE 
CARLO, SIMULATED, AND THE DEVICE 
AND A BUNCH OF OTHER ALGORITHMS. 
MORE IMPORTANTLY, THE PURPLE LINE 
DOWN HERE, THAT SCALES REALLY WELL, 
AND COMPARABLE IN SPEED, IT IS BURIALOUS. 
THE ALGORITHM THAT WON THE COMPETITION. 
AND I'M GOING TO DO NOW, MAKE THIS 
MORE PALATABLE FOR YOU, COMPUTE 
THE SLOPE FORCE THE LARGEST SYSTEM 
SIZES. IN OTHER WORDS, PERFORM A 
SCALING ANALYSIS AND JUST SHOW YOU 
THE SLOPES FOR DIFFERENT ALGORITHMS. 
OF COURSE, SMALLER MEANS BETTER. 
YOU CAN SEE RIGHT AWAY, THE QUANTUM 
APPROACH IS HERE ARE VERY GOOD. 
BUT THEY ARE NO MATCH AGAINST OUR 
ALGORITHMS. NOTICE ALSO THAT I KIND 
OF COLORED IN THIS FIGURE. ON THE 
LEFT HAND SIDE, HAVE YOU WHAT WE 
CALL SEQUENTIAL ALGORITHMS. THESE 
ARE SIMULATING AND POPULATION, AND 
MONTY CARLO. YOU HAVE A CONTROL 
PARAMETER. A STRONG TEMPERATURE, 
HIGH TEMPERATURE, AND YOU SEQUENTIALLY 
REDUCE TO TARGET VALUE. SPECIFIC 
TYPE OF ALGORITHM. THEN YOU HAVE 
THE TAILORED ALGORITHM, THAT IS 
CHEATING. I A KNOW AHEAD OF TIME 
WHAT THE PROBLEM IS SUPPOSED TO 
SOLVE. EXPLOIT SOME SIGNATURE OF 
THE PROBLEM. THEN YOU HAVE GENERIC 
ONES LIKE MONTE CARLO AND BASED 
ON TEMPERING MONTE CARLO THAT IS 
CLEARLY COMPETITIVE AND OUTPERFORM 
QUANTUM OPTIMIZATION. SO WE THOUGHT 
THAT WE WOULD CAPITALIZE HERE AT 
MICROSOFT. AND DECIDED TO LOOK INTO 
THIS MORE CLOSELY. WHAT IS OUR APPROACH? 
FIRST AND FOREMOST, DEVELOP COMBINE 
AND IMPROVE ALGORITHMS. THIS IS 
VERY IMPORTANT. I'LL GET BACK TO 
THIS AGAIN. WE WANT TO BUILD A FLEXIBLE 
STACK WITH MODULAR ALGORITHMIC DIVISION 
THAT IS MASSIVELY PARALLEL. YOU 
SEE, NOT EVERY ALGORITHM IS MEANT 
FOR EVERY PROBLEM. YOU WANT TO BE 
ABLE TO SWAP THAT IN AND OUT. DEPENDING 
WHAT YOU ARE LOOKING AT. ADVANTAGES 
ARE, THAT THE BIGGEST SPEED-UPS 
TO DATE, COME FROM BETTER ALGORITHMS. 
YOU SEE, IF YOU WANT TO DOUBLE THE 
SPEED YOU HAVE TO DOUBLE THE SIZE 
OF THE MACHINE. IF YOU COME UP WITH 
A BETTER ALGORITHM, THAT SCALES 
BETTER, YOU DON'T HAVE TO DOUBLE 
THE MACHINE. DEVICES ARE DIGITAL, 
MEANING THAT WE HAVE A LARGER APPLICATION 
DOMAIN AND BECAUSE WE CAN DO WALL 
TO WALL CONNECTIVITY, WITH LOCAL 
INTERACTIONS, EXPLAIN LATER, DIGITAL 
POSITION, NO NEED FOR MAPPING OR 
EMBEDDED. WE DEVELOP THE CODES AND 
CPU'S. WE RUN THEM NOW IN FPJ'S 
FOR SPEED AND HOPEFULLY ONE DAY 
RUN THEM ON QUANTUM ACCELERATED 
HARDWARE. LET ME SHOW YOU HOW WELL 
THIS METHOD WORKS FOR REAL WORLD 
PROBLEM. IN 2017, [INDISCERNIBLE] 
PUBLISHED FOLLOWING STUDY WHERE 
QUANTUM WAS USED TO OPTIMIZE THE 
TRAFFIC OF 480 VEHICLES IN BEIJING 
HEADING TO THE AIRPORT. THE HEAT 
HERE REPRESENTS THE TRAFFIC. NOW 
AS YOU SEE WITH TRANSITION, TRADITIONAL 
METHODS TAKES ABOUT 800 SECONDS 
TO SOLVE THE PROBLEM. IF YOU SAW 
IT ON THE QUANTUM ANEL IER, YOU 
ARE LOOKING AT 20 SECONDS. OUR APPROACH, 
20. FPJ, 0. 0004 SECONDS. NOW THIS 
POINT, I WOULD LIKE TO JUST SHIFT 
GEARS A LITTLE. AND SHOW YOU NOW 
HOW TO CAPITALIZE ON THIS TO SOLVE 
REAL WORLD PROBLEMS TODAY. AND HERE 
I WANT TO HIGHLIGHT FOUR OF THE 
CORE STRENGTHS THAT WE HAVE. ONE 
IS ALGORITHMIC EXPERTISE. LARGE 
SCALE SCALABLE HARDWARE. MODULAR 
SOFTWARE. AND MORE IMPORTANTLY, 
DOMAIN EXPERTISE. HAVING PEOPLE 
THAT CAN THINK ABOUT THE PROBLEMS 
AN CAST THEM IN A FORM THAT MAKES 
THEM EASIER TO SOLVE IS AS IMPORTANT 
AS HAVING GOOD HARDWARE IF NOT MORE. 
THE FIRST ONE I WANT TO TALK ABOUT, 
HIGHLIGHTS OUR ALGORITHMIC EXPERTISE 
IS OPTIMIZING SEATTLE'S TRAFFIC. 
I THINK THAT EVERYBODY HAD AN ISSUE, 
UNLESS YOU HAPPEN TO BE STAYING 
AT THE SHERA IT ON, GETTING TO THIS 
BUILDING TODAY. AVOID IT ALL TIMES. 
SO THE OBJECTIVE WAS TO OPTIMIZE 
RUSH HOUR TRAFFIC USING THE QUANTUM 
INSPIRED OPTIM GLIERKS TECHNIQUES. 
WHAT IS CURRENT STARTER, IF WE TAKE 
BING'S BEST ACCOMMODATIONS AND LOOK 
AT REALTIME TRAFFIC DATA, FIND 50 
OR LESS CONGESTION. AND WE CAN REDUCE 
TRAVEL TIMES BY ABOUT 8. NOW REMEMBER 
THE CASE STUDY I SHOWED YOU BEFORE, 
WAS ON 480 VEHICLES. WE THOUGHT 
LET'S KICK IT UP A NOTCH. FOR THIS, 
WE HAD FRANCES AND ARETHA, SITTING 
HERE IN THE FIRST, SECOND ROW, BEING 
KIND OF SHY AND NOT DOING THIS. 
AND DOING A PHENOMENAL JOB AND IMPLEMENTING 
THIS WITH REND THIS FOR 5, 000 VEHICLES. 
WELL, HERE YOU HAVE A LIVE DEMO 
ON THIS. AND YOU BASICALLY SELECT 
THE NUMBER OF VEHICLES, WE LOOK 
AT THE 5, 000 VEHICLES OUT OF 5, 
000 RANDOM LOCATIONS IN SEATTLE. 
WE KEEP EACH VEHICLE TEN DIFFERENT 
OPTIONS TO TRAVEL. AND NOW YOU SEE 
THIS IS NASTY TRAFFIC. YOU SEE AT 
THE BOTTOM HOW THE CONGESTION SCORE 
KEEPS GOING DOWN. AFTER 2, 000 STEPS, 
OR OPTIMIZER, IF YOU COMPARE THE 
RESULTS, I DON'T THINK THAT I NEED 
TO CONVINCE YOU THAT THINGS ARE 
MUCH BETTER ON THE RIGHT HAND SIDE 
THAN THE LEFT HAND SIDE. AGAIN NOT 
MY JOB. THESE TWO DID THE BRUNT 
OF THE WORK. THEY ARE AWESOME. GOOD. 
NOW THE NEXT EXAMPLE I WANT TO SHOW 
YOU, IT IS ACTUALLY WORK TOGETHER 
WITH OUR PARTNER ONE CUBIC. AND 
HERE I WANT TO SHOW YOU HOW TO TACKLE 
HEART OF THE PROBLEMS IN CHEMISTRY. 
WHAT WE DID HERE ON THE SOFTWARE 
SIDE IS USE MICROSOFT QUANTUM INSPIRED 
TOOL BOX. AND ON THE HARDWARE, THIS 
IS WHAT I WANT TO HIGHLIGHT HERE. 
WE HAVE A CONFIGURED FPJ CLOUD IN 
A MASSIVE SCALE. THIS IS CATAPULT 
VERSION TWO. WHAT IS IT BASICALLY 
SEVERAL, A LOT OF THE SERVER BLADES 
IN AZURE HAS BUILT-IN FPJ'S. NOW 
THIS BY ITSELF IS NO NEWS. WHAT 
IS AMAZING ABOUT IT IS AS YOU CAN 
SEE THE FPJ CONNECTED. NOT ONLY 
THAT, YOU HAVE A FABRIC THAT CONNECTS 
FP JOVMENT -- FPJ CONNECTED. THAT 
MEANS WE CAN SOLVE THINGS THAT MOST 
PEOPLE WOULD SAY TODAY, THERE IS 
NO WAY OF SOLVING THEM. SO TO JUST 
SHOW YOU WHAT WE CAN POTENTIALLY 
DO, I JUST LIKE TO SHOW YOU AN EXAMPLE 
OF WORK THAT WAS DONE [INDISCERNIBLE] 
ONE CUBIC, GRAPH BASE MOLECULAR 
NARRATIVE. THIS IS KEY, IF YOU WANT 
TO MAKE SURE THAT YOU DON'T WAIT 
TOO LONG FOR PAINT TO WAIT. HOW 
MUCH DO THESE MOLECULES AGREE? WHAT 
YOU DO IS, YOU MAP THE MOLECULES 
TO A GRAPH. THEN YOU CREATE WHAT 
IS KNOWN AS THE CONFLICT GRAPH. 
THIS IS THE HAIR BALL THAT YOU SEE 
ON THE LEFT. YOU CAN SEE IT IS VERY 
CONFLICTIVE IN ITS NATURE. YOU SAW 
THIS SO-CALLED CO-K PLEX PROBLEM. 
WHICH YOU SEE HERE BY COLORING IN 
CERTAIN OF THE DOTS. AND WE CAN 
DO THIS ABOUT 500 TIMES FASTER ON 
THE FPJ WITHOUT THE PARALYZATION. 
THEN YOU GO BACK AND YOU DECORATE 
YOUR GRAPHS. AND THEN YOU DO SCORING 
AND MAP IT BACK ON THE MOLECULES 
AND YOU CAN SEE WHICH MOLECULES 
AGREE AND DISAGREE. WHY IS THIS 
IMPORTANT? NOT JUST IMPORTANT IF 
YOU WANT TO SEE PAINT DRY FASTER, 
BUT THINK ABOUT IT. IF YOU COMPARE 
THE MOLECULES AND ENCODING, THEY 
ARE VERY, VERY SIMILAR. YOU WOULD 
LIKE TO HAVE A COATING THAT IS STRONGER 
BUT DOESN'T HAVE THE SIDE EFFECTS. 
THE ONLY WAY TO FIND THIS MOLECULES 
IS BY COMPARING THEM. AGAIN ACCESS 
TO LARGER MOLECULES. THE NEXT CASE 
STUDY WHERE ONCE PILOT THE MODULAR 
SOFTWARE WITH A PARTNER. ON THE 
RIGHT HAND SIDE, YOU ARE WATCHING 
A VIDEO OF ONE OF OTI'S TRANSPARENT 
ORGANIC SCREENS THAT YOU CAN USE 
FOR HEAD OF THIS PLACE. THIS IS 
REALLY COOL. THEY ARE SPECIALIZED 
IN MAKING THESE SCREENS. AND CREATE 
NEW ORGANIC MOLECULES FOR THIS PLAY 
TECHNOLOGY. SO THEIR APPROACH, COMBINES 
MACHINE LEARNING, SIMULATION, OPTIMIZATION 
STEP THAT I'M AFTER. WITH PROPERTY 
TESTING AND MATERIAL AND VALIDATION. 
IF THEY DON'T FIND THE MATERIAL 
IN THE PROPERTIES THEY JUST ITERATE 
WHAT THEY FOUND THROUGH THEIR DATABASE 
AND KEEP GOING UNTIL THEY HAVE WHAT 
THEY WANT. I WANT TO FOCUS HERE 
ON THIS. THEY CAN HANDLE MATERIALS 
WITH 250 MOLECULES. THEY HAVE THE 
MATERIAL DISCOVERED ON THE QUANTUM. 
WE SAID, LET'S HAVE A LOOK AT THIS. 
WHAT IS THE DIFFERENCE OF WHAT WE 
DO TO WHAT IS TYPICALLY DONE IN 
QUANTUM OPTIMIZATION? QUANTUM OPTIMIZATION, 
YOU ARE LOCKED IN HANDLE TWO LOCAL 
[INDISCERNIBLE]. THESE ARE PROBLEMS 
WHERE EACH VARIABLE, THE BLUE DOT 
HERE CAN ONLY INTERACT WITH ONE 
OR THE OTHER NEIGHBORS. THINK OF 
IT AS A PARTY WHERE EVERYBODY IS 
ONLY ALLOWED TO HOLD HANDS WITH 
ONE PERSON BUT THERE IS NO KUMBAYA 
WHERE EVERYBODY HOLDS ONE POINT. 
THE NATIVE PROBLEM ON THE LEFT HAND 
SIDE, YOU CAN SEE, HIGHER ORDER 
TERMS. THREE PEOPLE HOLDING HANDS. 
FOUR PEOPLE HOLDING HANDS AND SO 
FORTH. AND INSTEAD OF HAVING TO 
LOOK AT THE PROBLEM, WE CAN LOOK 
AT THE K LOCAL PROBLEM. NOW FOR 
A SAKE OF TIME, LET ME SHOW YOU 
WHAT HAPPENS. IF YOU LOOK AT THE 
K LOCAL PROBLEM WITH 48 VARIABLES, 
THIS TURNS INTO TWO LOCAL PROBLEM 
WITH 3, 000 VARIABLES. IF YOU LOOK 
AT THE ONE WITH 95, YOU GET SOMETHING 
WITH OVER 10, 000 VARIABLES. NEED 
TO SAY SOLVING SOMETHING WITH 10, 
000 VARIABLES IS VERY, VERY HARD. 
AS A MATTER OF FACT, WHEN WE SOLVE 
IN 9 SECONDS WOULD OTHERWISE TAKE 
MORE THAN 24 HOURS. AND THIS THING 
HERE THAT WE SOLVE IN ROUGHLY UNDER 
FOUR MINUTES, NOBODY HAS BEEN ABLE 
TO SOLVE BEFORE. OKAY. SO WE CAN 
SOLVE CURRENTLY ENTRACTABLE PROBLEMS. 
JUST LOOKING AT THEM. IN THEIR NATIVE 
FORM. NOW THE FINAL THING THAT I 
WANT TO SHOW YOU, WHICH IS VERY 
EXCITING IS WORK WITH CASE WESTERN 
RESERVE UNIVERSITY. WHERE WE ARE 
ADVANCE MRI RESEARCH. THE OBJECTIVE 
IS SIMPLE OPTIMIZE MRI SCANS. MAKING 
SHORT AND BETTER. THE IDEA HERE 
IS VERY SIMPLE. MRI YOU HAVE ELECTRO 
MAGNETIC PULSES THAT EXCITE THE 
MOLECULES IN YOUR BODY AND HAVE 
CALLS THAT PICK UP THAT SIGNAL AND 
DETERMINE, IS IT FAT, BONE, MUSCLE 
AND SO ON. SO YOU HAVE A SPECIFIC 
PULSE SEQUENCE THAT WILL GO IN A 
SPECIFIC WAY BE ABLE TO DISCERN, 
TICKLE THIS, YES, TICKLE, TO DISCERN 
THE DIFFERENT TYPES OF TISSUE, OKAY. 
HERE THE IDEA IS NOT TO CHANGE THE 
SOFTWARE, NOT TO CHANGE THE HARDWARE, 
JUST PROGRAM THE MACHINE DIFFERENTLY 
AND SEE IF WE CAN DO BETTER. WE 
OPTIMIZE THE PULSE FUSIONS WITH 
THE MONTE CARLO, DO SCANS, PUT SOMEBODY 
IN THE TUBE. WE HOPE THIS PERSON 
DOESN'T GET FRIED. HASN'T HAPPENED 
YET. WHEN WE GET BACK, WE FEED IN. 
AND WE TRUST KEEP GOING LIKE THIS. 
LET ME SHOW YOU SOME RESULTS. WHAT 
YOU SEE HERE IS TWO SCANS. ON THE 
LEFT INSIDE, YOU HAVE THE QIO INSPIRED 
SCAN. ON THE RIGHT HAND SIDE, POWER 
STATE OF THE ART. IF ANYONE CAN 
TELL ME HERE THAT THEY ARE DIFFERENT, 
I WON'T BELIEVE IT. THEY ARE VERY 
MUCH COMPARABLE. HOWEVER THE STUFF 
ON THE LEFT IS A PULL SEQUENCE THAT 
NOTHING ANYBODY HAS BEEN ABLE TO 
ENVISION BEFORE. THAT IS HUGE. I 
CAN UNLOCK SHORTER SCAN TIMES, BETTER 
QUALITY IMAGINING, YOU NAME IT. 
AND YOU MIGHT SAY WHY ARE WE DOING 
THIS? WHY EMULATING QUANTUM PROCESSES 
ON CLASSICAL HARDWARE? WHY? NUMBER 
ONE BECAUSE WE WANT TO DELIVER QUANTUM 
SOLUTIONS TODAY. NUMBER TWO, WE 
CAN JUST CROSS OUT INSPIRED. SOMETHING 
HAPPENED WITH POWERPOINT HERE. OH. 
SO WHAT IF WE HAVE QUANTUM HARDWARE? 
THE ALGORITHMS THAT WE USE IN PHYSICS 
HAVE TYPICALLY A RANDOM WALK OF 
THE CORE. IF WE REPLAY THE CLASSICAL 
RANDOM WALK BY A QUANTUM WALK AND 
IMPLEMENT QUANTUM HARDWARE, WE GET 
SPEED UP OVER CLASSICAL HARDWARE. 
IN OTHER WORDS, ANY ALGORITHMS WE 
RUN TODAY, WE CAN RUN TOMORROW FASTER 
ON QUAN YUM HARDWARE. THIS I LIKE 
TO THANK YOU. AS I SAID, ALWAYS 
THINK ALGORITHMS FIRST. DOWNLOAD 
Q SHARP. INVENT NEW QUANTUM ALGORITHMS. 
THAT IS WHERE THE BIG IMPROVEMENTS 
WILL BE. ENGAGE IN THE QDK. ENGAGE 
IN THE QUANTUM NETWORK. LEARN MORE 
ABOUT QUANTUM. IF YOU HAVE QUESTION, 
SHOOT ME A MESSAGE. THANK YOU. [ 
