Universal Adiabatic Quantum Computer v1.0

UNIVERSAL ADIABATIC QUANTUM
COMPUTER SIMULATOR V1.0
Automatski Solutions
http://www.automatski.com
E: Aditya@automatski.com , Founder & CEO
M:+91-9986574181
© Automatski Solutions 2017. All Rights Reserved.

SO FAR…
 2014 - Automatski Creates The Worlds First 2048/4096 Qubit Quantum Computer
Simulator
 It couldn’t put it into production because of funds ($125m short)
 Status
Runtime Framework
Libraries Gates
Circuits Toolchain
Programming
Language IDE
10 of 200
Reference
Algorithm
Implementations
SaaS Cloud

AUTOMATSKI’S EFFORTS
(LAST 25 YEARS)
1. 25+ years of Fundamental Research
2. 2014
1. 2048/4096 Universal Circuit Quantum
Computer Simulator v0.1
2. <Project put on hold due to funds>
3. 2018 (Mid)
1. Universal Adiabatic Quantum
Computer Simulator v1.0
2. Production Launch
3. Quantum Supremacy!!!
4. 2018 (End)
1. Special Purpose Quantum Annealing
Simulator v1.0

THE DESIGN
Reference Use Case - Boolean
Satisfiability
Domain Specific Adiabatic Application
Module
Universal Adiabatic Quantum Runtime

“IF YOU THINK YOU UNDERSTAND
QUANTUM MECHANICS, YOU DON’T
UNDERSTAND QUANTUM MECHANICS.”
RICHARD FEYNMAN

THE CONCEPT
 Quantum Computing is simply Physics used to solve
Maths Problems. The way it works is by extending your
Maths to Infinite Dimensions of Energy Spaces. The
solution then seems very simple but borders of Near
Infinite Impracticality in this Universe to achieve.
 The best approach is The Automatski Way. To use
Reductions in Hamiltonian (Energy Functions) in Infinite
Hilbert Spaces (aka Dimensions) to achieve the results.
The trick is to maintain or improve the probability of
achieving the solution while doing all this mumbo jumbo. ;-)
 n-qubit Hilbert space has dimension 2^n. And the
Hamiltonian Matrix has the dimensions 2^n X 2^n = 2^2n
elements.
 The age of the Universe is 10^61 Planks Time. Or ~ 2^190
 A 1000 qubit Hamiltonian will have 2^1000 dimensional
Hilbert Space.

THE STRATEGY
i. Design a Hamiltonian (Energy
Function) who’s Ground State
encodes the Solution to the
Problem
ii. Prepare the known Ground State
of a Simple Hamiltonian to Start
with
iii. Interpolate slowly starting from (ii)
to (i)
iv. The final state will encode the
Solution. Decode it to find the
solution.

TIME
Quality of Quantum Algorithm - Polynomial Time vs Exponential Time

HARDNESS OF CLASSICAL
SIMULATION
 Complexity theory. Hardness of sampling
classically from the probability distribution of
measurement outcomes, for a measurement
performed on a quantum system which can be
prepared by a quantum circuit with relatively low
depth.
 Hardness of multiplicative approximation follows from
very plausible assumptions.
 Hardness of additive approximation follows from
reasonable assumptions.
 We have no good classical algorithm for
simulating a quantum circuit classically, or for
simulating the time evolution of a quantum state
governed by a local Hamiltonian.
 Circuit Based Quantum Computers work on Local
Hamiltonians. While Adiabatic Quantum
Computers work on the Global Hamiltonian.

REVERSIBLE COMPUTING
 Quantum Computing is Reversible...
 Reversible computing is a model of computing
where the computational process to some extent
is reversible, i.e., time-invertible. In a model of
computation that uses deterministic transitions
from one state of the abstract machine to
another, a necessary condition for reversibility is
that the relation of the mapping from (nonzero-
probability) states to their successors must be
one-to-one. Reversible computing is a form of
unconventional computing.

EXTREME VALUE THEOREM
 In calculus, the extreme value theorem states that if a real-valued function f is
continuous on the closed interval [a,b], then f must attain a maximum and a minimum,
each at least once.

NP-COMPLETE & NP-
INCOMPLETE
 None of the Solutions based on any type of Quantum Computers are NP-Complete.
And they may or may not deliver a super-polynomial time acceleration.
 QCs always work probabilistically to solve problems. You might get a solution in 'X'
runs. But in no case will any QC ever tell you that the problem doesn't have a solution.
Hence the QC based solutions are NP-Incomplete.
 The good part is that when you know that the problem definitely has a solution then
you can try your luck with QCs or even Heuristics.
 The Problems we try to solve on Quantum Computers are NP-Complete by definition.
 But the Solutions we get on Quantum Computers by running Quantum Computing
Algorithms are NP-Incomplete

 Possible Problems
 A Boolean Satisfiability Solver as a Reference Use Case Implementation
 Is perhaps the toughest application to get right on a quantum computer simulator
 And was chosen precisely because of that reason
 Hence Proved!!!

COMPETITORS
 D-Wave 2000 Qubit
 Technology - Quantum Annealing
 Application Areas
 Optimization
 Machine learning
 Sampling / Monte Carlo
 Pattern recognition and anomaly detection
 Cyber security
 Image analysis
 Financial analysis
 Software / hardware verification and validation
 Bioinformatics / cancer research
 Timeline
 2011 128 Qubit
 2015 1000 Qubit
 2017 2000 Qubit
 Reference Application
 Quadratic unconstrained binary optimization (QUBO)
Types of Implementations
1. Physical Quantum Computers
2. Quantum Computer Simulators

COMPETITORS
 In nature, physical systems tend to evolve toward their lowest energy state: objects slide
down hills, hot things cool down, and so on. This behavior also applies to quantum
systems. To imagine this, think of a traveler looking for the best solution by finding the
lowest valley in the energy landscape that represents the problem.
 Classical algorithms seek the lowest valley by placing the traveler at some point in the
landscape and allowing that traveler to move based on local variations. While it is
generally most efficient to move downhill and avoid climbing hills that are too high, such
classical algorithms are prone to leading the traveler into nearby valleys that may not be
the global minimum. Numerous trials are typically required, with many travelers beginning
their journeys from different points.
 In contrast, quantum annealing begins with the traveler simultaneously occupying many
coordinates thanks to the quantum phenomenon of superposition. The probability of being
at any given coordinate smoothly evolves as annealing progresses, with the probability
increasing around the coordinates of deep valleys. Quantum tunneling allows the traveller
to pass through hills—rather than be forced to climb them—reducing the chance of
becoming trapped in valleys that are not the global minimum. Quantum entanglement
further improves the outcome by allowing the traveler to discover correlations between the
coordinates that lead to deep valleys.

COMPETITORS
 The D-Wave system has a web API with client libraries available
for C/C++, Python, and MATLAB. This allows users to access the
computer easily as a cloud resource over a network.
 To program the system, a user maps a problem into a search for
the “lowest point in a vast landscape,” corresponding to the best
possible outcome. The quantum processing unit considers all the
possibilities simultaneously to determine the lowest energy
required to form those relationships. The solutions are values that
correspond to the optimal configurations of qubits found, or the
lowest points in the energy landscape. These values are returned
to the user program over the network.
 Because a quantum computer is probabilistic rather than
deterministic, the computer returns many very good answers in a
short amount of time—thousands of samples in one second. This
provides not only the best solution found but also other very good
alternatives from which to choose.
 D-Wave systems are intended to be used to complement classical
computers. There are many examples of problems where a
quantum computer can complement an HPC (high-performance
computing) system. While the quantum computer is well suited to
discrete optimization, for example, the HPC system is better at
large-scale numerical simulations.

COMPETITORS
 Microsoft – Quantum Computer Simulator (2017)
 30 Qubits (Desktop)
 40 Qubits (Cloud)
 IBM - Quantum Computer Simulator (2017)
 56 Qubits (Super Computer)
 IBM – Quantum Computer (2018)
 50 Qubits
 Google – Quantum Computer (2018)
 72 Qubits

MACHINES USED
 Development of Solution
 Machine #1
 AMD FX-6300 (6-Core), 32GB RAM
 Eight Year Old Machine
 Physical
 Testing of Parallelization & Concurrency
 Machine #2
 AWS EC2 m4.16xlarge ($3.2/hr)
 64 Core, 256 GB RAM
 Virtual Machine
 Machine #3
 AWS EC2 x1.32xlarge ($13.4/hr)
 128 Core, 1952 GB RAM
 Virtual Machine

FROM HERE…
 We already have a “Reference” Base Line Implementation v1.0 in Production
 The Universal Adiabatic Quantum Runtime is Ready!
 First Use Case of Solving Boolean Satisfiability Problems is Ready!
 Java -> Go -> C++
 On Premise Cloud/SaaS (Human)
 Cloud/SaaS API
 Domain Specific Adiabatic Application Modules

BEFORE THE
A Message from our CFO!!!

MESSAGE FROM THE CFO!!!
 We are looking for Research & Development Grants
 Between
 $1m to $100m
 $1m will help us with the Research
 $100m will help us develop Path Breaking Solutions for over 10+ Domains using the
Breakthrough with a Big Bang.

AUTOMATSKI FUNDAMENTAL
RESEARCH
 Fundamental Research at Automatski has been working for the last 20-25+ years on
solving the toughest problems on the Planet.
 We have solved 7 NP-Complete Problems and 4 NP-Hard Problems, including the N-
Queens Completion Millennium Problem.
 We are applying them towards breakthroughs in 50+ Technology Domains
 These problems are considered unsolvable in a 1000 years given the current state of
Human Technology and Capability

SOLVED PROBLEMS
 N-Queens Completion
(Millennium Puzzle) Clay
Math Institute
 3-Sat/k-Sat
 Knapsack***
 Longest Common
Subsequence
 Travelling Salesman
Problem***
 3DM/nDM
 Graph Coloring -
Chromatic Number
 Linear Programming
 Integer Programming
 Mixed Integer
Programming
 Quadratic Programming
 Universal Expression
Programming
 Global Optimum in Hyper
Dimensional Space
 K-Means Clustering
 Universal Clustering
Algorithm
 Universal Constraint
Programming/Scheduling
 Integer Factorization***
 Prime Number Test***
 Universal Regression
 Non-Linear Random
Number Generation
 Automatic Theorem Proving
 Universal Experience
 Universal Heuristics
 Consciousness, Mind, Brain
 Genomics
 Billion & Trillion Actor Nano
Second Framework
 Universal Multi-Scale Simulations
 Internet Scale Rule Engine
 Internet Scale Workflow Engine
 Perfect Finance/Markets
 Perfect Environment
 Compromised All Cryptography
(RSA-2048, Elliptic Curve etc.)
 Post Quantum Cryptography
 Logarithmic Gradient Descent
Convergence
 Blackbox Function
Cracking/Reversal
 Hash Reversal (Incl. SHA-
256/512, LanMan etc.)
 NP-Complete Machine Learning
Algorithms (Clustering,
Regression, Classification)
 NP-Complete Deep Learning
Algorithms (ALL)
 Artificial General Intelligence
 Robotics (Simulations + RAD)***
 Universal Emotions
 Universal IQ
 Universal Creativity

FUND OUR RESEARCH
Together we can build the foundations of a better world

THE DEMO!

NEXT STEPS FOR
PROSPECTIVE
CUSTOMERS Please contact sales at info@automatski.com
 Send an email with the following information…
 Desired Application Area for Quantum Computing
 Any Domain Specific Applications?
 Time Frame for Purchase
 Number of Annual Subscriptions Needed (1 Subscription
can run one job at one time)
*** No Free Trials/Pilots/POCs Offered

AVAILABLE
CONFIGURATIONS
 1000 Qubits (Entry Level)
 10,000 Qubits
 100,000 Qubits
 1,000,000 Qubits (Real World Problems)
 10,000,000 Qubits
 100,000,000 Qubits (Extreme)

WARNING!!!
 Don’t contact us asking for The Source Code
 Don’t contact us asking us to
 File Patents
 Make Public Disclosures of our Algorithm(s)
 Publish Academic Papers

SAMPLE PROBLEMS &
RESULTS
 The .Zip File Contains
1. Sample Problems
2. And their Solutions
 Download .Zip File here
 http://bit.ly/2xsxHkz
 Download this Presentation here
 http://bit.ly/2xtwkCg

APPENDIX
 First, a (potentially complicated) Hamiltonian is found whose ground state describes the solution to the
problem of interest. Next, a system with a simple Hamiltonian is prepared and initialized to the ground state.
Finally, the simple Hamiltonian is adiabatically evolved to the desired complicated Hamiltonian. By the
adiabatic theorem, the system remains in the ground state, so at the end the state of the system describes
the solution to the problem. Adiabatic quantum computing has been shown to be polynomially equivalent to
conventional quantum computing in the circuit model.
 The time complexity for an adiabatic algorithm is the time taken to complete the adiabatic evolution which is
dependent on the gap in the energy eigenvalues (spectral gap) of the Hamiltonian. Specifically, if the
system is to be kept in the ground state, the energy gap between the ground state and the first excited state
of H(t) provides an upper bound on the rate at which the Hamiltonian can be evolved at time t. When the
spectral gap is small, the Hamiltonian has to be evolved slowly.
 In practice, there are problems during a computation. As the Hamiltonian is gradually changed, the
interesting parts (quantum behavior as opposed to classical) occur when multiple qubits are close to a
tipping point. It is exactly at this point when the ground state (one set of qubit orientations) gets very close
to a first energy state (a different arrangement of orientations). Adding a slight amount of energy (from the
external bath, or as a result of slowly changing the Hamiltonian) could take the system out of the ground
state, and ruin the calculation. Trying to perform the calculation more quickly increases the external energy;
scaling the number of qubits makes the energy gap at the tipping points smaller.

Universal Adiabatic Quantum Computer v1.0

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