Quantum Time Crystals

Time crystals—how scientists created a new state of matter

>>> What is a [Quantum] Time Crystal?

A time crystal or space-time crystal is a state of matter that repeats in time, as well as in space. Normal three-dimensional crystals have a repeating pattern in space, but remain unchanged as time passes. Time crystals repeat themselves in time as well, leading the crystal to change from moment to moment.

Its considered a new state of matter.


See this short introductory video

See this longer more technical video

>>> History

The idea of a quantized time crystal was first described by Nobel laureate Frank Wilczek in 2012. In 2014 Krzysztof Sacha predicted the behavior of discrete time crystals in a periodically-driven many-body system and in 2016, Norman Yao et al. proposed a different way to create discrete time crystals in spin systems.

From there, Christopher Monroe and Mikhail Lukin independently confirmed this in their labs. Both experiments were published in Nature in 2017.

In 2019 it was theoretically proven that a quantum time crystal can be realized in isolated systems with long range multi-particle interactions.

>>> How were they first created?

Folks at University of Maryland took 10 Ytterbium atoms and then used a laser to create an electromagnetic field around those atoms. Which entangled various atoms in repeating patterns before blasting it with a second laser which jostled the atoms.

And as predicted as energy was introduced into the system – it never stopped and after a while it started moving in an oscilating pattern which was not created by the laser in the first place.

But a team from Harvard did it in a totally different way. They used Nitrogen Vacancy Centers which are flaws in diamonds.

Since then people have created Time Crystals in multiple other ways.

>>> Ultracold & Condensed Matter Physics

We need to create Time Crystals at one ten thousandth of a degree from absolute zero (0.0001K or -273.15°C). So thats ultracold physics.

And crystals are basically condensed matter [physics].

>>> Broken Time Translation Symmetry & Law of Conservation of Energy

Symmetries in nature lead directly to conservation laws, something which is precisely formulated by the Noether theorem

The basic idea of time-translation symmetry is that a translation in time has no effect on physical laws, i.e. that the laws of nature that apply today were the same in the past and will be the same in the future. This symmetry implies the conservation of energy.

Noether Theorem

Noether's theorem or Noether's first theorem states that every differentiable symmetry of the action of a physical system has a corresponding conservation law. The theorem was proven by mathematician Emmy Noether in 1915 and published in 1918, after a special case was proven by E. Cosserat and F. Cosserat in 1909

>>> Applications

First of all Time Crystals make a Perfect Time Piece. We can use them to create clocks that are infinitely more precise than current Atomic Clocks. Because of their constant, repeating motion in time despite no external input. Their atoms are constantly oscillating, spinning, or moving first in one direction, and then the other. They endlessly move perfectly in repeating patterns with time. At a certain frequency without using any extra external energy input. They could also improve technology such as gyroscopes, and systems that rely on atomic clocks, such as GPS.

The quantum nature of time crystals shift from moment to moment in a predictable, repeating pattern — can be used to simulate large, specialized networks, such as communication systems or artificial intelligence.

“In the classical world, this would be impossible as it would require a huge amount of computing resources,” said Marta Estarellas, one of the first authors of the paper from the National Institute of Informatics. “We are not only bringing a new method to represent and understand quantum processes, but also a different way to look at quantum computers.” their goal is to propose real applications for embedding exponentially large complex networks in a few qubits, or quantum bits.

>>> New & Better Topological Quantum Computers

“Time crystals form when arbitrary physical states of a periodically driven system spontaneously break discrete time-translation symmetry.” What the researchers noticed is that when they introduced “one-dimensional time-crystalline topological superconductors” they found a fascinating interaction where “time-translation symmetry breaking and topological physics intertwine—yielding anomalous Floquet Majorana modes that are not possible in free-fermion systems.”

Majorana fermions are particles that have their own anti-particles.

The research was led by Jason Alicea and Aaron Chew from CalTech, as well as David Mross from the Weizmann Institute in Israel.

While studying Majorana fermions, the team observed that it is possible to enhance topological superconductors by coupling them to magnetic degrees of freedom that could be controlled. “Then we realized that by turning those magnetic degrees of freedom into a time crystal, topological superconductivity responds in remarkable ways,” shared Alicea.

One way the phenomen noticed by the scientists could be potentially exploited is to create more stable qubits – the bit of quantum information in quantum computing. The race to create qubits is at the threshold of bringing on a true quantum technology revolution.

“It’s tempting to imagine generating some useful quantum operations by controlling the magnetic degrees of freedom that intertwine with the topological physics. Or perhaps certain noise channels can be suppressed by exploiting time crystals,” said Alicea.


>>> The Bullshit Explanation

Unlike clocks or any other known objects, time crystals derive their movement not from stored energy but from a break in the symmetry of time, enabling a special form of perpetual motion.

The nonsense explanation given by 3000 BC Pseudo Scientists is that – Time crystals “spontaneously” break time-translational symmetry . They emphasize “spontaneous” breaking of Time translation symmetry and Laws of conservation of energy. That somehow is ok according to their theories of physics. Hence they imply and assert that their theoretical Laws of Time Translation Symmetry and Laws of Conservation of Energy still hold as unviolatable laws of physics of our universe.

We all know that this is utter rubbish. Automatski has uncovered the underlying algorithm of the functioning of the universe. And we can state that this is the worst explanation possible and a nonsensical effort to still stick to one’s archaic outdated theories even in the face of counter-facts. This is an act of desperation by 3000 BC pseudo science in the absence of better explanations and theories at their disposal.

>>> Conclusion

While Time Crystals are a new state of matter and the entire physics community is excited about it and its prospective applications. Including the possibility of being able to create Topological Quantum Computers which will be error free.

But more than that this is a inflection point moment in history. We are looking at the possibility of completely new physics. And having to develop new physics theories to explain our universe. (Outside Automatski) At Automatski we pretty much have all this covered since 1990’s. But for the rest of the world this is a moment for a reality check.

What is Quantum Machine Learning Exactly?

Quantum Machine Learning | SpringerLink
Quantum Machine Learning

>>> Lets understand why would anyone use Quantum Computers? For Machine Learning?

Its proven that Quantum Computers cannot solve NP problems in sub-exponential time. Which was the primary purpose of inventing quantum computers. These are all the problems humanity has not been able to solve using Classical Computers.

Then, why does one use Quantum Computers? What do we hope to achieve? What benefits can anyone derive from them?

Well, Quantum Computers can deliver ‘acceleration’ over classical computers for solving the same problems albeit differently using Quantum Mechanics Principles. Normally we expect Quadratic Acceleration or rarely Exponential Acceleration at best. (Over Classical Computers)

So that is our intent in using Quantum Computers for Machine Learning. To get acceleration over classical computers. That is to solve machine learning problems many times faster than classical computers.

>>> So, what does Quantum Machine Learning involve?

The Model, Structure & Parameters

With machine learning we do two things together. Firstly we learn from the data we have. And secondly for unseen data we make predictions using what we have learnt. The thing we learn is called ‘The Model’

The model has a structure and parameters. Structure basically means how it is internally designed or connected, its width, depth, layers etc. And parameters basically mean the numerical values used in the model that represent and correspond to the data we feed into it to learn from.

The Quantum Data & State Preparation

It is also quite clear that what we have at our disposal is Classical Data. While what the Quantum Computer can operate on is quite complex due to its quantum nature. For example we know that 100 Qubits can represent 2^100 combinations. Which a quantum algorithm can use.

Hence we have to load and convert the classical data into a more compressed, complicated form for processing by the Quantum Machine Learning Algorithm. This is done by loading and encoding to prepare the initial quantum state in terms of Qubits. Which are then put into the Quantum Circuit to execute. Please see the second column ‘QML algorithm’ in the first diagram above to understand how this step differs from classical machine learning in the first column.

Parametrized Quantum Circuits

Without going into details about how exactly a Quantum Machine Algorithm is represented as Quantum Circuits that can be executed on a Quantum Computer to learn from data. We should note that the circuit corresponding to ‘any’ quantum machine learning algorithm will have a specific structure and will have a lot of parameters used in it which we hope to learn from data.

Such circuits are in general called parameterized quantum circuits.

>>> The Overall Workflow

Such solutions are called as Hybrid Quantum-Classical Solutions because the higher level logic is executed in a Classical Program which delegates intractable problem solving functionality to a Quantum Program.

In technical terms such solutions are also called Variational Quantum Algorithms. The classical program sets some parameters at a time and executes the parameterized circuit on a quantum computer. It does this many many times e.g. millions. Each time with different parameters for the quantum circuits. In doing this repetitive process the classical program tries to figure out the best parameters for the parameterized quantum circuit. And hence in a sense ‘learns’ the best parameters. Which basically in our case means that the solution will learn the ‘Best’ Quantum Machine Learning Model from the data we had at our disposal.

Such hybrid solutions can run reasonably well on NISQ quantum computers. Because ‘any’ hybrid solution ‘cannot’ give a perfect answer. All such algorithms are heuristics. And they can give approximate answers at best. Which is great for the NISQ quantum computers we have at our disposal because they are also not accurate and can only solve problems approximately or never at all.

>>> The HHL [Quantum] Algorithm

The HHL algorithm is a quantum algorithm for solving a linear systems of equations, designed by Aram Harrow, Avinatan Hassidim, and Seth Lloyd, formulated in 2009. The algorithm estimates the result of a scalar measurement on the solution vector to a given linear system of equations.


Why is HHL Algorithm so important?

The algorithm at the center of the “quantum machine learning” mini-revolution is called HHL. Many of the subsequent quantum learning algorithms extend HHL or use it as a subroutine. It is the single most important underlying quantum algorithm in quantum machine learning.

>>> Conclusion

A generic overview of quantum learning methods. The algorithm column names the classical learning method. The papers column refers to the most important papers related to the quantum variant. The Grover column indicates whether the algorithm uses Grover’s search or an extension thereof. The speedup column indicates how much faster the quantum variant is compared to the best known classical version. Quantum data refers to whether the input, output, or both are quantum states, as opposed to states prepared from classical vectors. The column generalization performance refers to whether this algorithm generalizes and performs well to unseen data. Implementation refers to attempts to develop a physical realization vis-a-vis a theoretical analysis.


Outside Automatski, we are far from developing scalable universal quantum computers. Learning methods, however, do not always require universal quantum computing hardware: special cases of quantum machine learning are attainable with quantum annealing quantum computers just by using optimization models.


>>> Where can I learn more?


The World Of Quantum Computing Status Update Nov-2020

So what is happening in the world of quantum computing around us?

Without any specific order. Lets talk about the tools and applications startups are building for Quantum Computing.

7 powerful SEO & management tools to make your social ...

>>> Tools

Horizon Quantum Computing

They are pioneering an approach to quantum programming that allows programs written in a single unified language to be compiled and run on either conventional or quantum computers, producing fast efficient implementations no matter the platform. At the core of their technology is a process that automatically constructs quantum algorithms based on programs written in Matlab or Octave.

*** This approach is like Cross Compilation of Classical -> Quantum Programs

We are not sure how successful this approach will be. Because one is to one translation of Classical Programs into Quantum Programs makes little or no sense. It takes huge engineering and research effort to create a ‘specific’ Quantum Algorithm which solves a ‘classical’ problem. And achieves a Quadratic or Exponential Quantum Advantage. So from where we stand this is a No Go!

QC Ware

Is making Quantum Data Loaders to load data into Quantum Machine Learning Models. It also creates GPU based Quantum Simulators which can do 1000 Gate 20 Qubit Circuits on GPU’s in 6 seconds.

Cambridge Quantum

They are into creating Architecture Agnostic Quantum Solutions. This has huge value because there are multiple Quantum Computers from multiple vendors and there is no standardization yet or expected for the next 10-20 years. So Customers need to be able to build solutions once and deploy it over any Quantum Computer Backend. Without being locked to any specific backend by using its proprietary API’s and Quantum Programming Languages and Frameworks. This is like creating the Java of Quantum Computing.

Zapata Computing

Zapata Computing created a Workflow Engine called ORQUESTRA that allows one to Compose ‘quantum-enabled workflows’TM and execute freely
across the full range of classical and quantum devices. This is quite interesting, because it allows someone to exploit classical quantum inspired algorithms, mix and use Quantum Annealing and Gate Based Quantum Computers, and use hybrid – both classical and quantum backends to create solutions. We think this is a great way to go.

Sidenote: Quantum Benchmarks

Some people use useless misleading terms like Quantum Volume to describe a Quantum Computers capabilities. The problem is that if something doesn't work well enough to solve my problems that I'm interested in then a number like Quantum Volume 200 doesn't help. I'm only interested in knowing if a Quantum Computer can solve the problem I want to solve. What the world needs is a suite of representative problems that can be executed periodically on Quantum Computers to create a Benchmark of the performance of Quantum Computers and what customers can expect from them. A metric like Quantum Volume is probably great for Quantum Computer Vendors who want to measure their progress. But from a customers viewpoint it is pretty much useless.


Strangeworks is creating Standards for Quantum Computing Definitions, Performance Metrics & Performance Benchmarking.


Image for post


Alibaba Says Its New “Tai Zhang” Is the World’s Most Powerful Quantum Circuit Simulator. Please see the various techniques used in implementing Quantum Simulators above. Creating a Quantum Simulator is a Research effort that will need to utilize GPGPU’s and FPGA’s and also invent new more efficient algorithms.

University of Michigan

QuIDDPro is a fast, scalable, and easy-to-use computational interface for generic quantum circuit simulation. It supports state vectors, density matrices, and related operations using the Quantum Information Decision Diagram (QuIDD) datastructure. Other efforts have used Matlab, Octave, QCSim, and libquantum to simulate quantum circuits. However, unlike these efforts, QuIDDPro does not always suffer from the exponential blow-up in size of the matrices required to simulate quantum circuits. QuIDDPro is significantly faster and uses significantly less memory as compared to other generic simulation methods.

High Level Programming Languages


One of the newest efforts in this space is Silq, a high-level programming language for quantum computers out of Switzerland’s ETH Zurich. Existing quantum languages for programmers still work at a very low abstraction level, which makes life for quantum programmers a lot harder than necessary. Programming Quantum Computers with low level gate descriptions is erroneous due to the side effects entangled temporary qubits have with other qubits. Silq primarily solves that.


There are other prominent High Level programming languages for example…

  • QCL – A Programming Language for Quantum Computers
  • LanQ – A quantum imperative programming language

IDE – Integrated Development Environment

Eclipse XACC

Eclipse XACC

Eclipse XACC is probably the worlds first IDE for Quantum Computing.

XACC is an extensible compilation framework for hybrid quantum-classical computing architectures. It provides extensible language frontend and hardware backend compilation components glued together via a novel quantum intermediate representation. XACC currently supports quantum-classical programming and enables the execution of quantum kernels on IBM, Rigetti, and D-Wave QPUs, as well as a number of quantum computer simulators.

Eclipse XACC is a programming specification and software framework that tackles the aforementioned challenges and provides a hybrid classical-quantum programming model that enables quantum acceleration within existing classical HPC applications. XACC provides the software interfaces and infrastructure required by domain computational scientists to offload computationally intractable work to an attached quantum accelerator. It handles algorithm programming in a manner similar to OpenCL, with code expressed as language-agnostic quantum kernels, thus enabling XACC to interact with existing quantum programming languages (QPLs) such as Scaffold, Quipper, and QCL. To accomplish this language interoperability, XACC keeps track of associated compilers for each programming language and orchestrates their compilation, or translation, to accelerator-level assembly code. XACC provides developers with two mechanisms for compilation: (1) a runtime API (just-in-time compilation) that enables the control of all aspects of high-level programming, translation, and execution, and (2) a static compiler that transforms invoked quantum kernels into an execution of the compiled result on the accelerator. Both mechanisms delegate to a classical compiler for generation of the hybrid classical-quantum executable. Furthermore, since XACC is extensible in languages and compilers, users can program and execute algorithms suited for either gate model quantum computing or adiabatic quantum computing.



>>> Applications

It seems everyone is trying to solve Chemistry, Finance, Materials, Machine Learning, Molecular Modelling and Drug Discovery Problems with Quantum Computers. So creating those applications for the customers makes sense. We need to be able to define the problem conceptually at a high level and encode it into an algorithm and execute it over ‘any’ suitable Quantum Backend.

Sidenote: Applications for Gate Based Quantum Computers

Everyone understands at a high level how a Gate Based Quantum Computer works. But it is extremely complicated to program one and create algorithms to solve interesting problems. So there is a huge multi-billion dollar market to create Applications that can encode a problem of interest, execute it over 'any' Gate Based Quantum Computer and decode the results into a form which is usable for solving business problems. This is a huge idea for a Quantum Applications Startup.

Sidenote: Applications for Quantum Annealing Quantum Computers

Everyone understands at a high level how a Quantum Annealing Quantum Computer works. But it is extremely complicated to program one and create algorithms to solve interesting problems. So there is a huge multi-billion dollar market to create Applications that can encode a problem of interest, execute it over 'any' Quantum Annealing Quantum Computer or a Classical Solver and decode the results into a form which is usable for solving business problems. This is a huge idea for a Quantum Applications Startup.

The Path Ahead

So how do the above two Applications be offered. Yes, they will primarily be offered as a SaaS service but other than that the Customer just needs to worry about the problem and (the problem description) format.

So lets say the customer needs to crack RSA-2048 (thats his problem [type]) he checks the problem description format for that service and uses that to enter the RSA Key he needs to be cracked. Apart from that he doesn’t need to worry about anything other than the estimate of the costs of using the service for his problem ‘instance’. The service should be able to analyse the problem instance described in the problem description format and offer that estimate (of time and costs). Beyond which it is the service’s job to accurately and efficiently encode [convert] that problem definition into a Gate Based Circuit or a Annealing Optimization problem. Optimize the encodings etc. and execute it over a suitable backend. And then decode the results and give the two numbers which are the factors of the given RSA-2048 key. The customer should just be worried about taking and using those.

Similarly for other problems like Route Optimization, Portfolio Optimization, Trading Decisions etc.

So this kind of a service will be a front end SaaS service to a Quantum Backend Service like Amazon Braket.

>>> Cyber Security

Quintessence Labs

Is working on Quantum Random Number Generators. And Enterprise Key & Policy Management solutions.

Isara Corporation

Isara has emerged as an early frontrunner, working to develop security systems that essentially allow communication between classical and quantum algorithms.

Sidenote: The Insight

It is not just necessary to create Quantum Secure algorithms. One has to build infrastructure and services around them too. Just like we have built for Classical Cryptographic Algorithms over the last few decades.

Until that happens lets just hope nobody outside Automatski develops a quantum computer and breaks all cryptography.

Alice in wonderland – “And who is to be master?”

Don’t get us wrong. We don’t hate anyone. We are polite, gentle soft-spoken people. We also don’t hate XBM or Z-wave or anyone in our village either. They are fighting a turf battle with us, in the hope of growing their fiefdoms. In the race for world domination nothing less. We still meet over a cup of tea occasionally and talk of Cricket, Baseball and Hollywood. But the moment we are back to business we are fierce ruthless competitors. Its not easy for Automatski to defend its inventions worth over $275 trillion. They make sure that we sweat to defend ourselves everyday.

Whats up Automatski? What are you guys upto?

Wassup GIFs | Tenor

You mean besides




Some of the things we are working on…

  • Quantum Mind, Brain, Consciousness, Cognition & Holonomic Brain Theory
  • Quantum Chaos
  • Quantum Logic
  • Temporal Quantum Logic
  • Hyperdimensional Computing
  • Classical (Deterministic & Nondeterministic) Reversible Computing
  • Quantum Probability
  • Quantum Bayesianism

Why are we working on these things? Why not Quantum Computing specifically?

Well, we have the worlds first billion qubit infinite precision circuit based quantum computers. we have billion qudit infinite precision photonic quantum computers. and we will put our quantum gravity quantum computers in production very soon. So what everyone dreams about doing is a direct incremental consequence of the above. They do it because they don’t have a quantum computer of any consequence.

We are focussed on applying our Quantum Computers to solve the worlds problems. Which includes reversing climate change, curing 8000 diseases etc.

We are simulating large plank scale simulations of quantum gravity and trying to create the ‘second’ theory of everything.

What we do is so far ahead of everyone else that we call them 3000 BC pseudo science.

Now lets see what we mean by the terms mentioned above

Quantum Mind, Brain, Consciousness, Cognition & Holonomic Brain Theory

The quantum mind or quantum consciousness is a group of hypotheses proposing that classical mechanics cannot explain consciousness. It posits that quantum-mechanical phenomena, such as entanglement and superposition, may play an important part in the brain’s function and could explain consciousness.

Quantum cognition is an emerging field which applies the mathematical formalism of quantum theory to model cognitive phenomena such as information processing by the human brain, language, decision making, human memory, concepts and conceptual reasoning, human judgment, and perception. The field clearly distinguishes itself from the quantum mind as it is not reliant on the hypothesis that there is something micro-physical quantum mechanical about the brain. Quantum cognition is based on the quantum-like paradigm or generalized quantum paradigm or quantum structure paradigm that information processing by complex systems such as the brain, taking into account contextual dependence of information and probabilistic reasoning, can be mathematically described in the framework of quantum information and quantum probability theory.

*** Automatski is the front runner in Quantum Computing and hence is in a unique position to research Quantum Mind and Quantum Cognition.

Quantum Chaos

Quantum chaos is a branch of physics which studies how chaotic classical dynamical systems can be described in terms of quantum theory. The primary question that quantum chaos seeks to answer is: “What is the relationship between quantum mechanics and classical chaos?” The correspondence principle states that classical mechanics is the classical limit of quantum mechanics, specifically in the limit as the ratio of Planck’s constant to the action of the system tends to zero. If this is true, then there must be quantum mechanisms underlying classical chaos (although this may not be a fruitful way of examining classical chaos). If quantum mechanics does not demonstrate an exponential sensitivity to initial conditions, how can exponential sensitivity to initial conditions arise in classical chaos, which must be the correspondence principle limit of quantum mechanics?

*** Automatski already has a Non-Deterministic Calculus to describe the functioning of the universe. We are not trying to explain the same using ‘regular’ quantum mechanics.

Quantum Logic

In quantum mechanics, quantum logic is a set of rules for reasoning about propositions that takes the principles of quantum theory into account. This research area and its name originated in a 1936 paper by Garrett Birkhoff and John von Neumann, who were attempting to reconcile the apparent inconsistency of classical logic with the facts concerning the measurement of complementary variables in quantum mechanics, such as position and momentum.

Quantum logic can be formulated either as a modified version of propositional logic or as a noncommutative and non-associative many-valued (MV) logic.

*** We are trying to mix Quantum Mechanics Principles into Logic
Temporal Quantum Logic

A model of quantum concurrent program, can be used to model the behaviour of reactive quantum systems and to design quantum compilers. Quantum temporal logic, QTL, for the specification of quantum concurrent systems using the time-dependence of events. QTL employs the projections on subspaces as atomic propositions, which was established in the Birkhoff and von Neumann’s classic treatise on quantum logic.

Hyperdimensional Computing

Hyperdimensional computing (HDC) is an emerging computing approach inspired by patterns of neural activity in the human brain. This unique type of computing can allow artificial intelligence systems to retain memories and process new information based on data or scenarios it previously encountered.

*** It is very exciting to have a method which can compute based on memories and fresh data. Automatski will make some breakthrough announcements very soon.

Classical (Deterministic & Nondeterministic) Reversible Computing

Reversible computing is a model of computing where the computational process to some extent is time-reversible. 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.

This article got delayed. And we have already announced our Reversible Computer.

Quantum Probability

Quantum probability was developed in the 1980s as a noncommutative analog of the Kolmogorovian theory of stochastic processes. One of its aims is to clarify the mathematical foundations of quantum theory and its statistical interpretation.

*** Quantum Probability will allow us to understand Quantum Mechanics better.

Quantum Bayesianism

In physics and the philosophy of physics, quantum Bayesianism (abbreviated QBism, pronounced “cubism”) is an interpretation of quantum mechanics that takes an agent’s actions and experiences as the central concerns of the theory. QBism deals with common questions in the interpretation of quantum theory about the nature of wavefunction superposition, quantum measurement, and entanglement.

According to QBism, many, but not all, aspects of the quantum formalism are subjective in nature. For example, in this interpretation, a quantum state is not an element of reality—instead it represents the degrees of belief an agent has about the possible outcomes of measurements.

*** This is one of our enquiries into Reality and the understanding of our Reality.

The Deal With Hybrid Quantum Computing

What is Hybrid Quantum Computing?

Hybrid can mean a combination of Classical and Quantum Computing. Though some people use Hybrid Quantum Computing to signify Discrete (Qubits Based) and Continuous Variable (Photon Based) Quantum Computing.

1 – Hybrid Classical & Quantum Computing

Companies like D-wave are all gung ho about Hybrid Quantum Computing. They have solvers which can solve lets say 100-300 variable problems. And using a layer of Classical Computing on top of it they attempt to solve problems that are way larger, say 1,000 – 2000 variables.

*** Please note we said 1000 ‘variables’ not Qubits. a 1000 variable problem requires N^2 or even N^3 qubits which means a real world 1000 variable problem might require 1000,000 qubits or 1000,000,000 qubits.


2 – Hybrid Discrete & Continuous Variable Computing

Well Qubits have two basis states |0> and |1> and they exist in a super position of both these states. Thats Discrete Quantum Computing.

Qubits made from things like Photons or Magnetic Fields can have Continuous Variables.

Why would anyone use that? Its way more complex and non-intuitive.

Because CV Quantum Computing promises exponential acceleration of computation. Thats why.

“DV and CV encoding have distinct advantages and drawbacks,” says Hugues de Riedmatten of the Institute of Photonic Sciences in Barcelona. CV systems encode information in the varying intensity, or phasing, of light waves. They tend to be more efficient than DV approaches but are also more delicate, exhibiting stronger sensitivity to signal losses. Systems using DVs, which transmit information by the counting of photons, are harder to pair with conventional information technologies than CV techniques. They are also less error-prone and more fault-tolerant, however. Combining the two, de Riedmatten says, could offer “the best of both worlds.”


So proponents of Hybrid Discrete & Continuous Variable Computing say that they can build Quantum Computers using Photons which can use both Discrete Qubits and Continuous Variable Quantum Computing. And combining the benefits of both. Achieving Nirvana.


At Automatski we believe in solving the low hanging fruit first. Nobody outside Automatski has a working quantum computer (Well, crapware and vaporware aside).

So basically lets put all our money and efforts in building a Discrete Qubit based Quantum Computer before fantasizing about Type 2 Hybrid Quantum Computing. DQQC is Universal so it would be quite an achievement if done. And then we can worry about Type 2 Hybrid Quantum Computing.

As far as Type 1 Hybrid Quantum Computing is concerned. Hybrid Classical + Quantum Algorithms can be proven that they will ‘NEVER’ deliver a perfect solution. In the near term Quantum Computing it might help us solve bigger problems. But in any case it will work only to give us approximate solutions. Which are way off from the global perfect solutions. But something is better than nothing.

The Deal With Distributed Quantum Computing

Distributed Computing - YouTube

What is the “Proposed” Concept of Distributed Quantum Computing?

Lets say you have 10 Quantum Computers each with a 10 Qubit Capacity (like what we have today). So in total they have 100 usable Qubits. Lets say you have a problem to solve which requires 100 Qubits. You breakup the problem in such a fashion that each quantum computer processes a small part requiring 10 qubits and then you aggregate the solutions together to get the final answer.

There is a huge huge problem with that.

What is the problem?

Well Quantum Computers are being built for a reason. And that is to solve the most difficult problems which we have which cannot be solved with Classical Computers. And ‘all’ such problems have one problem. They cannot be broken up into parts, solved in parts and the results aggregated into the final ‘perfect’ solution. If that was possible then these problems would have been solved already.

So basically, this concept of Distributed Quantum Computing won’t work.

But, there is a twist to the story. What is that?

Proponents of DQC say that by distributed we mean just 10 meters. All these QC’s are separate physical entities but they are kept only 10 meters apart in the same room.

OK. Then what?

Then the second twist is that the proponents claim that instead of breaking up the problem into 10 parts. We will integrate all the Quantum Computers such they function as one logical quantum computer with 100 qubits. Then they will be able to solve the 100 Qubit problem? right?

Well, theoretically yes! If such an integration is possible. Which maintains All-To-All Qubit connectivity, coherence times and precision.

So what do we have to do to make this possible?

First of all, such an integration is not possible with Superconducting Qubits because we cannot move them around. It is also not possible with Ion Qubits for the same reason. It might just be possible with Photonic Qubits. Because we can move entangled photons around.


At Automatski we believe in solving the low hanging fruit first. Nobody outside Automatski has a working quantum computer (Well, crapware and vaporware aside).

So basically lets put all our money and efforts in building a ‘Single Piece’ Quantum Computer before fantasizing about Distributed Quantum Computing.

Universal Quantum States, Gates & Circuits

In this article Universal implies – the capability to represent and/or compute anything (any permutation of the possible solution)


So what does one mean by universal state. Well! We normally initialize qubits to |0> state and from that point onwards we pass the qubits through gates to transform their collective entangled states.

Is there a better way to achieve a Universal Quantum State? Yes!

Only in Automatski’s Quantum Computers can we initialize a collection of Qubits to arbitrary/universal states. If !!! If we can design such states.

Our 1WQC One Way Quantum Computers or aka MBQC Measurement Based Quantum Computers development led us to create such a capability.

So basically only in Automatski’s Quantum Computers, if you can conceive an entangled Quantum State you can simply create it at one go.


A Universal Gate can take any sets of Qubit states and produce any other set of Qubit States. These are called permutations. So basically a Universal Gate can create any mapping or permutation from input to output qubit states.

Only in Automatski’s Quantum Computers if you can define a / any Unitary Transformation the Gate can be created.


A Universal Quantum Circuit should be able to compute any permutation of input quantum states to output quantum states . Just like Universal Gates, but gates act only on 1,2, or 3 qubits at a time. While when we talk of Universal Circuits we talk of ‘N’ Qubits. Where N can be 1 or 1000 or 1000,000 or 1000,000,000


These 3 capabilities are the essential and necessary conditions and the essence of Universal Quantum Computing. And this has been achieve only at Automatski.