Scientists have discovered that facts and reality may be subjective to the individual observing them. This is because in quantum mechanics which examines particles on the sub-atomic level particles can exist in a point of superposition. Perhaps the most famous example of superposition is Erwin Schrodingers thought experiment, Schrodingers Cat.

To demonstrate his theory, Schrodinger said a cat placed in a box whose fate depended on the outcome of a random event could be both alive and dead simultaneously, its state only being known when the box is opened.

In quantum physics, these facts are established as a contradiction and a person inside the box would observe a definite answer, while a person on the outside observes a superimposed state.

Massimiliano Proietti, lead author of a new study and PhD student at Heriot-Watt Universitys Mostly Quantum Lab, said: This brings about a paradoxical situation where the fact established inside the laboratory seemingly contradicts the fact observed on the outside.

To test the theory, the team created a quantum test which included four observers in a quantum computer.

Six entangled light particles which means they are in a state of superposition were introduced to the observers.

The team were able to show that inside and outside observers could not agree as to what happened to the entangled particles.

Lab leader Professor Alessandro Fedrizzi, adds: The insight we gained is that quantum observers may indeed be entitled to their own facts. If we insist that this shouldnt be the case for classical human observers, the challenge now is to pin down where the two domains depart from each other.

It may for example hint at quantum mechanics not being applicable to big, everyday objectssomething that is allowed by textbook quantum physics.

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Reality is subjective to the observer - scientists make stunning claim in quantum study - Express.co.uk

Sometimes friendly competition is the best route to realizing new innovations.

This is the primary goal of two recently annouced an open call for hackathons participants taking place between now and mid-December.

01 Communique LaboratoryQuantum encryption

01 Communique Laboratory Inc. launched its quantum hackathon tackling the threat of quantum computing. Cybersecurity companies, computer science students and hackers have begun challenging the Companys quantum-safe encryption in a $100,000 hackathon.

On November 6th, the Company hosted an innovation celebration event with technology presentations from industry experts in artificial intelligence and cyber security. Andrew Cheung, 01 Communiques CEO, was one of the presenters addressing business people, students, and hackers on the threat quantum computers present with respect to keeping your data safe. He revealed the purpose behind the hackathon and why he is confident enough to offer a $100,000 prize.

Andrew Cheung enthusiastically described the hackathon challenge, Our hackathon will show the world that our encryption is rock-solid. We are the only Canadian company and the first post-quantum encryption to offer a prize of $100,000. We have invested over three years in developing our IronCAP technology with a development team that has combined 50 years of experience in code-based encryption. We are very confident that our technology will withstand any attempt by any participant to crack the code in our hackathon.

01 expects contestants from around the world to challenge its quantum-safe encryption. The hackathon is available online globally. Anyone to who has a Google or Facebook account can sign up to participate. Contestants will be given 30 days to crack IronCAPs code. A cash prize of $100,000 will be awarded to the first person (if there is any) who is able to break the encryption. A paper describing the method used to crack the encryption is required to be submitted by the participant.

Innovative people working in tech along with researchers, computer scientists, students and hackers are encouraged to sign up for the hackathon. Signup will be accepted online atwww.ironcap.cabeginning November 11, 2019, and the contest closes on December 12, 2019. Result will be announced on or about December 16, 2019.

Taking flight

Global IT provider Stefanini is hosting an Innovation Hackathon on Dec. 6, 2019 at its Southfield Innovation Center. The focus for this hackathon is to fly a Tello drone using voice commands. The event is open and free and requires some technology knowledge: HTML, Javascript and CSS, HTTP REST API, C# (.NET). Familiarity with Microsoft PowerBI streaming datasets and dashboards is a plus.

This event will provide manyopportunities for participants, both personally and professionally, as they co-create solutions for their future, said Renata Galle, vice president of innovation and digital business at Stefanini.

The companys goal is to attract talent through a dynamic and interactive process, identify and foster new skills within its employees, and connect students and professionals in the local market with its Digital Talent Mall and job opportunities.

A voice recognition platform and a software development kit for the drone will be provided. The teams will have to code the app to interact with the drone, send commands, extract flight information from it, and display some of this flight information back into a visualization dashboard. Each participant will be evaluated and may bepart of Stefaninis Digital Projects and Talent Mall based on:

The teams will have at their disposal different paths to follow to achieve the goal.Those interested in participating are encouraged to registeronline. Details are available here.

Excerpt from:
Innovation Focused Firms Issue Open Call for Hackers - IndustryWeek

Hypothetical fault-tolerant quantum computer based on topological condensed matter

A topological quantum computer is a theoretical quantum computer that employs two-dimensional quasiparticles called anyons, whose world lines pass around one another to form braids in a three-dimensional spacetime (i.e., one temporal plus two spatial dimensions). These braids form the logic gates that make up the computer. The advantage of a quantum computer based on quantum braids over using trapped quantum particles is that the former is much more stable. Small, cumulative perturbations can cause quantum states to decohere and introduce errors in the computation, but such small perturbations do not change the braids' topological properties. This is like the effort required to cut a string and reattach the ends to form a different braid, as opposed to a ball (representing an ordinary quantum particle in four-dimensional spacetime) bumping into a wall. Alexei Kitaev proposed topological quantum computation in 1997. While the elements of a topological quantum computer originate in a purely mathematical realm, experiments in fractional quantum Hall systems indicate these elements may be created in the real world using semiconductors made of gallium arsenide at a temperature of near absolute zero and subjected to strong magnetic fields.

Anyons are quasiparticles in a two-dimensional space. Anyons are neither fermions nor bosons, but like fermions, they cannot occupy the same state. Thus, the world lines of two anyons cannot intersect or merge, which allows their paths to form stable braids in space-time. Anyons can form from excitations in a cold, two-dimensional electron gas in a very strong magnetic field, and carry fractional units of magnetic flux. This phenomenon is called the fractional quantum Hall effect. In typical laboratory systems, the electron gas occupies a thin semiconducting layer sandwiched between layers of aluminium gallium arsenide.

When anyons are braided, the transformation of the quantum state of the system depends only on the topological class of the anyons' trajectories (which are classified according to the braid group). Therefore, the quantum information which is stored in the state of the system is impervious to small errors in the trajectories. In 2005, Sankar Das Sarma, Michael Freedman, and Chetan Nayak proposed a quantum Hall device that would realize a topological qubit. In a key development for topological quantum computers, in 2005 Vladimir J. Goldman, Fernando E. Camino, and Wei Zhou claimed to have created and observed the first experimental evidence for using a fractional quantum Hall effect to create actual anyons, although others have suggested their results could be the product of phenomena not involving anyons. It should also be noted that non-abelian anyons, a species required for topological quantum computers, have yet to be experimentally confirmed. Possible experimental evidence has been found,[1] but the conclusions remain contested.[2]

Topological quantum computers are equivalent in computational power to other standard models of quantum computation, in particular to the quantum circuit model and to the quantum Turing machine model[citation needed]. That is, any of these models can efficiently simulate any of the others. Nonetheless, certain algorithms may be a more natural fit to the topological quantum computer model. For example, algorithms for evaluating the Jones polynomial were first developed in the topological model, and only later converted and extended in the standard quantum circuit model.

To live up to its name, a topological quantum computer must provide the unique computation properties promised by a conventional quantum computer design, which uses trapped quantum particles. Fortunately in 2002, Michael H. Freedman, Alexei Kitaev, Michael J. Larsen, and Zhenghan Wang proved that a topological quantum computer can, in principle, perform any computation that a conventional quantum computer can do.[3]

They found that a conventional quantum computer device, given an error-free operation of its logic circuits, will give a solution with an absolute level of accuracy, whereas a topological quantum computing device with flawless operation will give the solution with only a finite level of accuracy. However, any level of precision for the answer can be obtained by adding more braid twists (logic circuits) to the topological quantum computer, in a simple linear relationship. In other words, a reasonable increase in elements (braid twists) can achieve a high degree of accuracy in the answer. Actual computation [gates] are done by the edge states of a fractional quantum Hall effect. This makes models of one-dimensional anyons important. In one space dimension, anyons are defined algebraically.

Even though quantum braids are inherently more stable than trapped quantum particles, there is still a need to control for error inducing thermal fluctuations, which produce random stray pairs of anyons which interfere with adjoining braids. Controlling these errors is simply a matter of separating the anyons to a distance where the rate of interfering strays drops to near zero. Simulating the dynamics of a topological quantum computer may be a promising method of implementing fault-tolerant quantum computation even with a standard quantum information processing scheme. Raussendorf, Harrington, and Goyal have studied one model, with promising simulation results.[4]

One of the prominent examples in topological quantum computing is with a system of fibonacci anyons.[5] These anyons can be used to create generic gates for topological quantum computing. There are three main steps for creating a model:

Fibonacci Anyons are defined by three qualities:

The last fusion rule can be extended this to a system of three anyons:

Thus, fusing three anyons will yield a final state of total charge {displaystyle tau } in 2 ways, or a charge of 1 {displaystyle 1} in exactly one way. We use three states to define our basis.[6] However, because we wish to encode these three anyon states as superpositions of 0 and 1, we need to limit the basis to a two-dimensional Hilbert Space. Thus, we consider only two states with a total charge of {displaystyle tau } . This choice is purely phenomenological. In these states, we group the two leftmost anyons into a 'control group', and leave the rightmost as a 'non-computational anyon'. We classify a | 0 {displaystyle |0rangle } state as one where the control group has total 'fused' charge of 1 {displaystyle 1} , and a state of | 1 {displaystyle |1rangle } has a control group with a total 'fused' charge of {displaystyle tau } . For a more complete description, see Nayak.[6]

Following the ideas above, adiabatically braiding these anyons around each-other with result in a unitary transformation. These braid operators are a result of two subclasses of operators:

The R matrix can be conceptually thought of as the topological phase that is imparted onto the anyons during the braid. As the anyons wind around each-other, they pick up some phase due to the Aharonov-Bohm effect.

The F matrix is a result of the physical rotations of the anyons. As they braid between each-other, it is important to realize that the bottom two anyonsthe control groupwill still distinguish the state of the qubit. Thus, braiding the anyons will change which anyons are in the control group, and therefore change the basis. We evaluate the anyons by always fusing the control group (the bottom anyons) together first, so exchanging which anyons these are will rotate the system. Because these anyons are non-abelian, the order of the anyons (which ones are within the control group) will matter, and as such they will transform the system.

The complete braid operator can be derived as:

B = F 1 R F {displaystyle B=F^{-1}RF}

In order to mathematically construct the F and R operators, we can consider permutations of these F and R operators. We know that if we sequentially change the basis that we are operating on, this will eventually lead us back to the same basis. Similarly, we know that if we braid anyons around each-other a certain number of times, this will lead back to the same state. These axioms are called the pentagonal and hexagonal axioms respectively as performing the operation can be visualized with a pentagon/hexagon of state transformations. Although mathematically difficult,[7] these can be approached much more successfully visually.

With these braid operators, we can finally formalize the notion of braids in terms of how they act on our Hilbert space and construct arbitrary universal quantum gates.

Explicit braids that perform particular quantum computations with Fibonacci anyons have been given by [8]

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Topological quantum computer - Wikipedia

The Turing machine, developed by Alan Turing in the 1930s, is a theoretical device that consists of tape of unlimited length that is divided into little squares. Each square can either hold a symbol (1 or 0) or be left blank. A read-write device reads these symbols and blanks, which gives the machine its instructions to perform a certain program. Does this sound familiar? Well, in a quantum Turing machine, the difference is that the tape exists in a quantum state, as does the read-write head. This means that the symbols on the tape can be either 0 or 1 or a superposition of 0 and 1; in other words the symbols are both 0 and 1 (and all points in between) at the same time. While a normal Turing machine can only perform one calculation at a time, a quantum Turing machine can perform many calculations at once.

Today's computers, like a Turing machine, work by manipulating bits that exist in one of two states: a 0 or a 1. Quantum computers aren't limited to two states; they encode information as quantum bits, or qubits, which can exist in superposition. Qubits represent atoms, ions, photons or electrons and their respective control devices that are working together to act as computer memory and a processor. Because a quantum computer can contain these multiple states simultaneously, it has the potential to be millions of times more powerful than today's most powerful supercomputers.

This superposition of qubits is what gives quantum computers their inherent parallelism. According to physicist David Deutsch, this parallelism allows a quantum computer to work on a million computations at once, while your desktop PC works on one. A 30-qubit quantum computer would equal the processing power of a conventional computer that could run at 10 teraflops (trillions of floating-point operations per second). Today's typical desktop computers run at speeds measured in gigaflops (billions of floating-point operations per second).

Quantum computers also utilize another aspect of quantum mechanics known as entanglement. One problem with the idea of quantum computers is that if you try to look at the subatomic particles, you could bump them, and thereby change their value. If you look at a qubit in superposition to determine its value, the qubit will assume the value of either 0 or 1, but not both (effectively turning your spiffy quantum computer into a mundane digital computer). To make a practical quantum computer, scientists have to devise ways of making measurements indirectly to preserve the system's integrity. Entanglement provides a potential answer. In quantum physics, if you apply an outside force to two atoms, it can cause them to become entangled, and the second atom can take on the properties of the first atom. So if left alone, an atom will spin in all directions. The instant it is disturbed it chooses one spin, or one value; and at the same time, the second entangled atom will choose an opposite spin, or value. This allows scientists to know the value of the qubits without actually looking at them.

Next, we'll look at some recent advancements in the field of quantum computing.

Excerpt from:
Qubits and Defining the Quantum Computer | HowStuffWorks

Scientists and researchers have long extolled the extraordinary potential capabilities of universal quantum computers, like simulating physical and natural processes or breaking cryptographic codes in practical time frames. Yet important developments in the technologythe ability to fabricate the necessary number of high-quality qubits (the basic units of quantum information) and gates (elementary operations between qubits)is most likely still decades away.

However, there is a class of quantum devicesones that currently existthat could address otherwise intractable problems much sooner than that. These near-term quantum devices, coined Noisy Intermediate-Scale Quantum (NISQ) by Caltech professor John Preskill, are single-purpose, highly imperfect, and modestly sized.

Dr. Anton Toutov is the cofounder and chief science officer of Fuzionaire and holds a PhD in organic chemistry from Caltech. You can follow him at @AntonToutov.

Dr. Prineha Narang is an assistant professor of computational materials science at the John A. Paulson School of Engineering and Applied Sciences at Harvard University. You can follow her @NarangLab.

As the name implies, NISQ devices are noisy, meaning that the results of calculations have errors, which in some cases can overwhelm any useful signal.

Why is a noisy, single-purpose, 50- to few-hundred-qubit quantum device exciting, and what can we do with it in the next five to 10 years? NISQs provide the near-term possibility of simulating systems that are so mathematically complex that conventional computers cannot practically be used. And chemical systems definitely fit that bill. In fact, chemistry could be a perfect fit for NISQ computation, especially because errors in molecular simulations may translate into physical features.

To understand this, its valuable to consider what noise is and how it occurs. Noise arises because physical and natural systems do not exist in isolationthey are part of a larger environment, which has many particles, each of which are moving in different (and unknown) directions. This randomness, when discussing chemical reactions and materials, creates thermal fluctuations. When dealing with measurement and computing, this is referred to as noise, which manifests itself as errors in calculations. NISQ devices themselves are very sensitive to their external environment, and noise is already naturally present in qubit operations. For many applications of quantum devices, such as cryptography, this noise can be a tremendous limitation and lead to unacceptable levels of error.

However, for chemistry simulations, the noise would be representative of the physical environment in which both the chemical system (e.g., a molecule) and the quantum device exist. This means that NISQ simulation of a molecule will be noisy, but this noise actually tells you something valuable about how the molecule is behaving in its natural environment.

With errors as features, we may not need to wait until qubits are hyperprecise in order to start simulating chemistry with quantum devices.

Perhaps the most immediate application for near-term quantum computers is the discovery of new materials for electronics. In practice, however, this research is often done with little or no computer-based optimization and design. This is because it is too hard to simulate these materials using classical computers (except in very idealized scenarios, such as when there is only a single electron moving in the whole material). The difficulty comes from the fact that the electrical properties of materials are governed by the laws of quantum physics, which contain equations that are extremely hard to solve. A quantum computer doesnt have this problemby definition the qubits already know how to follow the laws of quantum physicsand the application of NISQs to the discovery of electronic materials is an important research direction in the Narang lab.

What is special about electronic materials is that they are usually crystalline, meaning that atoms are laid out in an organized, repeating pattern. Because the material looks the same everywhere, we dont need to keep track of all atoms, but only of a few representative ones. This means that even a computer with a modest number of qubits may be able to simulate some of these systems, opening up opportunities for highly efficient solar panels, faster computers, and more sensitive thermal cameras.

Chemical research has been going on for centuries, yet new chemistry is most typically discovered by intuition and experimentation. An application of quantum devices in which we are particularly interested at Fuzionaire is the simulation of chemical processes and catalysts, which are substances that accelerate chemical reactions in remarkable ways. Catalysts are at the heart of the entire chemical industry and are relied on each day in the production of medicines, materials, cosmetics, fragrances, fuels, and other products. Significant challenges exist, but this area is a very important opportunity for NISQ devices in the next five to 10 years.

For example, the Haber-Bosch synthesis (HB) is an industrial chemical process that turns hydrogen (H2) and nitrogen (N2) into ammonia (NH3). HB makes it possible to produce enough ammonia-based fertilizer to feed the world, but the process is energy-intensive, consuming approximately 1 to 2 percent of global energy and generating about 3 percent of total global CO2 emissions.

At the heart of the entire process is a catalyst based on iron, which is only active at high temperatures and without which the process fails. Scientists have been trying to discover new catalysts for HB that would make the chemistry more efficient, less energy-intensive, and less environmentally damaging. However, the catalyst discovery and testing process is challenging, painstaking, and costly. Despite many decades of tremendous effort by chemists and engineers, the iron catalyst discovered over 100 years ago remains the industrial state-of-the-art.

Near-term NISQ systems would be used to give chemists unprecedented insights into the inner workings of the current iron catalyst in its physical environment and would be applied to simulate novel, viable catalyst architectures, including those based on elements other than iron.

Biological systems are extraordinarily complex, which makes modeling and simulation very challenging. Prediction of biological molecules and biochemical interactions with conventional computers, especially in biologically relevant environments, becomes difficult or impossible. This forces even basic, earliest-stage biomedical research to be done by working with chemicals, cells, and animals in a lab and hoping for reproducible conditions between experiments and organisms. This is why drug discovery, a vital area of biomedical innovation that encompasses both chemistry and biology, is such a tantalizing opportunity for NISQ intervention.

Developing new medicines for cancer, neurodegenerative diseases, viruses, diabetes, and heart disease is one of the most important activities within the entire chemistry enterprise. However, the current reality is that bringing a new drug to market continues to be slow and costly, to the tune of about 10 to 15 years and more than $2 billion, by some estimates.

A central challenge within the drug discovery process is to identify a biological target that has relevance to human disease and to design molecules that could inhibit that target with the hope that this would treat the disease. Quantum devices could be used to simulate common biological targets such as kinases, G-protein-coupled receptors (GPCRs), and nuclear receptors in their dynamic environments and in complex with inhibitor molecules. These simulations would enable drug discovery scientists to identify potentially active molecules early in the process and discard non-actives from consideration. The most promising drug candidate molecules would then be synthesized and promoted to biological studies (e.g., pharmacology, toxicology) in the laboratory.

While there are great opportunities for near-term quantum devices and much hope for improved systems in the future, we must not get carried away. Research will need to solve significant challenges, including creating systems with many more qubits, improving qubit performance, and developing coding languages for quantum computers, among others.

Nevertheless, there are great reasons to be optimistic as we look forward to the next five to 10 years. Significant resources are being committed by large companies like IBM, Google, and Microsoft to quantum computing efforts; healthy investment is flowing into quantum hardware startup companies like Rigetti, D-Wave, IonQ, and others; and important academic results are being reported using current or near-term quantum devices, including solving lattice protein folding problems, predicting the optical response of exotic materials, investigating the mechanism of nitrogen fixation by nitrogenase, and many others.

As a professional chemist and physicist, were excited about the current capabilities and optimistic about the utility of near-term quantum devices. Were hopeful that these systems will provide to the scientific community new insights that will accelerate discovery and help us solve problems to improve the human condition.

WIRED Opinion publishes pieces written by outside contributors and represents a wide range of viewpoints. Read more opinions here. Submit an op-ed atopinion@wired.com

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Noisy Quantum Computers Could Be Good for Chemistry Problems ...