“I think it is safe to say that no one understands quantum mechanics.”
Physicist Richard P. Feynman
First proposed in the 1970s, quantum computing relies on quantum physics by taking advantage of certain quantum physics properties of atoms or nuclei that allow them to work together as quantum bits, or qubits, to be the computer's processor andmemory. By interacting with each other while being isolated from the external environment, qubits can perform certain calculations exponentially faster than conventional computers
The massive amount of processing power generated by computer manufacturers has not yet been able to quench our thirst for speed and computing capacity. In 1947, American computer engineer Howard Aikensaid that just six electronic digital computers would satisfy the computing needs of the United States. Others have made similar errant predictions about the amount of computing power that would support our growing technological needs. Of course, Aiken didn't count on the large amounts of data generated by scientific research, the proliferation of personal computers or the emergence of the Internet, which have only fueled our need for more, more and more computing power.
Will we ever have the amount of computing power we need or want? If, asMoore's Law states, the number of transistors on a microprocessor continues to double every 18 months, the year 2020 or 2030 will find the circuits on a microprocessor measured on an atomic scale. And the logical next step will be to create quantum computers, which will harness the power of atoms and molecules to perform memory and processing tasks. Quantum computers have the potential to perform certain calculations significantly faster than any silicon-based computer.
Scientists have already built basic quantum computers that can perform certain calculations; but a practical quantum computer is still years away. In this article, you'll learn what a quantum computer is and just what it'll be used for in the next era of computing.
You don't have to go back too far to find the origins of quantum computing. While computers have been around for the majority of the 20th century, quantum computing was first theorized less than 30 years ago, by a physicist at the Argonne National Laboratory. Paul Benioff is credited with first applying quantum theory to computers in 1981. Benioff theorized about creating a quantum Turing machine. Most digital computers, like the one you are using to read this article, are based on the Turing Theory. Learn what this is in the next section.
Determining the natural diamond flaws and their practical scientific use has been in existence for close to ten years now. However, using lasers to effectively control quantum areas within the flaws has indeed opened up possibility realms for endless probabilities in future.
The most important thing to highlight is that scientists have been making excellent use of diamond flaws to test what are the chances of successfully developing semiconductor quantum nanoscale sensor and bits.
Entanglement : this is phenomenon of two states to be correlated. When you know one state you can evaluate other state. By Einstein's law, For an Entangle state, For a moving object, if you know its momentum, you can evaluate its position if you know its position you can evaluate its momentum.
By experimentally we can evaluate one value other can be determined by using entanglement.
Hilbert Space
Hilbert space is the vector notation of quantum mechanics. In this we try to solve the condition for result of input. The essential results in quantum mechanics are given through purely algebraic relations. Specific results can be derived, e.g., for vectors l X l and matrices being linear maps; however, those results are essentially independent of the specific representation of the operators. For the specific results only algebraic relations between operators and abstract properties of the Hilbert space enter. This point of view allows to consider problems in full generality and then consider a specific representation of the basis vectors of the Hilbert space and the operators ( e.g., matrices, differential operators ).
We call an inner product space a Hilbert space if it is complete as a normed space. Equivalently we may say that an inner product space is called a Hilbert space if it is a Banach space.
Quantum gates are same as logic gates, but they are having complex output.
Few Quantum gates are:-
- Hadamard Gate
- CNOT Gate
- Unitary Gate
Logic gates in Quantum mechanics works in the reversible operations, which means that by knowing the output you can predict input.
Hadamard gate:- Simplest gate involves one qubit and is called a Hadamard Gate ( also known as a square-root of NOT gate ) . Used to put qubits into superposition. This give the relation of entanglement. this can be used to find out input when the output is known. One simple state is multiplied by hadamard gate gives complex output and complex state give simple output.
CNOT Gate:- A gate which operates on two qubits is called a Controlled-NOT (CN) Gate. If the bit on the control line is 1, invert the bit on the target line. In this one bit is control bit another bit is operand bit so when we change control bit operand bit changes.
The CN gate has a similar behavior to the XOR gate with some extra information to make it reversible.
Unitary Gate:- Unitary operators are like Identity matrix as in Linear Algebra. So they used multiple for the operation on any two bits so that the output is interference of two Q-bits. which is used for the calculation of matrix problem
Abstract
A method of performing a quantum Fourier transform in a quantum computing circuit is disclosed. The method includes forming a quantum computing circuit as a collection of two-qubit gates operating on a sequence of input qubits. Auxiliary qubits are then interacted with the original input qubits to place the auxiliary qubits in a state corresponding to an output of a discrete Fourier transform of a classical state of the input qubits. The original input qubits are then re-set to their ground state by physically interacting the input qubits with the auxiliary qubits. The auxiliary qubits are then transformed to a state representative of a quantumFourier transform of the sequence of input qubits.
The present invention provides for the first time a quantum mechanics-based method for scoring protein-ligand interactions and binding affinity predictions, using quantum mechanical Hamiltonians and/or a combined quantummechanical/molecular mechanical approach, and Poisson-Boltzmann (PB)-based solvation methods. Also provided is a method for using quantum mechanics to describe the enthalpic and solvation effects of binding. The method comprises comparing the calculated binding affinities to experimental values in order to measure the success of the method. The methods disclosed herein may further be used to score protein and drug or protein and inhibitor interactions. The present method can predict the free energy of binding of protein-ligand complexes with high accuracy so as to enable lead optimization, thus serving as a powerful tool in computational drug design.
How long have people been thinking about quantum computation?
The idea of quantum computing was proposed in the 1980s by physicists like Richard Feynman and David Deutsch, but it wasn't obvious that a quantum computer would be good for anything.
The only application people could see immediately was you could use a quantum computer to simulate quantum mechanics. That's sort of obvious.
The big discovery that sort of got people excited about this field was when Peter Shor discovered in [1994] that [if you had a quantum computer], you could use it to find the prime factors of enormous numbers.
That's a practical problem we don't know how to solve with [conventional] computers in any reasonable amount of time.
People care about it because the security of e-commerce is based on the difficulty of finding prime factors. If you can do that you can break most of the cryptography on the internet.
How likely is it that the US National Security Agency has succeeded in creating practical quantum computers?
People have speculated about that possibility. I don't know. I don't have the security clearance. But there are some things that make me think it's not likely.
One of them is that we know who the best experimentalists are, and yes the NSA is interested and talks to them and funds them, but we haven't seen them hoovering them up like the Manhattan Project.
The more important thing is that if your goal is to read people's e-mail, there are so many more straightforward ways to go about that than building a quantum computer. It's an exotic possibility that captures people's imagination, but in reality, when these systems are broken, it's not by bashing down the fortress, it's by finding a back door.
[Edward] Snowden himself said properly implemented strong crypto is one of the things you can rely on.
There are so many more prosaic possibilities I'd want to examine before considering the possibility that the NSA is building a quantum computer.
There's also just that it looks to most of us like [quantum computing is] in a basic research stage.
It doesn't look like it's at the point where people could say: "Here's how much money it would take and here's how many years it would take and we can build a device." We still don't know. We're still just trying to figure out which are the basic architectures.
Maybe in 5 or 10 or 20 years it becomes a question of time and money and manpower and how much do people want this thing. Right now, it's a research question of how do you do it at all.
Latest milestone in Quantum computing:
- August 2013. Huff... QCrypt is over. It was a heavy task for our institute and our students to host the premier conference in the field.
- May 2013. Postdoc position in our group is open for a good, highly qualified researcher.
- January 2013. Our laboratory has been the first one from the Institute for Quantum Computing to move in to the new Mike & Ophelia Lazaridis Quantum-Nano Centre building. We are located in room QNC 3303 (and will relocate to QNC 3301 once additional construction there is complete). We have to say thebuilding is fantastic, and the wait has been worth it.
- September 2012. Quantum teleportation experiment over record 143 km distance has been published in Nature
Logarithm used in Quantum Computing:
Interesting finding #1: V6 is the first superconducting processor competitive with state of the art semiconducting processors.
Processors made out of superconductors have very interesting properties. The two that have historically driven interest are that they can be extremely fast, and they can operate without requiring lots of power. Interestingly they can even be run close to thermodynamical reversibility — with zero heat generation. There was a serious attempt to make superconducting processors work, at IBM from 1969 to 1983 — you can read a great first hand account of it here. Unfortunately the technology was not mature enough, semiconducting approaches were immensely profitable at the time, and the effort failed.Subsequently there has been much talk about doing something similar but with our new knowledge, but no-one has followed through.
It is difficult to find the amount of investment that has gone into superconducting processor R&D. As best I can count, the number is about $4B. We account for about 3% of that number; IBM about 15%; and government sponsorship of basic research, primarily in Japan, US and Europe the remainder. Depending on your perspective, this might sound like a lot, or like a very small number — for example, a single TSMC state of the art semiconductor fabrication facility costs about six times this (~$25B) to build. The total investment in semiconductor fabrication facilities and equipment since the early days of Fairchild Semi is now approaching $1T — yes, T as in Trillion. That doesn’t include any of the investment in actual processors — just the costs of building fabrication facilities.
The results that were recently published in the Ronnow et. al. paper show that V6 is competitive with what’s arguably the most highly optimized semiconductor based solution possible today, even on a problem type that in hindsight was a bad choice. A fact that has not gotten as much coverage as it probably should is that V6 beats this competitor both in wallclock time and scaling for certain problem types. That is a truly astonishing achievement. Mattias Troyer and his team achieved an incredible level of optimization with his simulated annealing code, achieving 200 spin updates per nanosecond using a GPU based approach. The ‘out of the box’ unoptimized V6 system beats this approach for some problem types, and even for problem types where it doesn’t do so well (like the ones described in the Ronnow paper) it holds its own, and even wins in some cases.
This is a remarkable historic achievement. It’s the first delivery on the promise of superconducting processors.
Interesting finding #2: V6 is the first computing system using ideas from quantum information science competitive with the best classical computing systems.
Much like in the case of superconducting processors, the field of quantum computing has promised to provide new ways of doing things that are superior to the ways things are now. And much like superconducting processors, the actual delivery on that promise has been virtually non-existent.
The results of the recent studies show that V6 is the first computing system that uses ideas from quantum information science that is competitive with the best classical algorithms known run on the fastest modern processors available.
This is also a remarkable and historic achievement. It’s the first delivery on the promise of quantum computation.
Interesting finding #3: The problem type chosen for the benchmarking was wrong.
The type of problem that the Ronnow paper looked at — random spin glasses — made a lot of sense when the project began. Unfortunately about midway through the project it was discovered that this type of problem was expected theoretically to show no difference in scaling between simulated annealing (SA) and quantum annealing (QA). This analysis showed that it was necessary to add structure to the problem instances to see a scaling difference between the two. So if an analysis of the D-Wave approach has as its objective observing a scaling difference between SA and QA, random spin glass problems are the wrong choice.
Interesting finding #4: Google seems to love their machine.
Last week Google released a blog post about their benchmarking efforts that provide an overview of how they feel about what they’ve been seeing. Here are some key points they raise in that post.
- In an early test we dialed up random instances and pitted the machine against popular off-the-shelf solvers — Tabu Search, Akmaxsat and CPLEX. At 509 qubits, the machine is about 35,500 times (!) faster than the best of these solvers.
This is an important result. Beating a trillion dollars worth of investment with only the second generation of an entirely new computing paradigm by 35,500 times is a pretty damn awesome achievement. NOTE FOR EXPERTS: CPLEX was NOT run in these tests to global optimality. It was run in a mode where it was timed to the time it found a target solution, and not to the time it took to prove global optimality. In addition, Tabu Search is nearly always the best tool if you don’t know the structure of the QUBO problem you are solving. Beating it by this much is a Big Deal.
- For each classical solver, there are problems for which the hardware does much better.
This is extremely cool also. Even though we are now talking about the best solvers we know how to create, our Vesuvius chip, with about 0.001% of the investment of its competitor, is holding its own.
- A principal reason the portfolio solver is still competitive right now is actually rather mundane — the qubits in the current chip are still only sparsely connected.
This is really important to understand — making the D-Wave technology better is likely about making the problems being solved more rich by adding more couplers to the chip, which is just an engineering issue that is nearly completely decoupled from other things like the role of quantum mechanics in all of this. It is really straightforward to make this change.
- Eyeballing this treasure trove of data, we’re now trying to identify a class of problems for which the current quantum hardware might outperform all known classical solvers.
Now this is really cool. Even for Vesuvius there might be problems for which no known classical computer can compete!
Interesting finding #5: The system has been running 24/7 with not even a second of downtime for about six months.
This is also worth pointing out, as it’s quite a complex machine with the business end at or around 10 millikelvin. This aspect of the machine isn’t as sexy as some of the other issues typically discussed, but it’s evidence that the underlying engineering of the system is really pretty awesome.
Interesting finding #6: The technology has come a long way in a short period of time.
None of the above points were true last year. The discussion is now about whether we can beat any possible computer — even though it’s really only the second generation of an entirely new computing paradigm, built on a shoestring budget.
The next few generations of chip should push us way past this threshold — this is by far the most interesting time in the 15 year history of this project.
In Terminator 2, Arnold reveals that his CPU is a neural net processor, a learning computer. Of course it is! What else would it be? Interestingly, there are real neural net processors in the world. D-Wave makes the only superconducting version, but there are other types out there also. Today we’ll use one of our superconducting neural nets to re-run the three experiments we did last time.
I believe this is the first time quantum hardware has been used to train a DBM, although there have been some theoretical investigations.
Embedding into hardware
Recall that the network we were training in the previous post had one visible layer with up to four units, and two hidden layers each with four units. For what follows we’re going to associate each of these units with a specific qubit in a Vesuvius processor. The way we’re going to do this is to use a total of 16 qubits in two unit cells to represent the 12 units in the DBM.
All D-Wave processors can be thought of as hardware neural nets, where the qubits are the neurons and the physical couplers between pairs of qubits are edges between qubits.Specifically you should think of them as a type of Deep Boltzmann Machine (DBM), where specifying the biases and weights in a DBM is exactly like specifying the biases and coupling strengths in a D-Wave processor. As in a DBM, what you get out are samples from a probability distribution, which are the (binary) states of the DBM’s units (both visible and hidden).
In the Vesuvius design, there is an 8×8 tile of eight-qubit unit cells, for a total of 512 ‘neurons’. Each neuron is connected to at most 6 other neurons in Vesuvius. To do the experiments we want to do, we only need two of the 64 unit cells. For the experts out there, we could use the rest to do some interesting tricks to use more of the chip, such as gauge transformations and simple classical parallelism, but for now we’ll just stick to the most basic implementation.
Here is a presentation containing some information about Vesuvius and its design. Take a look at slides 11-17 to get a high level overview of what’s going on.
The theory of quantum electrodynamics (QED) which describes the interaction of light and matter is the most accurate theory in all of science, providing almost unbelievably accurate agreement with experiment. Yet in the middle of the twentieth century the theory was in a deep crisis. Calculations of even the simplest of events in the subatomic world, like the absorption and emission of a photon by an electron, seemed to give nonsensical infinite results that flew in the face of finite values from experiment. These infinities dotted the landscape of physics like ugly tumors, leading some to believe that physics was fundamentally on the wrong track. But hope was at hand. It took a whole post-war breed of brilliant young scientists to invent an ingenious set of tricks collectively called "renormalization" to get rid of these infinities and restore the theory to a complete form. Renormalization not only axed the infinities in QED but became the test that any fundamental theory of physics had to pass before being deemed acceptable. In a stunning set of successes, it was applied to the unification of the weak and electromagnetic forces and then to the strong force holding protons and neutrons together. In this book Frank Close tells us how all this happened.
The descriptions of basic physics models is always very challenging, and Close tries to do a good job of it. He does manage to get across a lot about how fundamental particles behave, but the various theories he discusses are just names, with no substantive content. I know that in mathematics, there are many areas that simply cannot be explained to the non-mathematical layperson, and that may be true of modern physics as well. However, in other fields that I know (population biology and economics, for instance) the important stuff can be fully explained with only the most minimal use of mathematical formalism. I am searching for a popular account of the Standard Model with this attractive feature.
Quantum superclock
Physicists say they believe they’re on track to creating a “quantum superclock” that would revolutionize the way the world tells time.
If the work proves to be a success, than the concept of time as it’s currently understood could be changed drastically and allow a whole new idea of accuracy to prevail.
According to a study published by the researchers this week in the Nature Physics scholarly journal, it might soon be possible to harness the power of a global quantum network of clocks to “allow construction of a real-time single international time scale (world clock) with unprecedented stability and accuracy.”
The study — “A quantum network of clocks” — calls for “a quantum, cooperative protocol for operating a network of geographically remote optical atomic clocks.”
“Using nonlocal entangled states, we demonstrate an optimal utilization of global resources, and show that such a network can be operated near the fundamental precision limit set by quantum theory,” reads an abstract of their report. “Furthermore, the internal structure of the network, combined with quantum communication techniques, guarantees security both from internal and external threats.”
Broken down, the scientists’ project isn’t all that complicated. Alexandra Witze wrote for the Naturewebsite that, essentially, the researchers are relying on two ideas that are already major points of focus for physicists: atomic clocks as they currently exist, and quantum entanglement, “in which pairs of particles become linked in such a way that measuring a property of one of them instantaneously determines the same property for the other,” she wrote.
By linking a network of orbiting, atomic clocks, those two schools of study may be able to be merged and provide physicists with what would unarguably be the most precise clock in existence. The scientists' response for the Nature Physics story says linking 10 such atomic clocks and putting them into satellite may be the way to proceed.
“One satellite, as the network's center, would start by preparing its clock particles in an entangled state. It would then communicate with a neighboring satellite to extend the entanglement there. The linking would eventually spread through the whole fleet, joining the satellites in one quantum network,” Witze wrote.
“You’d be able to see someone digging a tunnel under the US-Mexico border from space,” Chris Monroe, a physicist at the Joint Quantum Institute at the University of Maryland in College Park, told Science Newsthis week.
Eric Kessler, a co-author of the paper, told Nature that his colleagues’ proposal, while still in the planning stages, is admittedly “a little bit visionary.” Nevertheless, the researchers believe the blueprint does exist to take the theory behind quantum physics and create a network of atomic clocks that would be more accurate than anything ever available.
How to Win at Bridge Using Quantum Physics Science WIRED
Contract bridge is the chess of card games. You might know it as some stuffy old game your grandparents play, but it requires major brainpower, and preferably an obsession with rules and strategy. So how to make it even geekier? Throw in some quantum mechanics to try to gain a competitive advantage.
The idea here is to use the quantum magic of entangled photons–which are essentially twins, sharing every property–to transmit two bits of information to your bridge partner for the price of one. Understanding how to do this is not an easy task, but it will help elucidate some basic building blocks of quantum information theory. It’s also kind of fun to consider whether or not such tactics could ever be allowed in professional sports.
Putting together the nerdier sides of physics and cards has long been the hobby of physicist Marcin Pawlowski of the University of Bristol in the U.K. In 2000, he was a poor college student headed from Poland to China. Trying to save money, he opted to travel overland across the trans-Siberian train route, a trek of several weeks.
“We played bridge a lot on the train,” said Pawlowski. “And I was studying quantum mechanics at the time.”
Bridge is played in teams of two, and a major part of the game involves figuring out how to give your partner information about the cards in your hand using coded signals. Pawlowski realized that quantum particles would allow him to send extra bits of knowledge to his partner during a bridge game. With a team of co-authors and some help from professional bridge players, he wrote a paper about exactly how to do this, which appeared June 12 in Physical Review X.
Bridge is complicated. If you don’t know how to play the game, don’t worry. We won’t be delving too deeply into the details just yet. You do need to know that each round of bridge has two main parts; the auction and then the actual gameplay, which is similar to Hearts or Spades.
In the auction phase, players go around and declare the number of hands they expect to win during gameplay. Whichever team ends up with the highest bid sets the trump suit, the suit that can’t be beat. The bids have to be given in a very specific, constrained vocabulary of 38 words or phrases. This isn’t poker and it’s no good bluffing here, because if you set a bid much higher than you can actually win, you will be penalized points.
The bidding round also serves a second, more important function. Through bids, you are communicating to your partner across the table the strength of your hand. The higher you bid, the better you are saying your cards are. Experienced bridge players have added an additional layer of complexity, where certain types of bids actually communicate very specific things to their partners, like how many aces or kings they hold in their hand.
And here’s where the advantage that quantum mechanics offers comes in. Let’s say that two physicists named Alice and Bob decide to enter a bridge tournament. With them, they bring a laser and a special crystal that produces pairs of entangled photons when hit with the laser. Entanglement is a bizarre quantum mechanical property where two particles are perfectly identical. If you measure the characteristics of one of the pair, you immediately know that the other one is exactly the same.
Alice and Bob place their laser-crystal apparatus on the table, and each holds a device capable of measuring different aspects of photons. They fire the laser on the crystal and each take one of the entangled photons. They have agreed beforehand on a convention to pass information to one another using these implements. In bridge, no team is able to have secrets and so the two physicists have to tell everybody what they’re doing (whether or not their opponents understand quantum mechanics is their own problem).
The cards are dealt and the bidding starts. Bob has strong cards and thinks he and Alice can set the highest possible bid and win all the hands during the gameplay round. But he needs to know if Alice’s cards are good enough to support him in the places where his cards are weak. So he uses an agreed-upon convention to ask Alice indirectly about the strength of her cards.
Alice wants to tell Bob about two things: She has the queen in the suit that Bob is strongest in, and she has one ace in another suit. In normal bridge, conveying these two pieces of information would eat up two rounds of bidding. Because each bid must always be higher than the one before, Alice would also drive up the final contract sending these two signals. But then she and Bob might not have strong enough cards, and would end up bidding too high and losing the round and some points. Usually, Alice would just decide to tell Bob about the ace, because it is more powerful.
But now in Quantum Bridge, Alice can give a single bid that secretly has both pieces of information at the same time. She does this with her entangled photon. She can measure the polarization of her photon in one of two ways, let’s call them angle x and angle y. Based on the cards in her hands, she will choose which of these measurements to make. And then she takes the results and does a calculation, calling out a bid based on both the measurement of her photon and her cards.
Bob hears Alice’s bid. He’s only interested in one of the pieces of information. He has enough aces but wants to know if the trump queen is in his partner’s hand or his opponents’. Bob can try to extract the information he wants by measuring a corresponding angle on his entangled photon and combining that result with the bid he heard. With this method, he will correctly deduce the answer 89.5 percent of the time. Pretty sweet.
Even though the result was just one bit of knowledge about Alice’s cards, the partners have an advantage here because they can send two pieces of information at once, and Bob can then decide which is more relevant to him. Their poor non-quantum bridge opponents will fall behind, able to only send one piece of information at a time with their bids.
There’s a lot of chance at work in both this situation and bridge in general. In the card game, there are about 5.36 × 1028 different possible deals, making any particular scenario unlikely. Quantum mechanics, too, relies on probability. We have to take in to account the odds that Alice has some particular cards and the probability that Bob wants to know one piece of information or the other. All in all, Alice and Bob will win about 2 percent more often with their quantum method than if they had just played bridge normally.
All that for a 2 percent advantage? It may not sound like much, but in a card game like bridge, which is played tournament-style with points accumulating over many rounds, this slight benefit will add up in the long run. Even better, Alice and Bob would get to walk into a bridge game and plop down a bunch of physics equipment. Because they are not specifically sharing messages via the photons (everything is communicated through the bids), it wouldn’t really, technically be against the rules.
“I love the idea,” said physicist Michael Hall of Griffith University in Australia, who was not involved with the paper. “The physics isn’t all that much new, but what’s really cool is this application to something interesting in the real world.”
Hall added that quantum information theorists often make up all sorts of games that help elucidate some principle or method they are researching. But nobody actually plays any of these invented games. In this instance, the researchers were able to show that players could gain a real advantage with quantum mechanics that they wouldn’t have using classical techniques.
Would such a thing ever be allowed in the professional bridge world? Most likely not, Pawlowski said. But on some level, that’s what he wants.
“What we would really hope for is that the World Bridge Federation would say, “You can’t do this.” And then they have to mention the quantum information theory in their rules.”
According to the International Olympic Committee, bridge is considered a sport (it and chess are the only two games classified as “mind sports.”) So what Pawlowski and his team are hoping for is a ruling on their method, which would be the first instance of regulating quantum resources in a professional sport. And that might be the geekiest thing ever.
Entanglement is cross-correlation of two states, where one state is depending upon other
ReplyDeleteIf two state are entangle then by measuring one state we can evaluate other.
ReplyDelete