Concrete may have found it's killer app in graphene. Graphene, an abundant two-dimensional material has only been synthesized in the last few years. Graphene’s high electron mobility gives it a current density 1,000 times greater than silicon. Graphene might be used in ultra-thin, fast, batteries. Graphene might be able to store gigabytes of data in a space the size of a pencil eraser. With more recent theoretical advances it might be used as a quantum computer. One thing that will become apparent very quickly, graphene can be used to create all three. Graphene is the new Silicon. We're not far off. What Is Quantum? Quantum computers rely on qubits, the quantum counterpart of the digital bit. A bit can be a 0 or a 1. A qubit can be a 0, a 1, or a superposition of 0 and 1. A qubit in a superposition can have a 0 and a 1 simultaneously. The qubits will be engineered using quantum bits using a system called a quantum annealer which is modeled after a child’s Rubik's cube. The Rubik's cube is solved by looking at the final orientation of the cube and figuring out the combination of twists and turns required to get it to the solved position. A quantum annealer does the same thing. It's solved by looking at the final state of the quantum annealer and figuring out the combination of time and energy it required to get it to the solved state. Quantum is complicated. It's even more complicated than silicon or even graphene. It's not quite there yet. Although D-Wave's quantum computers are doing well on D-Wave's own benchmarks, they can't compete with ordinary computers on other D-Wave benchmarks. A quantum computer is like a Rubik's cube with an unsolved state. A quantum computer can be thought of as having the possible answers to all possible problems that can be solved. To get to that state requires solving another Rubik's cube, one with all possible answers, or as Feynman might say, all possible histories. Feynman has often said quantum computers that can solve problems that can't be solved by a classical computer are in the future. It's difficult to get a handle on the complexity of quantum computers. One of the great minds in quantum information is Richard Feynman. On April 14, 1982, Feynman gave a speech at the University of Washington in Seattle. The text of the speech was published in a book called Quantum Computing and Quantum Computation. The basic idea is that if we want to solve a problem we first need to know what that problem is. To know what the problem is, we have to know the information about that problem. To know the information about a problem we have to give a quantum system the information to know the answer. Then we have to see if the quantum system gives an answer we can understand. We can run this loop over and over until we understand what the answer was. This is pretty much what we do now with all our computing. At the time of Feynman’s talk, no one knew how to do that in quantum mechanics. It was a purely abstract idea. It didn’t help much with understanding what to do with quantum computers in the real world. Even today, quantum computers face some of the same challenges as D-Wave. It can be argued that quantum computing hasn't really taken off because they are not yet scalable. We can't turn quantum computers into quantum processors. Instead, quantum computer makers need to create a quantum simulator. With a quantum simulator, a computer is able to simulate quantum mechanics in a time frame similar to the computer of the 1960’s. This is only useful if the computer can be programmed to solve problems with quantum mechanics. While most computers today are digital, the first generation quantum computers will be analog computers with the number of electrons in their transistors scaled in powers of ten. In 2003 Seth Lloyd and Yuri Manin created a system called a quantum gate array or QGA. The idea was to look at the challenges faced by the early integrated circuit designers and apply them to quantum computers. A QGA is a computational model for a quantum computer based on a massive parallel array of quantum circuits. Although a quantum computer needs to be quantum to be a quantum computer, it only needs to be classical to be a classical computer. A QGA is a digital computer, although there are analog variants, i.e. superconducting. A QGA is a form of analog computer. It's like a neural network made of transistors. The number of gates in a QGA depends on the complexity of the problem to be solved. It has to have an “enough” number of elements in a quantum superposition that provides the quantum interference required to get a solution. Just like neurons in the brain must fire the right neurons at the right time to produce coherent behavior, a quantum computer must provide the right quantum interference between a sufficient number of qubits to solve the problem. The number of quantum gates depends on the size of the Hilbert space. A large Hilbert space is like having many memories. A larger Hilbert space means you need more memory to solve a quantum problem. A large Hilbert space is also required to support large entanglement between qubits. The more entanglement you have, the more information you have about the output of a quantum system. At a quantum level the classical computer looks for entanglement as soon as it runs a quantum algorithm. The more entanglement, the greater the output is for an input with an entangled bit. The more entanglement, the more information you have. Scalability. It's the holy grail of quantum computing. Until D-Wave’s processors came along no one had ever been able to solve a classically unsolvable problem on a quantum computer. Feynman’s idea about giving quantum systems the information to solve the problem helped a little. The quantum computer had to know the problem and the information required to solve the problem in order to be successful. Feynman's idea of quantum computers wasn't specific to gate arrays, it was a general statement about solving problems using quantum mechanics. The quantum computer needs a lot of memory or more properly a lot of Hilbert space. This is where it gets a little fuzzy. For a classical computer you can run a program you can have up to the 1014 bits of information. For quantum computers, it doesn't take that much memory. The reason is we are talking about the memory of a quantum computer. The computer doesn't have a classical memory. The quantum computer doesn't have a classical memory. Instead it has to get the data out of the entanglement between qubits. The entanglement between the qubits is what contains the problem. When a quantum computer has the information to solve the problem it will evolve and use it to solve the problem. If it doesn't have the information to solve the problem, it won't evolve. It can't solve the problem. It has to solve the problem. For a classic computer you just run the program. If the answer is wrong, you try again. The quantum computer is entangled with the problem. It will evolve if you give it the information to solve the problem. This will evolve and produce the solution. If the solution is correct you get the answer. If the solution is not correct, you get nothing. Here's where we get into the complexity of quantum computing. If we have a 1x1 or 3x3 matrix and the memory to solve the problem, we can do this relatively quickly. The number of qubits on the quantum computer is limited to the size of the matrix, i.e. 10x10. The only problem is that the qubits need to be entangled to solve the problem. There is no shortcut to entanglement. The entanglement between the qubits is a problem. The number of configurations of a given matrix with the same number of digits is enormous. To solve the problem requires running the matrix over and over for every configuration. This is the complexity of a classic computer. It requires all the data. Quantum computing requires entanglement to solve the problem. If the problem can't be solved in this universe it's not going to be solved in the next universe. The reason it is so difficult is it requires the solution to the problem in the real world. With quantum computers entanglement is the real world. Quantum computers aren't any smarter than classical computers. A quantum computer is an extension of a quantum computer. It has a qubit, the quantum version of the classic bit, like a quantum algorithm that uses a quantum register. A quantum algorithm may use a quantum register which can take on two states, e.g. 1 and 0, or two values, i.e. 0 or 1. In the quantum algorithm we run the algorithm with a quantum register. When a qubit is in a superposition it can be in a 0 and a 1 at the same time. A quantum computer is capable of storing entanglement in a quantum register for a short period of time. In principle, a quantum computer can store entangled qubits. Where quantum computers still remain a hypothesis, quantum algorithms have been proven to run. Feynman's idea about quantum mechanics being similar to digital computers led to a different way of doing quantum computing, quantum simulation. Quantum