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Introducing Quantum Information Science: From Bits to Qubits

Syracuse University’s Quantum Information Science research cluster aims to harness the potential of quantum computers. But what is quantum information?

Image of a quantum computer
A real-life quantum computer made by IBM. Quantum computers work differently from classical computers and need to be cooled to extremely low temperatures, leading to this chandelier-like design.

The most pressing questions facing researchers today require deep and broad knowledge, often spanning multiple disciplines. To tackle these problems, Syracuse University is establishing groups, or “clusters,” of scholars from diverse backgrounds dedicated to working on common projects. The clusters were chosen as areas where the University has potential to find breakthrough or breakout solutions to society’s greatest challenges. One field recently added to the research clusters is quantum information science.

Why do we need quantum information science?

To study the smallest particles that exist, scientists from all over the world came together to construct the largest machine every built. The Large Hadron Collider (LHC) is a 17-mile-long track, built deep underground, on the border of France and Switzerland. It uses almost 10,000 magnets, kept at a temperature of about -450 degrees Fahrenheit, and has cost over $4 billion. It has collected and archived 100 petabytes of data—the equivalent amount of HD-quality video would take more than 800 years to watch. Ideally, the results of particle collision experiments at the LHC would be used to confirm results predicted by theories. However, the events at the LHC are so complex that we cannot yet solve the equations governing them precisely, either by hand or on today’s computers. Until we can do that, we must simply wait and continue collecting data.

Elsewhere, drug companies are also studying particle interactions, but with the goal of designing molecules for use in medicines. To test the behavior of these molecules, researchers often use computer simulations. But even the most powerful computers cannot simulate the complexity of large molecules, making it necessary to use approximations, or to abandon the simulations altogether and turn to trial-and-error laboratory tests. In any case, finding the optimal molecule for a given task means testing each possible solution, one at a time.

Many of today’s problems rely on computers to store and process vast amounts of data to find a single, optimal result. From finding the fastest route between two locations or predicting whether it will rain next week, to understanding the fabric of our universe or discovering life-saving medicines, even the most powerful computers do not have the ability to predict solutions exactly, and researchers must run intensive experiments or make approximations to run simulations.

Although the speed of computers increases each year, so does our demand. Instead of trying to make our current computers do more of the same, we need a different type of machine altogether. The idea for such a computer has been around since the mid-20th century but gathered momentum in the 1980s and 1990s. It is called a quantum computer, and the study of how it works is called quantum information science. Quantum computers offer the new approach we need to store and process data, and they happen to be particularly poised to solve optimization and particle physics problems.

What is quantum information?

Imagine we both close our eyes and I flip a coin. The coin lands, but we keep our eyes closed. We don’t know the state of the coin—whether it has heads or tails facing up—but we know that the coin is in one of two possible states. Each of us has a fifty-percent chance of guessing the state correctly, but the coin is and always will be heads-up or tails-up.

Now imagine that, instead of a coin, I use an object that has different rules. If we open our eyes and look at the object, it will be in one of two states, just like the coin. But if we keep our eyes closed, the object will not be sitting there in that same state, just waiting for us to look. Instead, while our eyes are closed, the object is in a different, third kind of state.

How do we know that the object is in this third state without being able to see it? Moreover, how do we know that the everyday coin isn’t in this third kind of state before we open our eyes? These are the kinds of questions that quantum mechanics elicits and then answers, proving over and over that this different kind of state really does exist, and that it really is different than its classical counterpart.

In a classical computer, like the one on your desk or in your phone or in your car, information is stored in the physical states of objects in the computer. The nature of these objects is that they can, like coins, be in one of two states (heads or tails, 1 or 0, on or off), and nothing in between. In a computer with a hard drive, these objects are little magnets that can each point only north or south, while in a computer with a solid-state drive, these objects are transistors that are either charged or not charged. These objects are called bits. Just as we build a word that carries meaning by putting specific letters in a specific order, a computer constructs a piece of information from specific values of bits in a specific order.

A quantum computer also stores information in the physical states of objects. These objects, called qubits, will also, when measured, each be found to be in either a 1 or 0 state. Unlike a bit, however, the state of a qubit before we measure it is different. It’s a third kind of state. Amazingly, this third state is related to the other two, and we know exactly how. The state of the qubit before being measured is a combination of its likelihoods of being found to be a 1 or a 0.

The qubit is a new object with which to do computation. While the information contained in a bit is classical (either 1 or 0) a qubit contains quantum information. And because this is an object with new properties, it can potentially solve new problems, or old problems in new ways, using quantum computation techniques that were not accessible before. The new rules a qubit offers for doing computation are strange (Einstein called one of them “spooky”), but if we can understand them and harness them, the power of computers could grow tremendously.

Quantum Computing Diagram
From bits to qubits. It takes 256 bits to reproduce all the states contained in a single qubit. As the number of qubits increases, the number of states they contain grows exponentially. This means that for each additional qubit, the number of bits needed to represent the qubits’ states doubles. Single qubits are represented as blue dots, while the number of corresponding bits is shown in squares of 256 white dots. A set of four squares of bits is represented as a blue triangle. (Click on the image to enlarge it.)

What can quantum information do?

Just the fact that the state of a qubit can be something other than 1 or 0 means that a single qubit can hold an amount of information that a classical computer would need whole sequences of bits to construct. It’s true that if we measured the state of the qubit, we would lose all that special information, but it is actually possible to use the qubit for computation without measuring it, while it’s still in that third kind of state. This means that problems that require checking and comparing data, like cracking passwords or finding the best route between two places on a map, will become, literally, exponentially easier.

In addition to being able to do old computations in new ways, qubits offer the potential to solve problems that are intractable with classical computers. For example, simulating quantum-mechanical systems becomes much more straightforward using objects that are quantum-mechanical themselves. Particle collisions, like those at the LHC, or molecular interactions for potential pharmaceuticals, are therefore natural candidates for quantum computation.

“We are at a critical juncture in the field of quantum information,” says Britton Plourde, a professor of physics in the College of Arts and Sciences. “Applications of quantum computing are already being pursued intensively by many corporate research labs and new startup companies.” Quantum computation’s potential applications have also garnered attention from governments around the world. “The Chinese government has invested $11 billion recently to establish a national lab in this area, and in 2019 the U.S. enacted the National Quantum Initiative, which commits $1.2 billion to quantum technology efforts,” Plourde says.

For young researchers, the field offers an opportunity to contribute to cutting-edge applications of experimental and theoretical physics, chemistry, engineering and computer science. “Undergraduates involved in this type of research will have many research options if they choose to attend graduate school and career opportunities if they decide to work in industry,” says Plourde. The Quantum Information Science Cluster at Syracuse University will provide undergraduate and graduate students with a program to explore this field, both through classes and research helmed by a diverse group of scholars.

Amanda Parker

This story was published on .


Also of Interest

  • The College of Arts and Sciences

    The founding college of Syracuse University remains at the center of undergraduate learning. The College is divided into the natural sciences and mathematics, the humanities, and the social sciences, with the lattermost offered in partnership with the Maxwell School of Citizenship and Public Affairs.

  • Quantum Information Science (QIS)

    Students interested in Quantum Information Science (QIS) will find excellent opportunities at Syracuse University. The heart of the field is quantum computing that uses multilevel quantum systems (qubits) instead of classical semiconductor bits. The impact could be revolutionary on subjects ranging from fundamental science (quantum gravity, black holes, neutron stars, molecular structure and dynamics) to current technologies (cryptography, data mining, supply chains, cybersecurity, and artificial intelligence).