Although researchers have spent decades investigating quantum computing, it wasn’t until recently that the segment began to draw public attention, thanks in large part to Google’s much-publicized attempts to build a “quantum computer.” In theory, quantum computing will allow scientists to tackle some of the most vexing problems in computer science, most notably machine learning and breaking encryption. How does quantum computing actually work? Unlike conventional computers, which on their most basic, binary level deal in bits (i.e., a zero or a one, or true/false), quantum computers “think” in qubits, which can be a zero, one, or something in-between (i.e., a superposition). In other words, all possible combinations of outcomes can exist at the same moment. In conjunction with an algorithm, these qubits can settle into a “classical” solution, or a certain combination of zeros and ones. If researchers are correct, quantum processors will leverage the infinite possibilities of qubits to process complex problems much more quickly than conventional processors. For example, an encryption puzzle that might take a traditional computer a thousand years to solve could be successfully broken by a quantum system in just a few minutes. Manipulating qubits comes with its own challenges. “One of the primary challenges is that quantum memory elements (“qubits”) have always been too prone to errors,” Google wrote in a blog posting on quantum computing earlier this year. “They’re fragile and easily disturbed—any fluctuation or noise from their environment can introduce memory errors, rendering the computations useless. Even gathering “just a small number of qubits together to repeatedly perform the required quantum logic operations,” the posting concluded, “is just plain hard.” (Google provided the image above. Look, a qubit!) What does it take to break into quantum computing as a profession? As you might guess, a whole lot. Companies that have embarked on quantum research, such as Northrop Grumman, Google and D-Wave, are on the active hunt for staff physicists who already have experience in investigating the architectures that undergird quantum processing. A lot of current work at these firms deals with testing the efficiency of quantum algorithms, and attempting to verify that quantum processing operates in the manner predicted. On a skills level, that means having a Master’s or PhD in physics, mathematics, and computer science, with a strong background in the technical disciplines and classical computer science. Northrop Grumman, for example, wants researchers with “experience with Clifford simulation and full-scale quantum simulation.” D-Wave, meanwhile, desires machine-learning researchers who can help design the machine-learning algorithms that play well with quantum processors; a PhD in experimental physics also helps. Quantum computing is one of those fields that seems esoteric at the moment, but has the potential to become very huge over the next few years, provided researchers find a way to reliably build and scale quantum computers. Whether or not that research pans out, there are certainly opportunities for physicists and machine-learning experts with the right combination of skills.