Quantum computers could someday perform certain calculations faster than classical computers, with applications in science, medicine, security, finance, and beyond – but first, researchers need to improve the underlying science and technology. Since its launch in 2020, the Quantum Systems Accelerator (QSA) has already made major advances in both hardware and programming, improving the quantum tools that researchers hope will help solve some of humanity's biggest questions.
QSA is one of the Department of Energy's five national quantum information science research centers with a focus on all three major technologies for quantum computing: superconducting circuits, trapped-ion systems, and neutral atoms.
"We believe there are synergies between these three big technologies and that each one may have unique abilities and applications for solving different kinds of problems," said Rick Muller, the director of QSA and a senior manager at Sandia National Laboratories. "By looking at all three of them together, we can more easily find their strengths, apply innovations across technologies, and design a path forward to a universal quantum computer."
Led by Lawrence Berkeley National Laboratory (Berkeley Lab), QSA brings together more than 250 experts from 14 other institutions: Sandia National Laboratories, University of Colorado Boulder, MIT Lincoln Laboratory, Caltech, Duke University, Harvard University, Massachusetts Institute of Technology, Tufts University, UC Berkeley, University of Maryland, University of New Mexico, University of Southern California, University of Texas at Austin, and Canada's Université de Sherbrooke.
Together, QSA researchers are developing ways to better control qubits (the building blocks of quantum computers), finding algorithms and applications for current and emerging quantum information systems, and speeding their transfer to industry. QSA is also preparing the next generation of quantum scientists through activities, including peer mentoring programs, career fairs, and training for high school students and teachers.
"We're catalyzing national leadership in quantum information through co-design of quantum devices, algorithms, and engineering solutions, with the goal of delivering quantum advantage," said Bert de Jong, the deputy director of QSA and a senior scientist at Berkeley Lab. "We're advancing imperfect quantum technologies and figuring out how we in academia and the national laboratories working with our partners in industry can start using them today. At the same time, we're preparing scientists to use them to solve big science questions."
In March, the Quantum Systems Accelerator issued a full impact report on advances made since the center launched in 2020. Here are five highlights achieved by QSA scientists and partners so far:
Studied Quantum Magnetism and Matter With a 256-Atom Computer (Assembled Using Laser Beams)
QSA researchers from Harvard University and MIT used a special quantum device to observe several exotic states of matter for the first time and studied magnetism at the quantum level. Their findings help explain the physics underlying materials' properties and could be used to engineer exotic materials of the future. Their research was performed using a "programmable quantum simulator" similar to a quantum computer. The team at Harvard built the simulator using hundreds of laser beams known as "optical tweezers," arranging 256 ultra-cold rubidium atoms that acted as qubits. By some measures, that makes it the largest programmable quantum processor demonstrated to date. By moving the atoms into shapes such as squares, honeycombs, and triangles, QSA scientists manipulated how the qubits would interact with one another and made important measurements of quantum phases of matter and quantum spin liquids.
Stacked Qubit Layers on Microchips to Help Computers Grow
One way to build a useful quantum computer is by connecting qubits with superconducting circuits, which can conduct electricity without energy loss when extremely cold. But with every qubit added, engineering the connections and electronics becomes more difficult. You can imagine a group of qubits spread out like a grid on a piece of paper; trying to snake connections to the innermost qubits causes crowding that can degrade the qubits or signals. To address the challenge, scientists at MIT and MIT Lincoln Laboratory are taking inspiration from commercial electronics and investigating qubits with layers. These stacks of electronic chips reroute the connections to attach vertically, as though perpendicular to our grid – a kind of "3D integration." The change allows researchers to potentially connect, control, and read larger numbers of qubits. Through funding from QSA and other partners, they've already built and tested a "2-stack" qubit chip (with two layers), and QSA researchers are working on further enhanced versions. This milestone is an important step toward more densely packed qubits that can perform more complex calculations.
Made a Record-Setting Quantum Sensor That Can Be Used to Hunt Dark Matter
Any study that uses electronics is limited by random variations or noise that can hide the information researchers are searching for. Quantum systems, such as arrays of ultracold atoms, can be used to make extremely precise measurements that are better at picking the signal from the noise. Led by the University of Colorado Boulder, QSA researchers built a quantum sensor from 150 beryllium ions (atoms with an electric charge) arranged in a flat crystal. By using entangled particles, where a change in one immediately impacts the other, the quantum sensor measured electric fields with more than 10 times the sensitivity of any previously demonstrated atomic sensor. Picking up incredibly tiny changes makes such a sensor a powerful tool that could potentially enhance gravitational wave detectors or look for dark matter, one of the biggest mysteries in modern physics.
Harnessed Machine Learning to Correct Errors in Real Time
To improve quantum computers, researchers need a way to find and correct errors, such as a qubit randomly flipping between 0 and 1. Methods such as continuous quantum error correction (CQEC) keep an eye on qubits and look for telltale signs of problems – but they too are subject to noise that can hide issues. QSA researchers at UC Berkeley designed a machine learning algorithm that can process the CQEC signals and find errors more accurately than current real-time methods. Because the new algorithm is flexible, learns on the job, and requires small amounts of computing power, it could improve continuous error correction systems and support larger and more stable quantum computers.
Designed a Simpler Way to Link up Qubits
Our everyday computers use circuits with logic gates (such as "AND," "OR," and "NOT") to perform operations. Quantum circuits can also use gates as their building blocks – but instead of devices like transistors, their gates are made of qubits and interactions between qubits. While one or two entangled qubits can be used for basic operations, linking together many qubits can speed up computations, simplify quantum circuits, and make computers more powerful. QSA researchers led by Duke University developed a new, one-step method of creating these more versatile gates with multiple entangled qubits. Their technique expands logic operations for quantum computers, and includes a particular kind of gate (known as an N-Toffoli gate) that experts predict will be important in quantum adders, multipliers, and other algorithms – including ones with applications in cryptography.