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John Schaibley, PhD
Dr. John Schaibley is professor of physics and optical sciences at the University of Arizona. He is also the university’s first faculty fellow in quantum, a role in which his primary goal is to bring together University of Arizona researchers to create a united, centralized quantum initiative. He earned his PhD from the University of Michigan and has done postdoctoral research at the University of Washington.
Dr. Schaibley recently received the Gordon and Betty Moore Foundation Experimental Physics Investigator Award: a five-year, $1.3 million grant. With Moore Foundation support, his lab will engineer new quantum states in 2D semiconductor devices and further develop synthetic quantum materials, which can exhibit exotic behaviors like superconductivity and magnetism, potentially unlocking fundamental discoveries in condensed matter physics and enabling faster, more energy-efficient computation.
Greg Byrd, PhD
Dr. Greg Byrd is a professor and executive director of the IBM Quantum Innovation Center at NC State. His research is focused on quantum computing, computer architecture, and high-performance parallel systems. Dr. Byrd earned his MS and PhD in electrical engineering from Stanford University.
Dr. Byrd is chair of the IEEE Quantum Technical Community (QTC). He is the co-author of Principles of Superconducting Quantum Computers (2022). Prior to joining NC State, Dr. Byrd was a principal scientist at Celotek Corporation, where he developed software and protocols for the CellCase family of encryptors for ATM networks. He also worked at MCNC, in both the NC Supercomputing Center and the High Performance Computation and Communications Group.
In 1981, Nobel-winning physicist Richard Feynman argued that, since nature is quantum mechanical, simulating it would likely require a computer that operates according to the principles of quantum mechanics. Since then, quantum computing has gone from conjecture to theory to practice.
“When I started in quantum computing back in 2007, almost everything was being done in either national labs or at universities,” Dr. Schaibley says. “The idea of having a functional 100 or 200 or 1,000 qubit quantum computer seemed, to many, an insurmountable challenge. It’s all happened much faster than I would’ve thought.”
Today’s largest quantum processors have crossed the 1,000 physical-qubit threshold (physical qubits are distinct from logical qubits, which are more reliable and built from multiple physical qubits).
But there are several approaches to building the underlying hardware, including trapped ions, superconducting circuits, photonic systems, neutral atoms, and quantum dots. Each approach has its pros and cons: superconducting circuits might be faster at performing operations, while trapped ion systems might have higher fidelity. Research into each approach is ongoing.
“It’s really hard to predict if there will be an ultimate winner,” Dr. Byrd says. “It could be that we end up with multiple flavors.”
In addition to new types of computational hardware, quantum computers also necessitate new algorithmic designs. Classical computers are built to process ordinary digital data directly, often at enormous scale. But quantum computers are designed to manipulate quantum states—they’re not well-suited to loading, encoding, and reading out large volumes of classical data.
“Currently, the best quantum algorithms are those in which the amount of classical data in and out of the quantum machine is fairly modest,” Dr. Byrd says. “But what you take advantage of is the opportunity to expand within the quantum state, and explore many alternate solutions at once.”
One approach, known as a variational quantum algorithm, uses a hybrid loop: a quantum computer prepares and measures a candidate state, while a classical computer adjusts the setup and tries again. This is especially promising for problems like molecular simulation, where researchers want controllable qubits to mimic the behavior of quantum systems found in nature.
“We’re advancing on the hardware side, and there’s also a lot of movement on simplifying our algorithms and refining what we know about algorithms,” Dr. Byrd says. “There’s a push from both sides, and it’s going to get us closer to that point where we can do things that are infeasible or maybe even impossible to do with classical hardware.”
A quantum algorithm sets up many possible outcomes at once, then uses quantum effects that make useful answers more likely — and less useful answers less likely — when the final state is measured. This makes quantum computers good at solving very particular types of problems that would be inefficient to solve with classical computing. Not all of its applications have been discovered yet.
“Probably the biggest public misunderstanding is that a quantum computer is just a classical computer, but faster,” Dr. Byrd says. “There’s this misperception that everything we have now is either going to go away or can be done better with a quantum computer. But there are a number of things we can do classically much faster than we’d ever think about doing on a quantum computer.”
One quantum application that has attracted mainstream attention is factoring very large numbers, the difficulty of which underpins internet security protocols like RSA. If quantum could crack RSA encryption, then financial records, medical records, even national secrets could be decrypted as a result. The threat is real enough, and quantum is evolving quickly enough, that work has already started on creating and implementing post-RSA encryption protocols. (Future-proofing encryption standards alone isn’t enough: once-secure data harvested now could be decrypted later, after quantum computing passes the RSA threshold.)
But there are other applications where quantum can create positive upheaval. As Feynman postulated, quantum computers are particularly good at modeling physical systems. Modeling how molecules behave could aid in drug discovery, and modeling fusion energy reactions could one day make fusion energy a reality.
They’re also good at optimization, which is probably quantum’s most profitable near-term application. Solving the ‘traveling salesman problem’ — basically, finding the most efficient logistical routes and supply chains — would be extremely valuable to companies like Amazon, Maersk, and DHL, resulting in enormous financial savings while also saving fuel and reducing emissions.
“We still don’t have quantum machines solving optimization problems that can’t be solved in a classical way,” Dr. Byrd says. “We’re not seeing those problems where quantum has the advantage yet. But it’s close. People are excited. There’s been a lot of progress over the last few years.”
So when is all this going to happen? When will quantum make its bold leap into commercial applications? It depends on who you ask, and it depends on the conditions in which you ask. Like a quantum particle, the answer can appear to be in multiple states at once until it’s finally measured. Researchers and academics tend to be more cautious in their timelines: 10 years, 15 years, decades. Large, publicly traded companies are more likely to set aggressive timelines: Google has suggested that quantum’s commercial applications could arrive within five years.
“With quantum computing, it no longer seems to be a question of if but when,” Dr. Schaibley says.
Today’s quantum computers can already do meaningful simulation experiments. Compare it to the early days of flight: the Wright Brothers were aloft for around 12 seconds in their first powered flight, covering about 120 feet over Kitty Hawk; it took another 11 years for the first scheduled commercial passenger airline service. Quantum is well past the Wright Brothers stage, but it’s not in its commercial phase either. To get there will require overcoming challenges in scaling and error correction.
Quantum states are prone to decoherence, with qubits easily influenced by interactions with their environment or with one another. That can lead to errors: what should show up as a one shows up as a zero, or the state itself is correct but the measurement has an error. The more computations a quantum computer does, the longer a quantum algorithm runs, the more errors are likely to be hit.
“We have an error rate that’s much higher than we’d like it to be,” Dr. Byrd says. “Errors creep up. They accumulate. That limits how many things you can do. Part of the work has been trying to improve the system so that the error rate comes down.”
Error correction has long been used in the classical computing world, using parity bits, checksums, and redundancy. It’s made classical systems extremely reliable: classical error rates can be as low as 10-20, while quantum error rates are closer to 10-4. But error correction in quantum is tricky, given how sensitive quantum particles are. It takes multiple qubits to encode and protect a single logical qubit. Those extra physical qubits help detect and correct errors.
“Right now, most people are under the impression that we need something between 1,000 to 10,000 physical qubits to make one logical qubit,” Dr. Schaibley says. “So you very well might need a 10 million physical qubit computer to make an error-corrected one of 2,000 logical qubits. That may sound crazy, in some sense, but the processor in your laptop has billions of transistors.”
Better error correction pushes towards fault tolerance: the point at which quantum computers can keep running accurate computations even as errors continue to occur. Getting there means combining better qubits, error-correction schemes, and scalable hardware architecture. Part of the race is determining which hardware platform can most efficiently support reliable logical qubits.
Dr. Schaibley’s research involves developing quantum light sources for applications such as optical quantum computing, which is an encouraging direction. By generating many single photons and using optical circuits for computation, the number of qubits could potentially scale to millions.
“A number of approaches have roadmaps that can get to 10 million physical qubits,” Dr. Schaibley says. “Most people should not place bets on which approach will get there first, but it’s likely that one will get there, conservatively speaking, in the next 15 years.”
For engineers who enjoy the intellectual challenge of learning something new, quantum is an opportunity to work on the building blocks of potentially paradigm-shifting technology.
The two fields of engineering most closely related to quantum are electrical engineering and materials science (with computer engineering often applicable as well). Undergraduate engineers should consider taking a class on quantum mechanics to familiarize themselves with the physics underpinning quantum computing. They can also pursue a research position with a professor working on quantum devices, quantum algorithms, or quantum materials.
“My advice is to get familiar with all the pieces up and down the stack,” Dr. Byrd says. “You need to know a little bit about a variety of things. But don’t feel like you have to know all of quantum mechanics as well as a physicist would.”
Larger quantum computing companies have grown to thousands of employees, and they’re hiring engineers who don’t necessarily have a high familiarity with the inner workings of quantum mechanics. There’s much work to be done at the picks-and-shovels level: electronics, semiconductors, lasers. Quantum needs computer programmers who can implement control- and systems-level engineering; electrical engineers who understand RF electronics and circuit board layouts; and general-knowledge electrical, mechanical, and materials science engineers who can solve nitty-gritty problems.
“Whatever you are drawn to naturally as an aspiring engineer, there’s probably a way to apply that to quantum computing,” Dr. Byrd says. “What the quantum industry needs are engineers who understand quantum but have skills that can make the system better, more reliable, easier to manufacture, and lower in cost.”
Quantum truly sits at the frontier. Its future has the shimmer of science fiction: seemingly impossibly complex classical problems solved in minutes; an internet of photonic light encoded with quantum information. In the past, great minds described the physics underlying quantum mechanics as “spooky,” but today, that characterization is anachronistic and threatens to alienate future engineers by casting quantum as unintuitive, abstract, and unknowable. Just as quantum computing has evolved from conjecture to theory to practice, so has our understanding of how it works.
“Maybe I’ve been in the field too long, but I don’t think of quantum mechanics as spooky,” Dr. Schaibley says. “It follows the rules of linear algebra. The calculations are actually quite straightforward. They’re algorithmic. Quantum computing is real technology. It works.”
Matt Zbrog is a writer and researcher from Southern California. Since 2018, he’s written extensively about a wide range of engineering disciplines, with a particular focus on the potential impacts of major technological breakthroughs. His work largely centers around conversations with engineering faculty at top universities, highlighting their research in areas such as nuclear power, artificial intelligence, soft robotics, and semiconductor design. He’s also worked with leaders and subject matter experts at the Institute for Electrical and Electronics Engineers (IEEE), the California Department of Water Resources (DWR), the National Society of Professional Engineers (NSPE), and the Society of Women Engineers (SWE).
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