Commercializing quantum computers step by step
Quantum computing at AQT: How much does it take to schnapp? A case study in Monz’s humble humble beginnings
Monz acknowledges that his colleagues and he do not sell everything they produce. Indeed, they keep at least some of their secret ingredients off the shelves. “There is still quite a bit of ‘special sauce’ left,” he says.
Outside the company, such assessments are also made. AQT might have a problem with selling its components, which could give competitors a head-start on the inner workings of the firm.
The AQT team is determined to keep expectations in check despite this encouraging progress. The company has successfully demonstrated several universal quantum computers, installed in industry-standard 19-inch racks, used for computer servers. Monz acknowledges that manufacturers of quantum computers often boast about the quantity of qubits in their processors — the US technology giant IBM, for example, has announced plans for a computer with around 1,000 superconducting qubits this year. But Monz does not want to join in the numbers game. What really matters, he contends, is the quality of the qubits. He says if you want to make schnapps, it does not matter how many rotten apples you have. You need to start with a few apples.
Part of the IBM team’s gains are the result of measures that reduce errors in the quantum computers. Errors can be mitigated by using noise-cancelling headphones similar to those used in noise-cancelling headphones, and by identifying parts of a quantum circuit that can be separated out and recreated on a classical computer without losing quantum information. The last technique, which effectively doubles the quantum resources, was invented seven years ago.
The dual approach makes sense for Lieven to be working on rival qubits at the Dutch university. Selling the components allows the researchers to work on long-term challenges of a large-scale quantum- computing system while generating some income and putting them in a commercial operating mode.
Still, no type of qubit can remain immune to noise as quantum computers are scaled up, says Thomas Monz, a quantum physicist at the University of Innsbruck, and co-founder and chief executive of AQT. Increasing the number of qubits, along with the number of processor operations (known as gates), makes it more likely that mistakes will creep in during calculations. The solution to this is error correction, which involves using multiple physical qubits to represent a single logical bit of data. That way, the value of those qubits can be compared, and the system can weed out any that succumb to noise.
But with great power comes great fragility. The states can be destroyed by small amounts of energy or heat. So although qubits must be manipulated to execute quantum algorithms, they must also remain as isolated as possible from the outside world.
As a source of optimism, such calculations offer. Although 20 million qubits looks out of reach, it’s a lot less than the one billion qubits of previous estimates4. And researcher Michael Beverland at Microsoft Quantum, who was first author of the 2022 preprint2, thinks that some of the obstacles facing quantum chemistry calculations can be overcome through hardware breakthroughs.
The hardware itself is very difficult to build. The data is stored in quantum bits, or qubits, that can be made using a variety of technologies. Some technologies require cooling to near absolute zero, others operate at room temperature. A machine the size of a football pitch would be the one Hensinger has in mind, however other machines could end up in cars. There are no agreed on ways to measure the performance of quantum computers.
Whatever the design, the clever stuff happens when qubits are carefully coaxed into ‘superposition’ states of indefinite character — essentially a mix of digital ones and zeroes, rather than definitely being one or the other. Running algorithms on a quantum computer involves directing the evolution of these superposition states. The quantum rules of this evolution allow the qubits to interact to perform computations that are, in practical terms, impossible using classical computers.
Nicole Holzmann and her colleagues at Riverlane showed that quantum mechanics can be made even more efficient by calculating the ground-state energies of around 50 orbital electrons. The estimates of the amount of time it takes for such programs to be written had come in over 1,000 years. They found that by changing how the tasks are distributed around the logic gates, they cut the theoretical run time to a few days. It is a gain of around five orders of magnitude. “Different options give you different results,” Holzmann says, “and we haven’t thought about many of these options yet.”
At IBM,Garcia is starting to exploit the gains. It is easy to pick out the potential benefit of the quantum advantage, even if the calculations involve huge amounts of molecule.
Maniscalco is not the only one who believes that the first commercial use of quantum computing will be in speeding up or gaining better control overmolecular reactions. Ronald de Wolf, senior researcher at the CWI, a research institute for mathematics and computer science in Amsterdam, says that chemistry calculations is going to be useful in the next five years. That’s because of the relatively low resource requirements, adds Shintaro Sato, head of the Quantum Laboratory at Fujitsu Research in Tokyo. He said that it could be done using quantum computers with a relatively small number of qubits.
In February, Garcia’s team published6 quantum simulations of the sulfonium ion (H3S+). That molecule is related to triphenyl sulfonium (C18H15S), a photo-acid generator used in lithography that reacts to light of certain wavelengths. It could make the manufacturing technique more efficient if we understood its properties. The team started the work and looked like they would not be able to do it, but advances in quantum computing have made it possible to do simulations with less than 30 qubits of memory.
In exploratory work with South Korean car manufacturer Hyundai, Jungsang Kim at Duke University in Durham, North Carolina, and researchers at the firm IonQ in College Park, Maryland, developed quantum machine-learning algorithms that can tell the difference between ten road signs in laboratory tests (see go.nature.com/42tt7nr). The model they used used just 60 parameters to get the same accuracy as a classical neural network using 59,000 parameters. “We also need far fewer training iterations,” Kim says. “A model with 59,000 parameters requires at least 100,000 training data sets to train it. Your training becomes more efficient with quantum because of the small number of parameters.
That won’t happen soon for most applications. Silicon Quantum Computing, a Sydney-based start-up, has been working closely with finance and communications firms and anticipates many years to go before payday, says director Michelle Simmons, who is also a physicist at the University of New South Wales in Sydney.
If you think the hype, computers will be able to exploit the strange behaviors of the atomic realm, speeding up drug discovery, crack encryption, speed up decision-making in financial transactions, improve machine learning, develop revolutionary materials and even address climate change. The surprise is that those claims are now starting to seem a lot more plausible — and perhaps even too conservative.
The senior research manager for quantum applications and software at IBM says that she is surprised by how much work has been done.
