ParityQC: Quantum Computing in the Instantaneous Landau-California Era (You Can’t Get Ahold of Two Quantum Circuits)

Michael Biercuk, a quantum physicist at the University of Sydney in Australia, who is chief executive and founder of Sydney-based start-up firm Q-CTRL, says such operational tweaks are ripe for exploration. The work of Biercuk is to dig deeper into the interface between quantum circuits and classical computers so as to understand the details of other components that make up a quantum computer. There is a “lot of space left on the table”, he says; early reports of errors and limitations have been naive and simplistic. “We are seeing that we can unlock extra performance in the hardware, and make it do things that people didn’t expect.”

To turn this knowledge into revenue, ParityQC licenses its intellectual property to hardware developers so that they can build chips incorporating the architecture. According to Hauser, the company has sold licences to Japanese electronics giant NEC to produce a superconducting quantum chip, and has entered several consortia that were set up in response to the German government investing €2 billion (US$2.2 billion) to fund the development of quantum technologies.

Notably, the company jointly received an €83-million contract awarded by the German Aerospace Center in Cologne to build ion-trap computers. Along with other manufacturers, it won a contract to build a 10-qubit computer and then develop devices that respond to different types of stimuli. (This type of computer is also being developed by another The Spinoff Prize 2023 finalist, Alpine Quantum Technologies, although Alpine is not part of ParityQC’s collaboration.)

ParityQC’s architecture helps quantum computers to deal with both limitations. It does so by changing how the data are encoded in qubits. Rather than representing the values of individual logical qubits — as specified by the program being executed — physical qubits instead record the relationship between pairs of logical qubits in terms of parity. If the qubits in a pair are the same, then the parity is 1; if they are different, then the parity is 0.

A second major issue for quantum computing is the spatial properties of qubits. The physical processes that link qubits to one another are usually very short distances, and can be seen near the atomic nucleus or two superconducting circuits. This means that each qubit typically interacts only with its nearest neighbours, rather than qubits farther away.

qubits are fragile. Their states can be disrupted by the slightest amount of heat or other interference. The kind of physical qubit used affects their longevity. They might remain intact for a few seconds if they are perfectly isolated or might disappear after milliseconds if they interact with other qubits during a calculation.

A spin-off company: a machine for quantum chemistry and physics simulations in a small space-time environment and with near absolute zero temperature

Three years on from its set-up, the company now employs about 30 people. It has secured contracts worth tens of millions of euro from high-tech manufacturers and governments. Thanks to grants from the European Union and the governments of Austria and Germany, Lechner has been able to avoid having to drum up support from venture capitalists. The company made revenue from the start.

He quickly filed a patent and, just six months later, received an offer for the intellectual property from a large technology company. Lechner wouldn’t give the company or the size of the offer. This told him that the architecture had commercial potential, and that it might be better to try to reap the rewards directly. He and his colleagues decided to start a spin-off company after rejecting the offer. ParityQC launched in 2020, and has been named a finalist in The Spinoff Prize 2023.

Lechner, a physicist at the University of Innsbruck in Austria, discussed the proposal with a colleague, but they managed to persuade themselves that it was a non-starter. Over the next two years he kept turning the idea over in his mind, and says it became an obsession. He had a breakthrough at 3 a.m. in his hotel room that could mean he should use his parity approach again.

Some believe that the first commercial applications of quantum computing will be in speeding up or gaining better control over their reactions. Ronald de Wolf is the senior researcher at the CWI in Amsterdam and says chemistry calculations will be useful over 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. This would be possible with computers with a small number of qubits.

The problem is compounded by the difficulty of building the hardware itself. There are a number of technologies that can be used to make qubits, such as optical traps, superconducting rings, and the use of photons of light. It’s possible to operate at room temperature or near absolute zero for some technologies. Hensinger wants a machine that would be 888-282-0465 888-282-0465 888-282-0465 888-282-0465 888-282-0465 888-282-0465 888-282-0465 888-282-0465, but others could be installed in cars. Researchers cannot even agree on how the performance of quantum computers should be measured.

The clever stuff happens if qubits are forced to be a mixture of digital ones and zeroes rather than being one or the other. Quantum computers take part in directing the evolution of superposition states. The quantum rules of this evolution allow the qubits to perform computations that are not possible using classical computers.

Nicole Holzmann and her colleagues at Riverlane have shown that a quantum Algorithms can be made more efficient because it calculates the ground state energies of 50 orbital electrons. More than a thousand years had passed since the previous estimates of the program’s running time. But Holzmann and her colleagues found that tweaks to the routines — altering how the algorithmic tasks are distributed around the various quantum logic gates, for example — cut the theoretical runtime to just a few days. That’s an increase of about 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 these gains. The potential quantum advantage doesn’t have to be limited to calculations involving a lot of molecules.

One example of a small-scale but classically intractable computation that might be possible on a quantum machine is finding the energies of ground and excited states of small photoactive molecules, which could improve lithography techniques for semiconductor manufacturing and revolutionize drug design. Another is simulating the singlet and triplet states of a single oxygen molecule, which is of interest to battery researchers.

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. Understanding its molecular and photochemical properties could make the manufacturing technique more efficient, for instance. When the team began the work, the computations looked impossible, but advances in quantum computing over the past three years have allowed the researchers to perform the simulations using relatively modest resources: the H3S+ computation ran on IBM’s Falcon processor, which has just 27 qubits.

Part of the IBM team’s gains are the result of measures that reduce errors in the quantum computers. They include error mitigation in which noise is cancelled out by using similar software as that used in headphones, and entangle forging, where parts of a quantum circuit can be separated out and visualized on a classical computer. The technique that doubles the quantum resources was developed last year.

Machine-learning algorithms perform tasks such as image recognition by finding hidden structures and patterns in data, then creating mathematical models that allow the algorithm to recognize the same patterns in other data sets. Success involves a lot of parameters and training data. The range of different states open to quantum particles means that the routines could require less data and less training.

Simmons added: Silicon Quantum Computing has patient investors. So, too, does Riverlane, says Brierley. “People do understand that this is a long-term play.”

At IBM, they are surprised about how much they have done because they are not trying to take away from how much work there is.

If you believe the hype, computers that exploit the strange behaviours of the atomic realm could accelerate drug discovery, crack encryption, speed up decision-making in financial transactions, improve machine learning, develop revolutionary materials and even address climate change. It is surprising that those claims seem a lot more plausible now.