When it comes to computers, bits of technology are not created equal.
This can be seen with a quick stroll through your local electronics store. Photographers might take an SD card, while gamers might take discs for their home console. Meanwhile, down the road, the back-to-school consumer will be buying the latest MacBook with a “solid-state drive.” In the end every picture, video game, or final report boils down to a list of 0s and 1s. But how those bits are stored—whether in a magnetic groove, an electronic circuit, or some other format entirely—depends entirely on the specifics of the application.
Finding the right qubit for the right task
Today, we are in the early stages of quantum computing so it is hard to believe that one day we may need to make these kinds of choices: which type of qubit (quantum bit) is best for which task? But in the next decade – as quantum computers come to market – we expect to see the same thing happen. Key to all of this will be the ability to fairly measure the effectiveness of the claims being made.
Just as there are many different ways to store a traditional bit, there are many types of qubits. Today’s frontrunner technologies include qubits made of atoms, ions, photons, superconductors, and ions. Each of these technologies scores differently according to key metrics such as speed, scalability, reliability, interoperability, ruggedization, and cost. But critically, no qubit type wins in every category. In fact, many of these metrics are contradictory—for example, the fastest qubits are also often the least scalable.
Borrowing from a rich tradition of measurement
If there is no single dominant type of qubit, we must instead balance the power of each type of qubit to support different use cases such as machine learning, quantum dynamics, programming, sensors, or material science. Such benchmarks, well designed to suit concrete applications, will drive industry preferences and demand for specific quantum hardware models. As with traditional computing, we expect that different types of qubits will emerge to drive different software applications.
Designing an efficient and useful set of benchmarks is a challenging task, but fortunately we can borrow from a few principles in benchmarking. the old one computers for the past thirty years.
As business leaders begin to map their quantum strategies, performance measurement should be an integral part of any long-term plan. Hardware vendors will be fighting for market share, and there will be no shortage of options. A good understanding of the functionality each one provides will give business leaders the clarity to make the best decisions possible. In addition, high-quality benchmarks also empower leaders to cut through the hype and let data drive purchasing decisions.
A yardstick toward the holy grail: quantum error correction
While the early use of quantum computing is expected to unlock the acceleration of niche use cases, wider market use is embedded in the key enabler of the technology: quantum error correction (QEC). This technology, which allows qubits to behave properly, is the main goal of many leading quantum hardware vendors. The first qubit models featuring QEC will be well poised to capture the latent industry demand for applications that require more reliable quantum computers than we have today.
In this spirit, quantum benchmarks should include quantum error correction itself as an area of progress. In our work, we found that QEC “squeezes” very different aspects of quantum computing than other applications. For example, an important part of QEC is the software’s ability to adapt on the fly (or in technical terms, “feedforward measurement”). This segment is unique to QEC and is currently an area where hardware vendors need to make continued progress.
In this sense, setting appropriate benchmarks—informed by real-world industry demand—could accelerate progress in future quantum computers. Just as benchmarks have documented and influenced the design of traditional computers, benchmarks will play an important role in quantum computers by simulating qubits in applications. Let’s work to make it as easy as walking into the grocery store.
About the Author
Pranav Gokhale is VP of Quantum Software at ColdQuanta. ColdQuanta is a global quantum technology company solving the world’s most challenging problems. ColdQuanta combines quantum mechanics to build and integrate a range of quantum computers, sensors, and networks. From basic physics to leading commercial products, ColdQuanta enables “quantum everywhere” through our ecosystem of devices and platforms.
Featured image: ©Vchalup