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Some Quantum Software Works Today. Down the Road, It Might Not.
Quantum computers, technology’s next-big-thing for the last decade, may lack the surprise factor of generative artificial intelligence, whose late 2022 appearance sent IT teams scrambling. But similar concerns over being unprepared are helping drive efforts to build quantum apps for the day, years in the future, when commercial-grade quantum machines are available.
For quantum software makers and their customers, it’s a hard brief: Writing applications that companies can test on today’s nascent quantum computers, but that will continue to work as hardware technology matures.
“It’s incredibly hard because nobody actually knows what type of structure quantum computers will have in the next five years and probably not even the next one or two years—they have evolved so dramatically,” said Antonio De Negri, founder and CEO of Cirdan Capital.
A computer harnessing the properties of quantum physics could solve some problems many millions of times faster than a conventional computer. That potential has attracted a host of tech giants, including IBM and Google, racing to commercialize the technology.
Global venture investment in quantum computing was about $1.6 billion in 2022, up from roughly $160 million in 2018, according to data from research firm PitchBook.
While hardware develops, tech providers are tackling another area: writing algorithms designed to take advantage of quantum computers’ advanced capabilities. These algorithms have a fundamentally different approach to problem-solving than classical algorithms, said Markus Pflitsch, founder, chairman and CEO of quantum tech provider Terra Quantum. They are suited for certain uses, including optimization, which has applications across industries including in finance, manufacturing and supply chain, he said.
Demand for the algorithms—and the software code that expresses them to computers—has risen in the past couple of years. Enterprises say it’s valuable to identify key use cases and trial them via simulators or early quantum computers, which are often available to use over the cloud. After witnessing the shockingly rapid development of AI over the last year, companies want to be prepared for when hardware is more mature, and technology providers are looking to stake their claim on killer apps, said Richard Moulds, general manager of Amazon Braket, AWS’s managed quantum computing service.
The problem with staking claims on software apps is that there are many unknowns about what quantum computers will ultimately look like, and it’s unclear how well the software developed today will run on future machines, said Heather West, research manager and quantum-computing research lead at IDC.
“As the underlying structure of the quantum computers can change and evolve over time, and nobody knows how it will, it might be that the software written down doesn’t ‘speak’ anymore in a language that the quantum computers will understand,” De Negri said. Cirdan Capital is testing pricing optimization use cases with Terra Quantum’s simulation software.
The way of approaching certain problems may vary depending on the mechanics of the future computer, said Luke Ibbetson, head of group R&D at
, which is working on use cases like optimizing the placement of cell towers.
“You’ve got multiple layers of consideration when you look at the ecosystem as a whole,” he said.
One area to consider is the computer’s size. Classical computers use binary digits, or bits, which can either be zeros or ones. Quantum computers use quantum bits, or qubits, which represent and store information in a quantum state that is a complex mix of zero and one. Ultimately, quantum systems will contain millions of qubits, though today they have far fewer.
last year unveiled a 433 qubit chip, a step up from its 127 qubit chip released the year before.
Figuring out how well different quantum algorithms scale with increasing qubit count and program depth or runtime is currently a focus for researchers, Moulds said.
Updating algorithms to run on machines with larger numbers of qubits is, for the most part, a manual process that entails programming at the circuit level that entails the knowledge of a quantum specialist, said West.
Some companies are trying to address the software challenge with software itself. For example, Terra Quantum and quantum company Classiq both said they are working on software to automate that updating process.
“Algorithm developers are on a journey that evolves from current to future hardware,” said Moulds. “We don’t know where it will end up or the turns, and speed bumps, ahead.”
Read the full article in The Wall Street Journal
Quantum computers, technology’s next-big-thing for the last decade, may lack the surprise factor of generative artificial intelligence, whose late 2022 appearance sent IT teams scrambling. But similar concerns over being unprepared are helping drive efforts to build quantum apps for the day, years in the future, when commercial-grade quantum machines are available.
For quantum software makers and their customers, it’s a hard brief: Writing applications that companies can test on today’s nascent quantum computers, but that will continue to work as hardware technology matures.
“It’s incredibly hard because nobody actually knows what type of structure quantum computers will have in the next five years and probably not even the next one or two years—they have evolved so dramatically,” said Antonio De Negri, founder and CEO of Cirdan Capital.
A computer harnessing the properties of quantum physics could solve some problems many millions of times faster than a conventional computer. That potential has attracted a host of tech giants, including IBM and Google, racing to commercialize the technology.
Global venture investment in quantum computing was about $1.6 billion in 2022, up from roughly $160 million in 2018, according to data from research firm PitchBook.
While hardware develops, tech providers are tackling another area: writing algorithms designed to take advantage of quantum computers’ advanced capabilities. These algorithms have a fundamentally different approach to problem-solving than classical algorithms, said Markus Pflitsch, founder, chairman and CEO of quantum tech provider Terra Quantum. They are suited for certain uses, including optimization, which has applications across industries including in finance, manufacturing and supply chain, he said.
Demand for the algorithms—and the software code that expresses them to computers—has risen in the past couple of years. Enterprises say it’s valuable to identify key use cases and trial them via simulators or early quantum computers, which are often available to use over the cloud. After witnessing the shockingly rapid development of AI over the last year, companies want to be prepared for when hardware is more mature, and technology providers are looking to stake their claim on killer apps, said Richard Moulds, general manager of Amazon Braket, AWS’s managed quantum computing service.
The problem with staking claims on software apps is that there are many unknowns about what quantum computers will ultimately look like, and it’s unclear how well the software developed today will run on future machines, said Heather West, research manager and quantum-computing research lead at IDC.
“As the underlying structure of the quantum computers can change and evolve over time, and nobody knows how it will, it might be that the software written down doesn’t ‘speak’ anymore in a language that the quantum computers will understand,” De Negri said. Cirdan Capital is testing pricing optimization use cases with Terra Quantum’s simulation software.
The way of approaching certain problems may vary depending on the mechanics of the future computer, said Luke Ibbetson, head of group R&D at
, which is working on use cases like optimizing the placement of cell towers.
“You’ve got multiple layers of consideration when you look at the ecosystem as a whole,” he said.
One area to consider is the computer’s size. Classical computers use binary digits, or bits, which can either be zeros or ones. Quantum computers use quantum bits, or qubits, which represent and store information in a quantum state that is a complex mix of zero and one. Ultimately, quantum systems will contain millions of qubits, though today they have far fewer.
last year unveiled a 433 qubit chip, a step up from its 127 qubit chip released the year before.
Figuring out how well different quantum algorithms scale with increasing qubit count and program depth or runtime is currently a focus for researchers, Moulds said.
Updating algorithms to run on machines with larger numbers of qubits is, for the most part, a manual process that entails programming at the circuit level that entails the knowledge of a quantum specialist, said West.
Some companies are trying to address the software challenge with software itself. For example, Terra Quantum and quantum company Classiq both said they are working on software to automate that updating process.
“Algorithm developers are on a journey that evolves from current to future hardware,” said Moulds. “We don’t know where it will end up or the turns, and speed bumps, ahead.”
Read the full article in The Wall Street Journal
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