Big Blue has made Qiskit Metal generally available, to let “anyone” try their hand at quantum hardware design.
Building the hardware that underpins quantum computers might not sound like everybody’s cup of tea, but IBM is determined to make the idea sound less challenging.
The company has announced the general availability of Qiskit Metal, an open-source platform that automates parts of the design process for quantum chips, and which IBM promised will now let “anyone” design quantum hardware.
Big Blue detailed the progress made with Metal since the tool was first announced late last year as part of the company’s larger Qiskit portfolio, which provides open-source tools for creating programs that can run on IBM’s cloud-based quantum devices.
While most of Qiskit’s resources focus on building applications that can be executed on quantum machines, Metal targets a brand-new audience, providing software to help design the components that make up the hardware itself.
The idea is to let users play around with pre-built components on the platform to produce state-of-the-art chips for superconducting quantum devices in a matter of minutes – a process that traditionally takes months of manual design, analysis and revisions for scientists in the lab.
While automation processes are already firmly in place to accelerate the design of classical integrated circuits, the same cannot be said for quantum computers. As Zlatko Minev, IBM research staff and lead of Qiskit Metal, explains, quantum chips still require an intricate, time-consuming fabrication process.
“How it normally happens is, you take a grad student like my former self, you put them in the lab for six months or so to design a chip, and out comes a chip,” he tells ZDNet. “It would take a lot of work, it was very laborious. So one of the things I wanted was to make this easier for myself when I got to IBM, by automating the process.”
Qiskit Metal is, at first glance, fairly straightforward. The process starts with setting targets for the chip, such as a particular qubit frequency or qubit-qubit entanglement; users can then design an initial layout in a few minutes, using a library of pre-defined, customizable quantum components.
Metal then carries out both a classical and a quantum analysis to predict the performance of the device. “This is all the stuff you would normally do manually, which I had to do all the time,” says Minev. The platform can anticipate parameters such as qubit frequencies, convergence or entanglement, letting users go back and forth to tweak their model until the optimal design is found.
To check the reliability of Metal, IBM’s Qiskit’s team partnered with Chalmers University of Technology, which already has strong experience in building quantum test chips. The researchers were able to design a competitive eight-qubit chip using IBM’s platform, but in a record 30 minutes. It took another hour to run the design on a simulator, where the device performed as expected based on Chalmers’ previous experiments.
Another one of the early applications of Metal saw the tool deployed during a Qiskit hackathon in South Korea earlier this month. Participants gathered in teams of five or six, with the objective of designing a quantum chip from scratch in a couple of days. Using Metal, all of the teams successfully built two-qubit chips using superconducting qubits in less than 24 hours.
A partnership with the University of Tokyo also produced a five-qubit quantum processor called Tsuru in just a few hours and over WebEx.
As Minev explains, Metal is not designed to build large-scale quantum chips to compete against the experts currently developing fully-fledged quantum computers. Rather, the tool is meant to let users try their hand at designing quantum hardware, and optimize their models in ways that might benefit the entire ecosystem.
“If you’re trying to build quantum devices at a large scale, there is a lot more that goes into this, it’s not an easy job,” says Minev. “Metal is rather aimed at small-scale, rapid design and prototyping. The idea is to create new devices and optimize designs, to push and improve the scientific techniques.”
Central to the project is the ease of access. IBM is hopeful that Metal will eventually be accessible to users with little to no programming knowledge, and encourage experts from all fields to wet their feet with quantum.
This is important because improving quantum hardware will require input from experts from a host of different backgrounds – all of whom aren’t necessarily trained in the physics of quantum computing. “A lot of challenges in the field are yet to be resolved,” says Minev, “and it doesn’t just take physicists, but also engineers and software developers, among others. We want to give a point of entry for all these folks to be able to come together through an easy interface.”
Like quantum computing, Metal is in its early stages, and Minev hopes that as the field grows, so will the platform incorporate increasingly sophisticated quantum hardware and modeling advances. For now, though, IBM has warned that users are likely to face a fair share of bugs to fix, and has encouraged curious users to come forward and trial the tool, sharing feedback and criticism as they go with the rest of the open-source community
The global quantum computing race has begun. What will it take to win it?
Plenty of nations want to be the quantum computing leader, but with billions in investment flying around, coming out on top won’t be easy.
National quantum programs and decade-long quantum strategies are increasingly being announced by governments around the world. And as countries unlock billions-worth of budgets, it is becoming clear that a furious competition is gradually unrolling. Nations want to make sure that they are the place-to-be when quantum technologies start showing some real-world value – and the UK, for one, is keen to prove that it is a quantum hotspot in the making.
“We have a very successful program that is widely admired and emulated around the world,” said Peter Knight, who sits on the strategic advisory for the UK’s national quantum technology program (NQTP), as he provided a virtual update on the NQTP’s performance so far.
Speaking at an online conference last month, Knight seemed confident. The UK, said the expert, in line with the objectives laid out in the program, is on track to become “the go-to place” for new quantum companies to start, and for established businesses to base all manners of innovative quantum activities.
The UK is just over halfway through the NQTP, which saw its second five-year phase kick off at the end of 2019, and at the same time hit an impressive milestone of £1 billion ($1.37 billion) combined investment. This, the government claims, is letting the UK keep pace with competitors who are also taking interest in quantum – namely, the US and China.
There is no doubt that the country has made strides in the field of quantum since the start of the NQTP. New ground-breaking research papers are popping up on a regular basis, and so are news reports of rounds of funding from promising quantum startups.
But with still just under half of the national quantum program to carry out, and despite the huge sums already invested, the UK is now facing a bigger challenge yet: after having chased a top spot in the quantum race, retaining the country’s status in the face of ferocious competition is going to require some serious stepping up.
Clearly playing in favor of the UK is the country’s early involvement in the field. The NQTP was announced as early as 2013, and started operating in 2014, with an initial £270 million ($370 million) budget. The vision laid out in the program includes creating a “quantum-enabled economy”, in which the technology would significantly contribute to the UK’s economy and attract both strong investment and global talent.
“The national program was one of the first to kick off,” Andrew Fearnside, senior associate specializing in quantum technologies at intellectual property firm Mewburn Ellis, tells ZDNet. “There are increasingly more national programs emerging in other countries, but they are a good few years behind us. The fact that there has been this sustained and productive long-term government initiative is definitely attractive.”
The EU’s Quantum Technologies Flagship, in effect, only launched in 2018; some countries within the bloc, like France, started their own quantum roadmaps on top of the European initiative even later. Similarly, the National Quantum Initiative Act was signed into law by the Trump administration – but that was also in 2018, years into the UK’s national quantum technology program.
Since it launched in 2014, there has been abundant evidence of the academic successes of the initial phase of the NQTP. In Birmingham, the Quantum Sensing Hub is developing new types of quantum-based magnetic sensors that could help diagnose brain and heart conditions, while the Quantum Metrology Institute leads the development of quantum atomic clocks. There are up to 160 research groups and universities registered across the UK with programs that are linked to quantum technologies, working on projects ranging from the design of quantum algorithms to the creation of new standards and verification methods.
A much harder challenge, however, is to transform this strong scientific foundation into business value and as soon as the UK government announced the second phase of the NQTP at the end of 2019, a clear message emerged: quantum technology needed to come out of the lab, thanks to increased private sector investment that would accelerate commercialization.
Some key initiatives followed. A national quantum computing center was established for academics to work alongside commercial partners such as financial services company Standard Chartered, “possibly with an eye on financial optimization problems,” notes Fearnside, given the business’ established interest in leveraging quantum technologies. A £10 million ($13 million) “Discovery” program also launched a few months ago, bringing together five quantum computing companies, three universities and the UK’s national physical laboratory – all for the purpose of making quantum work for businesses.
The government’s efforts have been, to an extent, rewarded. The quantum startup ecosystem is thriving in the UK, with companies like Riverlane or Cambridge Quantum Computing completing strong rounds of private financing. In total, up to 204 quantum-related businesses have been listed so far in the country.
But despite these encouraging results, the UK is still faced with a big problem. Bringing university-born innovation to the real world has always been a national challenge, and quantum is no exception. A 2018 report from the Science and Technology committee, in fact, gave an early warning of the stumbling blocks that the NQTP might run into, and stressed the need for improved awareness across industry of the potential of quantum technologies.
The committee urged the government to start conveying the near-term benefits that quantum could provide to businesses – something that according to the report, CEOs and company chairs in North America worryingly seem to realize a whole lot better.
It’s been three years since the report was published, and things haven’t changed much. Speaking at the same forum as the NQTP’s Peter Knight, Ian West, a partner at consultancy firm KPMG, said that there remained a huge barrier to the widespread take-up of quantum technologies in the UK. “Some of our clients feel they don’t understand the technology, or feel it’s one for the academics only,” he argued.
“We need that demand from businesses who will be the ultimate users of quantum technologies, to encourage more investment,” West added. “We need to do much more to explain the near-term and medium-term use cases for business applications of quantum technologies.”
Without sufficient understanding of the technology, funding problems inevitably come. The difficulty of securing private money for quantum stands in stark contrast to the situation across the Atlantic, where investors have historically done a better job of spotting and growing successful technology companies. Add the deep pockets of tech giants such as Google, IBM or Microsoft, which are all pouring money into quantum research, and it is easy to see why North America might have better prospects when it comes to winning the quantum game.
In the worst of cases, this has led to US technology hubs hoovering up some of the best quantum brains in the UK. In 2019, for example, PsiQ, a promising startup that was founded at the University of Bristol with the objective of producing a commercial quantum computer, re-located to Silicon Valley. The move was reported to be partly motivated by a lack of access to capital in Europe. It was a smart decision: according to the company’s latest update, PsiQ has now raised $215 million (£156 million) in VC funding.
Pointing to the example of PsiQ, Simon King, partner and deep tech investor at VC firm Octopus Ventures, explains that to compete against the US, the UK needs to up its game when it comes to assessing the startups that show promise, and making sure that they are injected with adequate cash.
“The US remains the biggest competitor, with a big concentration of universities and academics and the pedigree and culture of commercializing university research,” King tells ZDNet. “Things are definitely moving in the right direction, but the UK and Europe still lag behind the US, where there is a deeper pool of capital and there are more investors willing to invest in game-changing, but long-term technology like quantum.”
US-based private investors are only likely to increase funding for the quantum ecosystem in the coming years, and significant amounts of public money will be backing the technology too. The National Quantum Initiative Act that was signed in 2018 came with $1.2 billion (£870 million) to be invested in quantum information science over the next five years; as more quantum companies flourish, the budget can be expected to expand even further.
Competition will be coming from other parts of the world as well. In addition to the European Commission’s €1 billion ($1.20 billion) quantum flagship, EU countries are also spending liberally on the technology. Germany, in particular, has launched a €2 billion ($2.4 billion) funding program for the promotion of quantum technologies in the country, surpassing by far many of its competitors; but France, the Netherlands, and Switzerland are all increasingly trying to establish themselves as hubs for quantum startups and researchers.
Little data is available to measure the scope of the commercialization of quantum technology in China, but the country has made no secret of its desire to secure a spot in the quantum race, too. The Chinese government has ramped up its spending on research and development, and the impact of that investment has already shown in the country achieving some significant scientific breakthroughs in the field.
In the midst of this ever-more competitive landscape, whether the UK can effectively distinguish itself as the “go-to place” for quantum technologies remains to be seen. One thing is for certain: the country has laid some very strong groundwork to compete. “The UK has some genuinely world-class universities with some really brilliant academics, so while the objective is certainly ambitious, it’s not out of the question,” argues King.
But even top-notch researchers and some of the most exciting quantum startups might not cut it. The UK has positioned itself well from an early stage in the quantum race, but becoming a frontrunner was only one part of the job. Preserving the country’s position for the coming years might prove to be the hardest challenge yet.
Quantum computing: Quantum annealing versus gate-based quantum computers
Researchers from pharmaceutical company GSK investigated whether existing quantum computers could already assist with drug discovery.
Quantum technologies have long been pitched as a way to fundamentally change the way drugs are discovered; to start putting the theory to the test, researchers from pharmaceutical company GlaxoSmithKline (GSK) have been toying with top-notch quantum devices, comparing the methods put forward by IBM and D-Wave to get a better picture of what to expect from those leading the quantum race.
The conclusion? The method used by D-Wave, called quantum annealing, can already compete against classical computers and start addressing realistic problems; on the other hand, gate-based quantum computers, such as the one that IBM is building, remain short of enough qubits to run problems that are relevant to the real world.
All is not lost for gate-based methods – quite the contrary, in fact. GSK’s researchers foresee that the expected increase in qubit count in computers like these will allow quantum devices to show a significant performance advantage over classical hardware, for pharmaceutically-relevant life science problems, but also many other types of application.
The results of the scientists experiments are still in pre-print, and are yet to be certified by peer review; in addition, the trials only focus on a specific problem – the use of quantum computing to assist drug discovery. Nevertheless, the research offers a valuable overview of the capabilities of quantum devices as they stand, and of the limitations of different approaches to quantum computing.
The problem addressed by the scientists is well-established in classical computing. Called codon optimization, it consists of finding sequences of genetic code, called codons, that will ultimately lead to the expression of a particular gene. Up to six codons can be required to represent an amino acid, which in turn form the proteins that determine the gene.
In classical computing, codon optimization is addressed with genetic algorithms (GAs) that sample and iterate many different combinations of codons before settling on the most “optimal” solution. Due to the limited capabilities of the hardware, however, GAs cannot sample a large number of solutions in little time, which is why drug discovery is a lengthy process.
“Thorough sampling of the solutions space is therefore often intractable with biologically relevant use-cases,” wrote GSK’s researchers.
Quantum computing, however, and the ability of qubits to carry out various calculations in parallel, shows a lot of promise for this type of optimization problem, and would allow for a larger solutions space to be explored much faster.
This is why the researchers set out to investigate the potential impact of quantum computing for codon optimization. Using a quantum algorithm, called the Binary Quadratic Model (BQM) that can run on different quantum platforms, the team decided to test two markedly different models: D-Wave’s quantum annealing method, and IBM’s gate-based quantum computer.
D-Wave’s technology, found the researchers, holds a lot of potential. The Canadian company’s 5,000-qubit Advantage system was used to run the BQM; the system was capable of mapping 30 amino acids, and when compared to the classical algorithms, it was found to achieve similar results. “(The computer) is found to be competitive in identifying optimal solutions, and future generations (…) may be able to outperform classical GAs,” concluded the scientists.
Current generations of quantum hardware are not mature enough to surpass classical computing for problems such as codon optimization. In other words, D-Wave’s processor did not run the calculation better than a classical algorithm; but it proved that a quantum device could perform competitively, even on a life-size problem. As the technology increases in scale, the researchers expect it to eventually outperform classical techniques.
In separate experiments, a similar conclusion was reached by researchers at materials design company OTI Lumionics, which is banking on quantum technologies to develop electronics with new properties. Using an optimization algorithm that is similar to the one run by GSK’s scientists, OTI Lumionics designed a new electronic material that will let phone and laptop manufacturers build transparent, bezel-free OLED displays.
Just like GSK’s team, OTI Lumionics’ researchers looked at the performance of different quantum approaches when running the algorithm. They eventually settled on D-Wave, finding that, contrary to other cloud-based quantum services, the company’s processor could already compete against classical methods, and reach a degree of industrial relevance.
D-Wave’s quantum annealing processor, however, is only reflective of one particular branch of quantum computing: based on a system that is capable of optimizing itself to reach the lowest energy state, quantum annealing is only suited to specific optimization problems. On the other hand, it is much easier to operate and control than gate-model computers like IBM’s. For this reason, D-Wave’s quantum computer already boasts thousands of qubits, while IBM has only hit 65 qubits.
To compare the two methods, GSK’s scientists ran the optimization algorithm on IBM’s 24-qubit Qasm simulator. This limited the outcome to four amino acids; in addition, the performance of the device was variable, with many examples of the quantum algorithm returning invalid results.
According to the paper, modelling biologically-relevant sequences would require thousands of qubits with high connectivity. “Implementing a version of this program for IBM Q devices, while successful, shows that modelling practical systems requires too many qubits to be run on even the most advanced gate-based devices available (e.g. IBM’s 65-qubit Hummingbird device),” wrote the researchers.
But although they are currently less mature than quantum annealers, gate-based quantum computers are expected to significantly increase their qubit count, while also reducing error rates. IBM’s roadmap for scaling quantum technology, for example, anticipates that a 1,000-qubit system will be available by 2023.
“While current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient,” said the researchers.
When this moment comes, the gate-based devices may be able to solve large-scale optimization problems – but also in running different types of calculations, from financial modelling to weather forecasting through traffic optimization. The range of applications that gate-based quantum computers will find, in fact, is likely to exceed that of quantum annealers. D-Wave and IBM told ZDNet they didn’t want to comment on the research.
So, while D-Wave’s quantum processor is already making strides in solving real-world problems now, a new comparison will only be fair once devices like IBM’s catch up on hardware scaling; the strengths and weaknesses of different methods will be clearer then. Until then, you can expect plenty more compare-and-contrasting from curious scientists trying to get a peek of the future.