The quantum computing revolution, part II: the path to commercialisation
There is no conclusive evidence yet that QAs are superior to classical computers. Most commercial use is mostly experimental in nature. However, with more and more experience and hardware advancement, there are already promising signs that annealing is delivering some benefits over current technologies — for example, see Quantum annealing versus classical machine learning applied to a simplified computational biology problem.

A universal quantum machine will deliver exponential improvement in capabilities

Universal QCs will not offer a mere improvement in computing capabilities, but a new paradigm, enabling us to tackle challenges thought to be insolvable with classical computers. There are three broad classes of problems that QCs should, theoretically, excel at:

Factoring (cryptography) — Cybersecurity systems and protocols rely on encryption for secure communication of information (including password protection, secure web browsing, etc.). A large proportion of encryption techniques, including RSA (widely used in digital signatures) and cryptographic key exchange protocols (e.g. Transport Layer Security handshake, or TLS), are based on solving the discrete log problem or the problem of factoring large integers. These algorithms have been considered impractical to break because of the time and computing power it would take for a classical machine to do so (time is exponentially growing with the number of bits used in the encryption). However, the properties of QC significantly challenge these assumptions. In fact, the first quantum algorithm (proposed by Peter Shor) to demonstrate the practical potential of QC solved the problem of factoring large numbers and computing discrete logarithms. It achieved that exponentially faster than any algorithm developed for a classical device. For example, it would take the best-in-class modern classic machine 300 trillion years to break an RSA key with 2048 bits, while a QC with 4099 logical qubits (i.e. ones that could be used in calculations) could do so in 10s. Admittedly, we are some time away from engineering a machine with 4099 logical qubits (would require thousands more physical qubits given the state of the art error correction techniques available today). However, it is clear that QC poses a significant threat to existing security software infrastructure and this has put many in the cyber community on high alert to develop quantum-secure encryption algorithms.

Commercial examples: The US National Institute of Standards and Technology (NIST) has an ongoing competition to build quantum-resistant encryption algorithms. Companies such as Post-Quantum and PQShield are participants in NIST’s programme.

Optimisation (including machine learning) — optimisation problems exist across a range of domains (transportation, finance, energy, logistics, engineering, computer science, etc.). The goal is to find the best solution of a set of possible solutions and usually involves minimising the error between a given solution and the correct solution. Classical computers need to sequentially optimise individual variables of the system before they can arrive at an answer. QCs can operate on multiple variables simultaneously, thus exponentially speeding up the calculation. QAs are particularly well suited to solving optimisation problems and have already been deployed commercially in industries such as finance, transport and supply chain. They can also be used to meaningfully improve machine learning capabilities.

Commercial examples: JPMorgan Chase wants to optimise stock trading strategies and financial risk analysis using QC. Delta Airlines is trying to use QC to optimise its flight schedule after massive disruptions like hurricanes and blizzards.

Simulation — Simulating quantum systems is probably the most natural problem set for a QC. The general purpose here is to simulate the environment in which the system exists and the interactions with that environment. There are a variety of valuable applications in this area ranging from material science to medicine. Via simulations of chemical reactions, scientists are hoping to revolutionise the way new materials are created. Some of these simulations may require QCs with only a few hundred qubits and therefore could become one of the first real-life applications for gate model computers. D-Wave has already employed their QA hardware in a number of scenarios, proving that it is capable of doing simulations of electronic structures.

Commercial examples: D-Wave has already employed their QA hardware in a number of scenarios, proving that it is capable of doing simulations of electronic structures (see the table above). ProteinQure has partnered with QC manufacturers (IBM, Rigetti) and pharma companies (AstraZeneca) to explore approaches to modelling proteins.

Chasing Quantum Advantage

A highly sought after milestone in the race to developing the first QC is achieving “quantum advantage,” i.e. demonstrating that a QC can perform a task that is beyond the capability of any classical computer. This often gets mixed up with achieving ‘quantum supremacy’, which has a tighter definition of a QC outperforming a classical one for solving a particular problem. In October 2019, Google published a paper claiming it hit quantum supremacy — the company used its 53-qubit quantum processor to perform a random sampling calculation (verifying a set of numbers is randomly distributed) in 200s, a speed unattainable for a classical computer. Arguably, the problem that Google chose is of little practical significance. Google claimed it would have taken IBM’s Summit, the most powerful supercomputer currently, 10,000 years to arrive at the solution. IBM argued that using a specific algorithm designed algorithm Summit could solve the problem for 2.5 days. Ignoring the technicalities, while it cannot be classed as ‘quantum advantage’, Google’s achievement is definitely a step in the right direction.

That said, we should not lose sight of the big picture — we are still in the era of the NISQ machines that are error prone, low in computing power and difficult to control. Using a QC to solve the hard problems that will meaningfully impact science, engineering and business is still years away. Using an aviation analogy, Google’s milestone is comparable to Wright Brothers’ first flight, which took place in 1903. The first commercial flight was not until 1910.

Cloud is emerging as the preferred delivery mechanism

While QC technology is relatively nascent, the potential for unlocking powerful use cases is enticing enterprises and academics alike.

D-Wave was one of the first companies to sell QCs commercially. In 2010, Lockheed Martin became the first customer, purchasing a 128-qubit D-Wave One machine (installed at University of Southern California’s Information Science Institute site). Following that milestone, other organisations (including Google and NASA) have also purchased D-Wave’s hardware or access to it. While there is still no conclusive evidence that annealers are supreme to classical machines, D-Wave helps its customers design the algorithms to achieve cost, speed and quality of answer advantages for specific problems. This approach has certainly resonated with its client base, which has now signed contracts with a total value exceeding $75m. Recently, D-Wave launched its newest machine using 5,000 qubits.

D-Wave started off by selling hardware for on-premise installation, however, the company is increasingly moving to a cloud model, i.e. providing access to machines instead of ownership. The cheapest tier on their new Leap 2 offering provides 10 minutes of access to a quantum process for $335 (additional time is charged at $2000/hr).

Among the major tech providers Alibaba, Amazon, IBM, Honeywell and Microsoft have introduced cloud access and application development environments to work on top of their hardware. Despite being among the QC pioneers, Google is yet to provide cloud access to their quantum device. Rigetti Computing is another prominent QC vendor that offers access over the cloud.

Cloud access to compute has well-understood benefits for the consumer: reduced capex, lower maintenance costs, greater scalability and flexibility. These are even more important for QC due to the highly sophisticated hardware involved and the specialised environment required. For the operators, cloud delivery allows more control over the hardware and greater flexibility when it comes to upgrading, introducing new software, languages and applications. Most startups also plan to use the cloud as the default access mechanism and commercial model. That said, the convenience and lower cost of cloud access have to be balanced with security considerations. Given the strategic importance of QC, it is likely that some companies would prefer an on-premise installation. But, at this point, it is difficult to see QCs being mass produced for home or professional environments.

The future is quantum… start now

Theorists in QC expect future machines to represent a step change in electronics, not too dissimilar to the progress made possible by the invention of the classical computer. Given the engineering challenges scientists face, a mature universal QC is at least 10 years away. Therefore, current efforts naturally focus more on the technology rather than thoroughly exploring all the possible applications. Nevertheless, experts envisage a revolution in our understanding of important science and engineering fields such as machine learning, materials, chemistry, medicine, meteorology, cyber security, code breaking.

Governments and companies should prepare for the potential paradigm shift because the implications will be profound. There are at least four axes for leaders to consider:

Quantum-proof security — a mature QC capability might render the present encryption techniques obsolete. Shifting to a quantum-secure architecture, by relying on technologies that harness quantum mechanics, is already becoming a priority for security leaders. The time required to acquire the new expertise and tooling should not be underestimated — the latest report on cyber security risks by the World Economic Forum further emphasises this point.

— a mature QC capability might render the present encryption techniques obsolete. Shifting to a quantum-secure architecture, by relying on technologies that harness quantum mechanics, is already becoming a priority for security leaders. The time required to acquire the new expertise and tooling should not be underestimated — the latest report on cyber security risks by the World Economic Forum further emphasises this point. Impact of new use cases and applications — as with any new enabling technology, the ‘killer app’ is difficult to imagine today. However, most of the theoretical research points to significant disruption across multiple industries. For example, advances in drug design, faster drug manufacturing, the invention of new materials, or the application of more powerful AI are all types of use cases that can materially shift industry structures. This explains why most governments think of the development of quantum technologies as a matter of strategic importance and a national priority.

— as with any new enabling technology, the ‘killer app’ is difficult to imagine today. However, most of the theoretical research points to significant disruption across multiple industries. For example, advances in drug design, faster drug manufacturing, the invention of new materials, or the application of more powerful AI are all types of use cases that can materially shift industry structures. This explains why most governments think of the development of quantum technologies as a matter of strategic importance and a national priority. New skills required — in many industries, maintaining a competitive edge will require harnessing the power of quantum systems. Organisations that want to be successful in navigating the hype and identifying business-relevant opportunities will need to develop QC competencies. An interdisciplinary skillset will be important in the early days both for assessing the business impact and selecting the relevant technology stack. Fortunately, with many cloud offerings up and running (see above), some of the tools needed to start experimenting are already present.

— in many industries, maintaining a competitive edge will require harnessing the power of quantum systems. Organisations that want to be successful in navigating the hype and identifying business-relevant opportunities will need to develop QC competencies. An interdisciplinary skillset will be important in the early days both for assessing the business impact and selecting the relevant technology stack. Fortunately, with many cloud offerings up and running (see above), some of the tools needed to start experimenting are already present. Regulation and responsible design — QC is a technology with significant disruptive potential. Therefore, who gets to develop, use and benefit from it matters. We are still in the early days of development and this presents an opportunity to embed ethical and responsible design principles in our approach to the technology.

QCs with 50–70 logical qubits would be only marginally superior to the most powerful classical supercomputers and this would be true for a relatively limited subset of problems. For example, in material science/chemistry, such quantum machines would simulate relatively simple structures, while the goal is to be able to examine complex reactions in order to find new, more efficient methods to produce ammonia, or to simulate the reactions occurring in the first moments of the universe after the Big Bang. The highly anticipated potential applications discussed above (e.g. code breaking or drug design) require thousands if not millions of logical qubits, a feat, which is clearly beyond any current engineering capability. Therefore, finding applications, which are going to fill the gap between the computers that we have today and the universal quantum machines of the future is crucial to keep investment flowing in.

UK is a global leader in quantum technologies

UK is one of the leaders in quantum technologies, ranking fourth by R&D budget globally.

UK ranks fourth by public investment into QC globally. Source: Qureca ltd.

The UK National Quantum Computing Programme is organised in four hubs, each focussing on a different set of quantum technologies:

The Sensors and Timing Hub aims to develop a range of quantum sensor and measurement technologies

aims to develop a range of quantum sensor and measurement technologies The Quantum Imaging Hub works on new types of ultra-high sensitivity cameras such as single-photon visible and infrared cameras, single-pixel cameras, extreme time-resolution imaging, 3D profiling, imaging beyond line-of-sight, and imaging of local gravity fields.

works on new types of ultra-high sensitivity cameras such as single-photon visible and infrared cameras, single-pixel cameras, extreme time-resolution imaging, 3D profiling, imaging beyond line-of-sight, and imaging of local gravity fields. The Quantum Computing & Simulation Hub focuses on accelerating progress within QC.

focuses on accelerating progress within QC. The Quantum Communications Hub focuses on the research, development and commercialisation of quantum secure communications.

The government provided £120m of funding to these hubs over a five year period (2014–2019). In 2019, the UK government launched a second wave of government support worth £94m. As part of this second phase, a National Quantum Computing Centre was also established. This is a dedicated national centre with £93m of funding covering four key streams:

100+ qubit NISQ demonstrator hardware platform(s)

Quantum software, algorithm & applications development

High performance, scalable qubit technology development

Roadmap and architecture towards fault-tolerant general purpose quantum computing.

The robust academic activity and government support are supporting the emergence of a vibrant early-stage ecosystem.
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['Quantum Computing', 'Quantum', 'Insights', 'Technology', 'Venture Capital']