Investment Case for QTUM: the Next Generation of Computing

Investment Case for QTUM: the Next Generation of Computing

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Quantum Computing (QC) describes the next generation of computing innovation, which could  in turn support transformative scope and capacity changes in Machine Learning (ML).

QC harnesses the peculiar properties of subatomic particles at sub-Kelvin temperatures to perform  certain kinds of calculations exponentially faster than any traditional computer is capable of. They  are not just faster than binary digital electronic (traditional) computers, they process information in a  radically different manner and therefore have the potential to explore big data in ways that have not  been possible until now. Innovation in QC is directly linked to developments in ML, which relies upon  machines gathering, absorbing and optimizing vast amounts of data.

Companies leading the  research, development  and commercialization  of QC include Google, Microsoft, IBM, Intel,  Honeywell, IonQ,  D-Wave and Regetti  Computing. 

Governments, financial services companies, international retail firms and defense establishments have  all joined tech giants IBM, Google and Microsoft in recognizing and investing in the potential of QC. While  D-Wave offered the first commercially available QC in 2011, frontrunners have mainly concentrated  on providing cloud access to their nascent QCs. IBM were the first to make available their 5 and then  20 and now 65 qubit QC in 2016 (a qubit is the basic unit of quantum information—the quantum  version of the classical binary bit), in order to allow researchers to work collaboratively to advance a  breakthrough in this cutting-edge field. IBM have since built a community of over 260,000 registered  users, who run more than one billion actions every day on real hardware and simulators. 

The evolution of QC and its applications have been analogized to that of the PC, whose  development and full functionality were not predicted when it was first developed.1 Near-term  utilization is foreseen however, in drug discovery, the optimization of complex systems, artificial  intelligence, risk management, retail, cyber security, materials science, defense, energy, and  logistics.

Sectors spearheading experimentation with potential use cases are  primarily manufacturing, financial services, and security industries

What are Quantum Computers?

Quantum Computers (QCs) are currently incredibly large machines that are highly sensitive to electrical,  magnetic and thermal “noise” – they therefore require their own room to ensure proper operational  conditions. (The commercially available D-wave QC is housed in a 10 foot tall container. The image  shows a non-insulated or cooled QC that would not actually function due to the conditions).

QCs exploit certain physical phenomena – superposition, entanglement, and interference – to store  and manipulate information in devices called qubits. Qubits are typically unstable and perishable- they  maintain their state for around 50 microseconds before errors creep in.

Superposition refers to the combination of states that would usually be described independently. To  explain this by analogy: if you play two musical notes at once, you hear the superposition of the two notes. 

Entanglement is a highly counter-intuitive quantum phenomenon describing behavior that is not  classically logical or observable in the perceivable physical world. Entangled particles behave together  as a system so that a change to one influences the other, even if the two particles are physically distant  from each other.

Quantum interference can be understood similarly to wave interference; when two waves are in phase,  their amplitudes add, and when they are out of phase, their amplitudes cancel.

Quantum Computing takes advantage of quantum mechanics rather than electrical conductivity (as  in classical computers). While the latter hold information in bits (short for binary digits – these can be  either 0 or 1) and perform calculations using circuits that implement Boolean algebra (the logic of true/false, and/or); QCs exploit the behavior of subatomic particles such as electrons and photons, which  can exist in multiple distinct states simultaneously (imagine 0 and 1 at the same time) and whose states  can be described only probabilistically using complex numbers. QC’s use linear algebra to manipulate  matrices of complex numbers and unlike classical computers that must consider possibilities one at a  time, they can consider all outcomes at the same time.

For example, a classical search algorithm would require 5 million
attempts to find a phone number in a phone book of 10 million
entries; a QC algorithm could do it in just 1,000 operations – 5,000 times faster.

In October 2019 Google officially announced that it had achieved “Quantum Supremacy,” with its 54  qubit QC Sycamore apparently solving a mathematical calculation in 200 seconds that would take  a supercomputer 10,000 years.2 IBM quickly countered that Google’s claim was exaggerated, and  that the world’s most powerful classical computer, the Summit OLCF-4 at Oak Ridge National Lab oratory, could have performed the same calculation in 2.5 days. The announcement however, did rise  awareness of quantum computing as something with commercial applications that was no longer a  far off academic dream.

IBM and others are working towards Quantum advantage (rather than supremacy), the point at  which quantum applications deliver significant advantages over classical computers.3 It announced  progress earlier in 2019, when its 20 qubit processor doubled its previous record and produced a  quantum volume of 16 with some of the lowest error rates reported. Quantum volume refers to the  power of a QC, dependent on the number of qubits, their connectivity and coherence, together with  gate and measurement errors, device interference and circuit design among other factors.

So size is not everything. Indeed, Sycamore is not even the largest quantum processor. Google itself  has a 72 qubit processor, IBM has said it will make a 100 qubit QC available on the cloud by 2024  and California start-up and recognized major player4 in the quantum field, Rigetti has been promising a 128 qubit system for at least a year.5 While it is not know exactly when Quantum advantage or  undisputed supremacy will be achieved, we believe it will support a quantum ecosystem, starting in  the field of sampling, in which QCs transform capabilities of inference and pattern recognition in ma chine learning.6 This could in turn support real business use cases, with potentially profound implications for academia, industry, defense and other areas.

Whether “supreme” or simply  “super”, Google’s Sycamore QC  
announcement reminds us of  
the future of computing

What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence, which is the science of training machines to  perform human tasks. ML is critically important in this endeavor (and others) in that it is responsible for  cultivating the machine’s learning capability by looking at patterns and drawing conclusions. ML refers  to the cycle of asking a question, gathering data relevant to that question, designing an algorithm, testing  that algorithm, collecting feedback, using the feedback to refine the algorithm and thereby improving  performance.  

The aim of ML is to develop continually improving machines that evolve with the data to produce reliable  and repeatable decisions. The need for human interaction is steadily reduced as highly sophisticated,  evolving algorithms are continually refined until they come to reflect human thought processes as  closely as possible. ML is the tool to leverage vast amount of data to support enhanced, informed  decision-making. In a recent survey companies currently investing in cloud-based quantum computing  technologies anticipated that ML would bring improved AI capabilities, accelerated business intelligence,  and increased productivity and efficiency.7 Examples of ML already in use are recommendations made  by online retail services (Amazon) or automatic credit ratings; but when combined with the potential  achievement of quantum computing, the scope for other applications is vast.

Quantum Computing and Machine Learning applications

QC capability and ML advances will not only accelerate the processing of data, they could allow  businesses, industries and governments to reconceptualize analytic workloads, pursue new strategies  and tackle new challenges. The earliest applications, some of which are already being developed, will  most likely be in computationally intensive problems in finance, risk management, cybersecurity, materials  science, energy, and logistics. These fields stand to benefit most from the simulation, optimization and sampling breakthroughs that QC may bring.



1. Governments hold vast amounts of data, for example relating to utilities and public safety. Quantum  methods could analyze and exploit this data more efficiently to increase efficiency, reduce costs  and improve standards of living.

2. Aerospace and Defense establishments could benefit from QC advances through early adoption of  more sophisticated computers and sensors to imaging technology and cybersecurity.

3. Cryptography – QCs will be able to decode classically encrypted data. Hence companies and  governments are already working to develop new quantum-based information-technology infrastructures  to ensure that communications, banking and defense will remain secure in a quantum era.

4. Financial service companies could use QC to identify insights in data and balance many competing  factors for portfolio optimization (selecting assets and minimizing transaction costs) or algorithmic  trading, asset pricing, capital project budgeting, data security, underwriting and credit scoring and  fraud prevention.

5. Healthcare – thanks to personal devices that collect increasing amounts of information on individuals,  QCs could spearhead the provision of personalized medicine. Drug discovery could also be enhanced  by QC’s ability to analyze and optimize unprecedented amounts of complex information on the  human genome and properties of the natural world.

6. Retail – QC could transform retail in its ability to analyze buying history, make suggestions, optimize  pricing and personalize the shopping experience. It could refine online recommendations and bidding  strategies for advertisements using optimization algorithms to respond in the most effective way  to consumers’ needs and changing markets

7. Oil and Gas industries could be enhanced through a QC contribution to the analysis of minerals  in the ground to help find new energy sources, predict refinery sensor failures, and streamline oil  distribution to make it more efficient and cost-effective. 

8. Transportation and logistics could see wide applications of QC and ML technology – from the  much-hyped self-driving car (the epitome of ML success in which the car “learns” to respond as a  human would) to route-planning and optimization or flight-scheduling

9. Materials Scientists could use QC to help design new materials and industrial processes by precisely  predicting the behavior of molecules. QCs could conduct chemistry simulations to improve batteries  for electric vehicles or to develop new pharmaceuticals. They could synthesize the nitrogen reaction  that makes fertilizer (and currently depletes the worlds’ natural gas reserves); or make robotics more  effective.

By the Numbers: Quantum Computing and Machine Learning  Global Value Chain

QCs are not positioned to replace smart phones and laptops. Rather by 2030 it has been suggested  that the technology supporting these and other devices will regularly be using QC accessed via the  cloud, and that the market will continue to develop into the 2030s. 

• Global spending on AI is forecasted to double during the next four years, growing to $110 billion in  2024.9 

• The global ML market is expected to grow to $8.81bn by 2022.10 

• 74% of over 1,600 surveyed business owners, decision makers, and tech leaders consider ML a  game changer, with the potential to transform their job and industry.10

• 76% of enterprises prioritize AI and machine learning (ML) over other IT initiatives in 2021.11

Analysts have suggested that the early stages of a full QC market will be led by the Noisy Intermediate  Scale Quantum (NISQ) computing market, which should be available soon.12 While the biological and  chemical sciences disciplines are likely to find this stage the most serviceable, the fields of security and  cryptography must be made quantum-safe before large QCs are finally developed to avoid significant  liability and financial overhead in the future. Morgan Stanley have emphasized the far-reaching potential  impacts of QC in a wide variety of spheres, from oil, gas and utilities to medicine, finance, aerospace,  defense, AI, and Big Data.13

Specialists in this field suggest a strong future global market for QC hardware, separate from the software  and services enabled by it. While QCs likely won’t be superior to PCs in many areas, the niche for QC’s  contribution could become roughly equivalent to the contemporary supercomputer market (which also  consists of large, nonportable million-dollar devices that are only capable of solving certain problems),  which was worth about US$32 billion in 2017 and continues to grow.14

Catalysts for Growth

Based on CB Insights data and press releases, Deloitte has declared that “In the last three years,  venture capital investors have placed $147 million with quantum computing start-ups; governments  globally have provided $2.2 billion in support to researchers.”15 Other data reveals the steady increase  in mergers and acquisitions within the global ML space since 2010, with the 278 worldwide deals in  2019 amounting to US $13 billion.16

In our view this level of investment, combined with evidence of real progress from the scientists and  considerable commitment among potential industrial and commercial operators, reflects significant  market confidence in the future value of QC and ML. For example, in June 2020, D-Wave announced  its partnership with and $10million investment from NEC, to work together on the development of hybrid  quantum/classical technologies and services to customers in Japan.17 This followed Rigetti’s March announcement of $8.6 million from the Defense  Advanced Research Projects Agency (DARPA),  as part of a larger collaboration with the NASA  Quantum Artificial Intelligence Laboratory (QuAIL)  and Universities Space Research Association (USRA),  to develop a full-stack system with proven quantum  advantage for solving real world problems.18  

Numerous IT giants have active quantum computing  research programs, including Google, IBM, Intel,  Hewlett Packard Enterprise, Microsoft, Nokia  Bell Labs, and Raytheon. Alibaba, Google, and  IBM in particular are working on hack-resistant  encryption, software troubleshooting and ML.  Barclays, Goldman Sachs, and other financial institutions are also examining the potential of QC  in maximizing investment profits, forward planning  and data security. In the aerospace field, Airbus  is probing applications in communications and  cryptography, while Lockheed Martin is advancing  applications in verification and validation of complex  systems and the development of ML algorithms.  The US Navy pays for QC training and is working  on algorithms for optimization problems such as  data storage and energy-efficient data retrieval  with underwater autonomous robots.19

Meanwhile the UK and European Commission  have launched “national” QC strategies20 and in  the USA, Congress passed the National Quantum  Initiative Act in December 2018. This is a ten year plan, including $1.25 billion of funding over  the first five years from the Department of Energy  to support research and development in QC and  promote industry participation.

While there is still a lot of fundamental science to  be worked out and commercializing that science  will take time, there is clear market confidence,  resolution and investment on the part of the major  scientific, industrial, governmental and commercial  interests.21 Analysts understand the QC market to  be following Moore’s Law or even the more rapid  Neven’s Laws, which forecasts historical trends  in technological and social change, productivity,  and economic growth.22 These Laws anticipate  a continual series of performance improvements  and declining costs in the market, just as occurred  with classical computers in their earliest phases of  innovation and development.23 According to this  model, future stages of QC development will require  scientific breakthroughs in the capacity, stability,  and reliability of qubits and the programming  and refinement of an environment that allows  non-experts to interact with the technology.  These developments are already playing out,  with Strangeworks, an Austin USA-based start up developing tools to ease researchers’ transition  from classical to quantum computing24 and IBM  reporting and anticipating even more advances  in quantum volume.25

Analysts see “QC [as] one of the largest ‘new’ technology revenue opportunities to emerge over the next decade.” 26

Defiance Next Gen Computing ETF- QTUM provides exposure to companies on the forefront of  transformative computing technologies including quantum computing, machine learning, super  computing, artificial intelligence, cloud computing and other disruptive technologies.


• Offers investors liquid, transparent and low-cost access to companies developing and applying Quantum Computing, Machine Learning and other transformative computing technologies.27 

• Tracks approximately 60 globally-listed stocks across all market capitalizations, using the BlueStar Quantum Computing and Machine Learning Index (BQTUM)* 

• Incorporates an equal weight methodology which offers investors more precise exposure, including to smaller companies with more potential for growth.28  

* BQTUM: The BlueStar Quantum Computing and Machine Learning Index is a rules-based index  comprised of equity securities of leading global companies engaged in the research & development  or commercialization of systems and materials used in quantum computing: advanced traditional  computing hardware, high powered computing data connectivity solutions and cooling systems, and  companies that specialize in the perception, collection and management of heterogeneous big data  used in machine learning. It is not possible to invest directly in an index.

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 1 “ITIF Technology Explainer: What Is Quantum Computing?” 20 September, 2018, 

2 Arthur Herman, “The Quantum Computing Threat to American Security,” Wall Street Journal, November 10, 2019. ing-threat-to-american-security-11573411715?yptr=yahoo  

3 Larry Dignan for Between the Lines, “IBM hits quantum computing milestone, may see ‘Quantum Advantage’ in 2020s,” March 4, 2019. article/ibm-hits-quantum-computing-milestone-may-see-quantum-advantage-in-2020s/  

4 Peter H. Diamandis, “Massive Disruption Is Coming With Quantum Computing,” October 10, 2016. tum-computing/

5 Chad Rigetti, “The Rigetti 128-qubit chip and what it means for quantum,” August 8, 2019. for-quantum-df757d1b71ea

6 Masoud Mohseni, Peter Read, Hartmut Neven, “Commercialize Early Quantum Technologies,” March 9, 2017 at cation-data/pdf/45919.pdf

7 Eight leading quantum computing companies in 2020, Esther Shein, November 2, 2020. nies-in-2020/ 

8 See Duncan Stewart for Deloitte, “Quantum computers: The next supercomputers, but not the next laptops. TMT Predictions 2019,” Dec 11, 2018, at https://www2. 

9 “Artificial Intelligence: A smart investment for financial services firms,” Rich Itri, March 16, 2021, at cial-services/

10 “The Top 9 Machine Learning Use Cases in Business,” March 13, 2019.

11 “76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets,” Louis Columbus, Forbes, January 17, 2021. 

12 Duncan Stewart for Deloitte, “Quantum computers: The next supercomputers, but not the next laptops. TMT Predictions 2019,” Dec 11, 2018, at com/insights/us/en/industry/technology/technology-media-and-telecom-predictions/quantum-computing-supremacy.html.  

13 Zayan Guedim, “Quantum Leap in Computing,” October 12, 2018, Note that the high-end QC  market is just one sector within the investable universe of quantum computing and machine learning, not the primary focus of the QTUM ETF. 

14 See Duncan Stewart for Deloitte, “Quantum computers: The next supercomputers, but not the next laptops. TMT Predictions 2019,” Dec 11, 2018, at https://www2.

15 David Schatsky, Ramya Kunnath Puliyakodil, “From fantasy to reality. Quantum computing is coming to the marketplace,” April 26, 2017 at insights/us/en/focus/signals-for-strategists/quantum-computing-enterprise-applications.html  

16 Machine learning M&A total deal volume worldwide 2010-2019,” Shanhong Liu, Statista, December 7, 2020.

17 UN 17, 2020 NEC and D-Wave Begin Joint Quantum Product Development, Marketing and Sales gin-joint-quantum-product-development-marketing-and-sales 

18 l?tc=portal_CAP 19 David Schatsky, Ramya Kunnath Puliyakodil, “From fantasy to reality. Quantum computing is coming to the marketplace,” April 26, 2017 at insights/us/en/focus/signals-for-strategists/quantum-computing-enterprise-applications.html 20 “ITIF Technology Explainer: What Is Quantum Computing?” September 20, 2018 at tum-computing 

21 Paul Teich for Tirias Research, October 23, 2017, at 2b4a01207319

22 “ITIF Technology Explainer: What Is Quantum Computing?” September 20, 2018 at tum-computing 

23 “Counterpoint Global Insights: Quantum Computing,” Morgan Stanley, October 2020. tumcomputing_letter.pdf?1614770692612

24 Strangeworks Company Website. 

25 Larry Dignan for Between the Lines, “IBM hits quantum computing milestone, may see ‘Quantum Advantage’ in 2020s,” March 4, 2019. article/ibm-hits-quantum-computing-milestone-may-see-quantum-advantage-in-2020s/ 

26 See Duncan Stewart for Deloitte, “Quantum computers: The next supercomputers, but not the next laptops. TMT Predictions 2019,” Dec 11, 2018, at https://www2.

27 The possible applications of quantum computing are only in the exploration stages, and the possibility of returns is uncertain and may not be realized in the near future.  QTUM’s gross expense ratio is 0.4%. Brokerage commissions will reduce returns. 

28 Index components are assigned an equal weight subject to a liquidity overlay, index components are reviewed semi-annually for eligibility, and the weights are reset  accordingly. Fund holdings and sectors are subject to change at any time and should not be considered recommendations to buy or sell any security. See the list of the  fund’s current top ten holdings.