March 8, 2021


Advances in world technology

What You Need For Your Quantum Computing Pilots In 2021

Quantum computing (QC) proof of concept (POC) projects abound in 2021 with commercialization already happening in pilots and building to broader adoption before 2025. This is in sharp contrast to 2015 where the forecasts were 20 years out or more to have practical applications in use and providing real benefits beyond digital computing. In my daily engagements’ pro bono with global communities: CEOs, computing science/engineering organizations, United Nations, investments, innovation hubs, I am finding nearly 50% of businesses see applications for QC in five years though most don’t fully understand how this will come about. In this article, I will highlight examples and key ones to follow to stay ahead and not be caught short since QC will happen: convincing POCs in five years, and proven commercialization within the next ten years.

Basic principles

A basic measure of QC capability is the number of quantum bits (qubits), the fundamental unit used to store and process data. Digital computers use bits which are like a light switch, on or off. Qubits can store a linear combination of one and zero (called superposition) encoding, an infinite number of possible states in each individual qubit creating powerful capabilities. While two bits represent two states in the classical computer, two qubits together can be in four different states. For n qubits the number of states increases by 2 to the nth power (2^n). So, for a 10-qubit configuration, each qubit can store 2^10 combinations; for 32 qubits, each qubit can store 2^32 combinations—that’s more than four billion. That’s much better than 32 digital bits that are either one or zero. The added quantum properties of entanglement where qubits work in perfect tandem, leads to exponential computing capabilities not possible even with the fastest exascale next generation supercomputers. Exascale supercomputers which are being released in 2021 can do more than 1 billion-times-billion calculations per second or more than 1000 petaflops.

Qubits are rather unstable and noisy—break down or decohere before they can do something useful—one of the fundamental challenges with QC. There are ways to address this though we are still in the early days. 

So, when qubit figures are published for quantum machines they are usually in terms of stable qubits or logical qubits. IBM, believes these published figures are not meaningful so have proposed another number, Quantum Volume. Quantum Volume (QV) combines qubits with connectivity between the bits and quality. IonQ believes the QV numbers are too large so have come up with a measure for their Algorithmic Qubits which is log 2 of QV.

Drivers for 2021 QC proof-of-concepts and pilots

From late 2019 to the present, several pivotal events are the catalyst for 2021 QC POCs and this beyond the billions in funding from governments (examples: government reported billion plus funding in the US and 10 plus billion in China).

•      2019: Google with Quantum Supremacy, solving a quantum problem not realistically possible with classical computers (54 qubit Sycamore) – 200 seconds vs 10,000 years for a supercomputer

•      2020: IBM, Google and others—chemical molecular-bonding simulations thus practical application potential such as novel material design, understanding chemical processes such as nitrogen fixation which potentially improves food production

•      2020: IBM 1 million qubit roadmap by 2030 (Quantum Advantage 1,121 qubit system by 2023). Quantum Advantage is where a problem can be solved faster on a quantum computer than on a classical supercomputer so it makes sense to use over classical computers

•      2020 Honeywell Quantum Solutions—10x Quantum Volume annually (2025: QV of 650,000) (model H0 QV 128; model H1 with 32 atomic ions released in Oct 2020)

•      Dec 2020: USTC (China) Quantum Computer—Gaussian boson sampling (detected 76 photons approx. 200 seconds)—taking supercomputer 2.5 billion years 

•      IonQ 2020 32 QB, QV 4 million = 22 Algorithmic Qubits (AQ = Log(2,QV)) – AQ 64 2025

•      many more pivotal events and frequency increasing to weekly

Major Vendors

This area is growing rapidly by the month thus I am listing those that have entered my radar screen of ones to follow and research further:

Rigetti, IonQ, QIC (Quantum Circuits), D-Wave, Google, Amazon (Braket), Microsoft (Azure Quantum), Honeywell (Quantum Solutions), IBM (Quantum Network), Intel, Atom Computing, Xanadu, ColdQuanta, Cambridge Quantum Computing, Rahko, PsiQuantum, UK-based Universal Quantum, Alpine Quantum Technology in Innsbruck, Austria; Silicon Quantum Computing in Sydney, Australia; Cambridge Riverlane DeltaFlow.OS for Quantum Computing, Zapata, QC Ware, 1Qbit, Strangeworks, Entropica Labs.

Top Applications

QC excels at large computation problems on small data sets since current quantum computers have limited input/output capabilities. This means chemistry and novel materials such as improved batteries, catalysts, and climate action.

To engage the broader community of researchers and developers for commercialization, IBM, Google, Microsoft, and startups such as Zapata, Cambridge Quantum Computing, are providing quantum development kits (QDKs) and tools that can be used across QC hardware—future proofing time spent now in QC development. Riverlane has their Deltaflow.OS universal operating system much like Windows 10 is an operating system that works across hardware. There is the open-source LLVM intermediate language to develop applications and companies such as Microsoft have taken this further with their open-source Quantum Intermediate Representation and Q# programming language. 

There is considerable work in applying quantum principles to improve performance for algorithms running on digital (classical) computers. The non-profit organizations IEEE and ACM are good communities to follow as these efforts progress and the top technology companies are thoroughly embedded as well with pre-built solutions. These “quantum-inspired” solutions scale with quantum computers as the hardware improves and work best in optimization problems found in healthcare (especially with the Pandemic), manufacturing, energy, automotive (BMW, Daimler are active here), government, aerospace, and particularly in finance.

The leading QC POCs are in AI/ML, financial services, molecular simulation, material science, oil/gas, security, manufacturing, transportation/logistics, IT, and healthcare (pharmaceuticals).

The largest near and far-term benefits are in financial services with JP Morgan, BBVA, and Goldman Sachs actively exploring QC. Three leaders to follow are Stefan Woerner (IBM), Will Zeng (Goldman Sachs), Marco Pistoia (JP Morgan).

Financial services in depth

IBM provides a good overview of QC in financial services and a good review of the current trends, data, and use cases which I adapt as a foundation below.

Many of the applications in AI also apply for QC. Think QC and/or AI applications in trading strategies / treasury & asset management, option pricing, new financial models, portfolio optimization, predicting risk and uncertainty, customer product targeting from behaviors in real-time. Market potential includes reaching two billion unbanked by reducing $40 billion lost annually from fraud/poor data analysis and reducing 80% false positives. Currently financial services companies are overly risk adverse due to the limitations in computation.

With the Pandemic, digital transformation is accelerated with adoption predicted in ten years happening now. The World Economic Forum (WEF) and Klaus Schwab 4th Industrial Revolution laid a foundation to trends to follow and implement. This evolved into wide-scale adoption across society and even culture—termed Society 5.0 (announced by Japan) to Smart Humanity (from the Dutch IT association, KNVI). This produces the outcome that 70% of banking is digital (widely reported across major consultancies) thus QC and/or AI offers faster/trustworthy credit scoring and onboarding which previously could take weeks. There is growing traction into new investment vehicles (bonds and ESG investments) in the tens of trillions, managing complexities of trading environments, re-engineer operational processes (front/back office, business optimization, risk management, compliance).

Added early-stage investments and POC examples

•      COVID pandemic drugs and predicting supply/demand

•      Save-On-Foods in-store logistics takings 25 hours per week now done in seconds

•      Portugal Nov 2019 Web-Summit Digital conference dynamic bus scheduling from 45 minutes to seconds

•      Menten AI protein folding molecular simulations for new drug therapeutics. Commercial applications before 2025

•      ExxonMobil on new energy developments

•      Used in sensing; example, quantum accelerometer hybrid better than GPS (from France digital photonics and nanoscience’s lab)

•      Used in sensing: example, quantum gravimeter (Quantum Imaging Center—University Glasgow) oil and gas deposits detection, cables, underwater, and early warning seismic-events/tsunamis

•      US National Security Agency (NSA) transitioning since 2015 for post-quantum cryptography since breaking current encryption schemes using Shor’s factorization algorithm may come to fruition around 2030. NIST (National Institute Standards Technology) call for research resulted in submissions with curation down to the top ones for post-quantum cryptography standards before 2024. Plan now for Harvest and Decrypt (bad actors storing data and decrypting later). NIST predicts 2030 as breaking RSA encryption though requires 20 million qubits

•      Work on quantum networks and quantum internet making ground such as the Institute for Quantum Optics and Quantum Information in Innsbruck, Austria which is part of the European Quantum Internet Alliance; China which has launched successive quantum communication satellites

•      Communications encryption practically applied (Quantum Key Distribution or QKD; example initially using the BB84 method and E91 protocol but now better solutions)

•      Applications using Grover’s algorithm derivatives for database searches and more

Key Resources

IEEE, the world’s largest technology engineering community, launched IEEE Quantum Week (QW) in 2020, their first major conference solely dedicated to Quantum Computing and Engineering. QW featured more than 270 hours of programming with more than 160 hours of workshops and tutorials. According to General Chair of QW in 2020 and 2021, Hausi Muller, the 2020 QW was a highly successful virtual event and the 2021 edition is scheduled for October 18-22, 2021. Hausi, professor at the University of Victoria and Co-Chair IEEE Quantum Initiative, provides his perspectives on QC and QW in my non-profit IEEE chat.  

Professor Muller shares (edited for clarity), “Today quantum technology is at our fingertips, and all of a sudden quantum software engineering has become very important. Quantum computing will always be hybrid—classical computers and quantum computers need to work together to solve real problems. To integrate classical and quantum computers, you need quantum software engineers. The IEEE Quantum Week tutorials by leading experts are aimed squarely at training quantum software engineers.”

“Till recently, everybody believed that solving real computational problems with quantum computers will take another five to ten years. The recent accelerated quantum hardware and software announcements have raised expectations considerably and opened avenues for serious applications such as simulating molecular processes—in many ways, the killer application for quantum computing. Quantum simulation for increasingly complex molecules that are beyond classical computing capabilities and scales naturally with increasingly powerful quantum computers.”

“Everybody talks about the quantum stack and how to orchestrate the software stack for different application domains. At the base layer you have quantum hardware providing qubits and circuits with a handful of hardware technologies, starting with superconducting qubits by IBM, Google and others; trapped ions with Honeywell and IonQ; Majorana qubits for topological QC by Microsoft; silicon spin qubits by Intel and others; silicon quantum photonics by Xanadu and PsiQuantum; and other related technologies in many research labs. The fidelity, coherence, and error-correction of this base layer is getting better and better. The quantum volume, defined by IBM, captures several hardware and software qualities in this one metric. Above this base, we have quantum programs or circuits that execute on these hardware platforms. These circuits are developed using quantum programming languages and quantum development kits (QDKs). The most prominent ones are Qiskit by IBM and Google Cirq based on Python; and QDK with Q# by Microsoft based on the C# language. Now the next step—all companies are deeply involved in this one—is to develop libraries, such as IBM’s Aqua and Q# libraries and provide workflows for different application domains. Examples are D-Wave’s Ocean development tool kit for hybrid quantum-classical applications and to translate quantum optimization problems into quantum circuits or Zapata’s Orquestra to compose, run and analyze quantum workflows. Above the circuits and libraries are the domain-specific application platforms. Orchestrating and integrating classical and quantum workflows to solve real problems with hybrid quantum-classical algorithms is the name of the game for the next few years.”