A non-quantum take on IBM’s quantum computing roadmap

Quantum computing will complement classical computing—not replace it. And like with all emerging technologies, it’s using them in combination that generates the most value: quantum computing will sit alongside classical computers, to deal with complex algorithms sent via cloud and then back again, ultimately producing new insight.

From my brief conversations with the real experts, quantum conversations need to focus on:

  1. Road-mapping and future potential
  2. the ecosystem
  3. complementing existing technology—not replacing it
  4. outcomes, as always…

Focusing on outcomes is as important for quantum computing as it is for any other technology.

The point where quantum computers start to outperform our most powerful supercomputers—or “quantum advantage”—is 3 to 5 years away on IBM’s latest quantum roadmap. So, the conversation has to change if you’re hoping to reach these outcomes. Start with: what are the barriers to solving the most complex problems in my business and/or industry? Is it computing power, memory, cost, accuracy? Can quantum computing potentially remove these barriers? If the answer is yes—start planning your roadmap over the next 5 years and beyond.

For more information see this particularly useful video from IBM.

IBM’s roadmap hits “quantum advantage” in 2023: when quantum computers will outperform our best currently available supercomputers. But even then, it’s not about replacing. It’s about complementing.

When it comes to any emerging technology, many are blinded by hype or “use cases” that aren’t really use cases, and are too quick to assume maturity and rush toward integrating it into their business… so naturally, some people could be disappointed by quantum computing. We’re still very much in the “making” phase of quantum hardware and software, and despite a range of quantum computers being operational, we’re not going to achieve the poster-child use cases i.e. beating classical computers, for 3 to 5 years in the eyes of IBM… and even then, it’ll be in hyper-specialized areas of chemistry and physics, finance, and optimization.

When we talk about quantum “use cases” now, what we’re really talking about is future potential:

  • Simulating physical processes like quantum mechanics: The first cases will likely be in theoretical chemistry and physics, while in the pharmaceutical industry, for example, simulating the behavior of large molecules and complex reactions is a longer way off.
  • Complex material discovery such as sustainable fuels (including nuclear isotopes) or low-carbon but super-strong building materials.
  • Risk modeling and analysis of complex, multi-dimensional interactions where multiple tiers of cause-and-effect (just like in nature) cannot currently be fully mapped e.g. the broad effects of the COVID-19 pandemic throughout supply chains and beyond into financial markets.

If you’re aiming for one of these use cases, or something similar, start to think about what you might use quantum computing for within the problem: what bottlenecks will you face in using classical computing—power, memory, accuracy, or cost? And how might having an ultra-powerful quantum computing machine just a stone’s throw away (via cloud) help to complement other emerging technologies you’re throwing at the problem. For want of a better analogy: Quantum computing will be the ultimate outsourcing machine for complex calculations.

Quantum computing intersects with the emerging technology spectrum and could soon (ish) be IBM’s new Watson—but it must focus on business outcomes.

IBM emphasizes that quantum computing will not replace classical computers—it’ll augment classical computers, but also complement the ever-expanding toolbox of emerging technologies. Applied to AI applications, quantum computing is expecting to run them faster and more precisely to extract information we couldn’t see before from classical computers. The convergence of hybrid cloud and AI architectures assisted by quantum computers will help to no end in programming and will revolutionize the way science is fundamentally carried out to accelerate discovery and underpin a whole new class of mission-critical applications.

This fits the “new IBM” bill well—having spun-out its managed infrastructure business—as it doubles-down on hybrid cloud and AI. IBM must, however, maintain a laser-like focus on applications and business outcomes of all this technology—including quantum—so that it doesn’t get lost in its own riches.

IBM currently has 20 quantum systems on its cloud—of which 10 are open and accessible to anyone.

Most of the 250,000+ users work through Jupyter notebooks, on Python code, via Qiskit, which’s seen over half a million downloads to-date.

IBM’s first quantum computer came online in May 2016 at 5 qubits (the most common measure of a quantum computer’s power—analogous to a bit, but capable of existing in way more states than zero or one). Since then, over 536 billion quantum circuits have been performed (the transformational operations that manipulate qubits into their various states to calculate complex problems—in IBM’s case, “superconducting qubits” are manipulated by microwave pulses). Currently, the biggest system IBM has comes in at 65 qubits. IBM plans to hit 1000 qubits in 2023 and in doing so reach the “quantum advantage” point where the system can outperform the most powerful supercomputers we currently have.

IBM is taking a Tesla-like approach to building the quantum computing ecosystem. Policymakers need to get themselves involved.

Google, Honeywell, and the startup landscape are where IBM sees the competition. But it’s more than competition. IBM is taking an ecosystem approach much like Tesla’s approach to the battery ecosystem: opening up its systems to promote innovation.

There are many options available when it comes to contracts, go-to-market, and partnership working for IBM in the quantum sphere: free-to-use computer systems are just one form of democratizing quantum computing by not restricting access. IBM is combining the competencies of its partners to prepare industry-specific applications for when the time (and technology) is right.

The Bottom Line is that IBM and its partners need to educate the ecosystem as we move towards real business value in 3 to 5 years. A quantum computing ecosystem, like any ecosystem, must include enterprise partners, startups, universities (and the students who will one day join the quantum workforce), potential clients, implementation partners, and policymakers.


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