Earlier this week, the global quantum community celebrated World Quantum Day on Monday, April 14th. It was a great time to pause and reflect on how far this technology has come over the past few years and the journey that remains in front of us. 

What Exactly is Quantum Computing? 

Quantum computers are a paradigm shift in information. They employ a fundamentally different logical unit – a qubit – derived from quantum physics. Classical computers use binary bits (0s and 1s) that comprise everything from social media to AI, gaming, and more. Qubits go beyond the scale, speed, accuracy, and security of what we can achieve even with the world’s largest supercomputers that rely on traditional, binary information. These computational characteristics signify the potential that quantum computers can address tough challenges like sustainable agriculture and open new regimes for scientific exploration like understanding the link between gravity and quantum particles.

With that primer out of the way, let’s get into the latest:

Going back to 2020, right as shutdowns began in California, a group of computer scientists and I envisioned future quantum computing infrastructure as a quantum data center with interconnected quantum computers, further integrated with classical compute technology like GPUs. What was then a dream is now becoming a reality. A Boston-based Accelerated Quantum Research Center was announced this past month housing GPU-accelerated supercomputers alongside quantum computers. In the same month, MIT researchers revealed quantum interconnects optimized with AI. I’ve used a lot of buzzwords here to call out important research announcements, so let’s break it down.

The AI Era and Heterogeneous Computing

The AI era is changing how we think about computing systems. Given the high demand for compute resources, different tasks are often performed on different processors. This approach to AI workloads, soon to be our new normal, is known as heterogeneous computing.

Quantum will both benefit from and contribute to heterogeneous architectures because AI makes quantum better and quantum makes AI better. Using AI techniques like reinforcement learning to control quantum computers can leverage GPUs on the edge. And using quantum for AI can realize tangible model enhancements on traditional AI tasks. This intersection of AI + Quantum is fast materializing as the future of data center computational workloads.

While I find myself musing on how far quantum has come, I should call out other attention-grabbing headlines from this past year: beyond logical threshold quantum chips, potential scientific advancements in topological qubits, and beyond classical certified randomness. These technological breakthroughs are marked and signify quantum technology progress; however, it can be challenging to discern what’s what and why someone should care about these quantum computing advancements. 

 Let’s see why these quantum computing developments matter: 

• Beyond Classical Results: You may have heard terms like quantum advantage and quantum utility of late. These terms mean quantum is providing answers to pressing problems that a classical computer will likely never compute accurately. For example, chemistry problems like modeling lithium interactions to produce better batteries, or material problems like high temperature superconductivity, which are too complicated to solve on classical computers. Quantum computation speed can also optimize large asset portfolios faster, enabling more exact risk calculations to give investment firms a competitive edge. And of course, let’s not forget better, more efficient AI.

• Quantum Algorithm Innovation: Algorithms that can solve hard problems faster than any classical computer don’t fall off trees. And if you do happen to find one, once you start getting into the weeds of how to practically run the algorithm, you may find yourself scratching your head wondering how ‘practical’ this quantum algorithm business really is. Luckily, the world’s smartest people are tackling these problems by inventing new quantum algorithms and working to make them more practical to execute and accelerate computation.

• Logical Quantum Computation: Qubits are sensitive to their environments and the information they hold degrades quickly. Quantum computers therefore have to overcome noise and error to reliably run the quantum algorithms that will transform both science and industry. This is done using quantum error correction, which can produce a logical qubit that reliably executes algorithms. Quantum technology roadmaps have 50-100 logical qubits as a near-ish term destination; however, there are subtleties in these statements. Pay attention to logical qubit demonstrations that go beyond the error threshold, and the implementation of logical quantum algorithms that go far beyond what classical computers could ever do.

• Distributed Quantum Computation – What makes a supercomputer super is the network underneath. Networking quantum computers together so they can share quantum information will scale the architectures to successfully execute on beyond classical use cases and realize the full promise of quantum computing.

Logical qubits, mentioned above, require hundreds of physical qubits to stabilize performance. Creating a single quantum chip that can support these workloads is unlikely. Therefore, linking multiple chips together across quantum computers is the strongest path to scale the machines. That’s why the new research linked above is so notable.

Will My Next Laptop Upgrade Be a Quantum Computer? 

Not exactly—quantum computers require specialized equipment and environments as cold as space to stabilize quantum coherence. I certainly don’t want a dilution refrigerator at my desk…offices are cold enough!

Today, quantum computers are likely to remain in specialized, quantum data centers, and act as research machines. Startups and large tech companies have made tremendous technical strides, but it is still going to be some years before quantum computers will cost-effectively support production workloads for enterprises. 

Quantum Inspired Today

Even so, quantum-inspired approaches are nearly ready for commercial applications. These are classical algorithms that make use of quantum theory approaches without needing quantum computers. Quantum inspired algorithms have the benefit of running on standard or accelerated (e.g., GPUs) computer hardware. Banks with leading quantum programs are already applying these methods for fraud detection and financial modeling. Such techniques are becoming early commercial value generators for industrial quantum programs, and will further prepare the enterprise for future, mature quantum hardware adoption.

I’m In! How Do I Prepare for the Quantum Computing Revolution? 

Quantum innovation is accelerating with frequent breakthrough announcements. A thoughtful approach that combines early business benefits using quantum-inspired computing, along with exploring quantum computers, will build a powerful base for long-term quantum success.

Curious about what a quantum-enabled future may look like? Check out the Deloitte Quantum Page, which discusses what we might use quantum computers for, the use cases, and the cybersecurity risks that future quantum computers might pose. 

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Here’s to keeping our ideas entangled and our progress exponential! 

— Mekena McGrew, PhD, Deloitte Quantum Information Lead