Summary of "Quantum Computing: The Past, Present, and Future" | Jonathan Wurtz's Presentation at Pawsey

This blog summarizes the presentation "Quantum Computing: The Past, Present, and Future" given by QuEra's Quantum Solutions Lead, Jonathan Wurtz, at Pawsey Supercomputing Centre in Australia on March 17, 2025. The high-level talk covered QuEra's background, the theory behind quantum computing, hardware & algorithms quintessential to advancement today, and quantum co-design. He offers a unique expert perspective on the state of quantum computing today and what it means for real-world applications. This blog was authored by a listener to Jonathan's talk, not by Jonathan himself.

1. A New Lens on Science and Computation

One of the most memorable analogies from Jonathan Wurtz’s presentation is that quantum computers might become our era’s “microscope.” Just as early microscopes revealed a hidden universe of single-celled life that no one had previously imagined, quantum computers could serve as an instrument to unlock entirely new scientific vistas—even yielding insights or applications we have yet to conceive. This possibility underscores the excitement that has been building around the field.

But, as with microscopes in the seventeenth century, the quantum computing field is still taking shape. Tools are emerging and capabilities are being demonstrated, but many breakthroughs await. The talk’s central message is that quantum computing is advancing rapidly, and organizations across industry and academia are investing now to be ready for—and help shape—the remarkable possibilities that could arrive.

2. Why Quantum? A Look at the Big Four Applications

Wurtz highlights four broad areas that drive most discussions on quantum computing’s potential. While these categories are not all-encompassing, they serve as a good starting point for understanding why people see quantum computing as a paradigm shift.

• ‍2.1 Optimization
  Why it matters: Many of today’s biggest computational challenges boil down to optimization. You want to minimize cost, maximize output, or find the best way to route a fleet of trucks. If you have thousands or even millions of interrelated variables, the search space grows exponentially, and classical computers often resort to heuristics or approximations.
  Quantum’s role: Quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) or Grover’s search show promise. They leverage “entanglement” and “interference” to guide the system toward optimal solutions faster than classical methods can—at least for certain structured problems. While the race isn’t yet definitively won, researchers keep refining quantum-heuristic approaches, pushing the boundary beyond what classical tools alone can achieve.‍
• 2.2 Quantum Simulation
  Why it matters: “Simulate nature with nature” is how some enthusiasts describe quantum simulation. Standard (classical) computers find it notoriously difficult to simulate quantum effects in materials, proteins, and molecular processes—these are inherently quantum phenomena. But by building a controlled quantum system, you can effectively “mirror” the physics of, say, a new material or a complex molecule.
  Quantum’s role: Quantum devices can emulate chemical bonds, exotic phases of matter, or even quantum field theories that are otherwise intractable with classical computers. Such simulations could be pivotal for discovering new pharmaceuticals or designing advanced materials (e.g., better energy storage, improved superconductors). Even near-term quantum devices, operating with dozens or hundreds of qubits in specialized “analog” modes, can already yield valuable insight into quantum chemistry and condensed matter—no error correction required.‍
• 2.3 Machine Learning
  Why it matters: From product recommendations to autonomous vehicles, machine learning is transforming the modern enterprise. But training large models or generating realistic data can overwhelm even today’s best high-performance computers.
  Quantum’s role: While quantum machine learning is still a young topic, quantum computers excel at sampling from complex probability distributions—arguably one of the hardest tasks in ML. The hope is that a quantum system acting as a specialized “kernel” or “reservoir” might provide richer, more powerful transformations than its classical counterpart. That could mean faster learning or the ability to tackle highly correlated data that is difficult for classical methods. Research in this area is burgeoning, although most demonstrations are still confined to small- or medium-scale devices.‍
• 2.4 Code-Breaking & Cryptography
  Why it matters: The mere possibility that quantum computers can factor large numbers much faster has rattled the world of cryptography. Modern secure communication—think online banking—relies heavily on encryption whose strength comes from the difficulty of factoring large integers.
  Quantum’s role: Shor’s algorithm shows that factoring can, in principle, be done exponentially faster on a quantum computer. But this feat requires thousands (if not more) of stable, error-corrected qubits. No one is close to that level of hardware yet, but governments and companies aren’t waiting. They are developing “post-quantum” cryptographic standards that aim to withstand a future quantum threat.

3. Four Modes of Quantum Computation

Wurtz classifies quantum computing approaches into four conceptual “modes.” Each reflects a different way of controlling and orchestrating qubits.

1. ‍Adiabatic (Annealing): The system slowly evolves from a simple quantum state into a target ground state encoding the solution to a problem (often combinatorial optimization). Annealers like D-Wave specialize in this approach.‍
2. Analog: You program a continuous “Hamiltonian” (the energy landscape) that operates on many interacting qubits or atoms. This can be extremely powerful for simulating physics directly, but it is not universally applicable to all algorithms. Neutral-atom devices, like QuEra's Aquila for instance, frequently use analog methods to simulate quantum materials.‍
3. Digital (“Noisy” Gate-Based): Gates—analogous to logic operations in classical computing—are applied in sequences (circuits). You can program a wide variety of algorithms, but noise and decoherence limit how many gates you can apply before errors become overwhelming.‍
4. Error-Corrected (Logical Qubits): By encoding a single “logical” qubit into many physical qubits, one can detect and correct errors on the fly. This is the holy grail for large-scale quantum computing, allowing for deep, complex computations. Until recently, demonstrations were minimal, but multiple hardware players have shown progress toward scaling up logical qubits.

4. The Hardware Landscape

Quantum hardware, the engines behind all these modes, typically revolves around four primary modalities:

1. ‍Neutral Atoms – Arrays of individual atoms (e.g., rubidium), held in place by laser light. They offer a path to large numbers of qubits and can be reconfigured mid-circuit.‍
2. Trapped Ions – Chains of ions controlled via electromagnetic fields. They have long coherence times and relatively uniform qubits.‍
3. Superconductors – Qubits built out of superconducting circuits, controlled by microwave pulses. Major players like IBM and Google have bet heavily on this technology.‍
4. Photonics – Qubits encoded in light (photons). These promise natural connectivity and the potential for interesting networking applications, but face challenges in scaling stable gates.

No single approach “wins” outright; each has advantages for particular needs. In the longer run, specialized tasks may benefit from specialized hardware. The community is also investigating the possibility of combining multiple types of hardware via quantum interconnects.

5. The Road to Fault Tolerance

A recurring theme is how to push forward from today’s “NISQ” (Noisy Intermediate Scale Quantum) devices—where noise limits the depth of algorithms—toward large, error-corrected machines. Error correction encodes one logical qubit across many physical qubits, frequently using sophisticated techniques such as:

• ‍Surface Codes: Arrange qubits on a grid and measure “syndromes” to detect and correct errors.‍
• Color Codes or LDPC Codes: Alternative structures that also permit error detection and correction with certain advantages, such as potentially fewer overhead qubits or faster decoding.

Although a fully fault-tolerant processor that can handle tens of thousands of qubits is likely years away, each incremental step—demonstrating, for instance, a single logical qubit that outperforms its best physical qubits—represents a milestone. Reaching “utility-scale” quantum computing will hinge on continuing these advances while keeping noise at bay.

6. Quantum Co-Design and Hybrid Integration

Wurtz strongly emphasizes the role of “co-design,” a process in which hardware, algorithms, and applications are crafted with an awareness of one another. Rather than waiting for a universal quantum computer that can do anything, a co-design mindset says:

1. ‍Identify a high-impact problem (or set of problems).‍
2. Match it to the right hardware modality (such as analog quantum simulation for certain physics or chemistry tasks).‍
3. Optimize the algorithm to exploit that hardware’s strengths while respecting its constraints (e.g., connectivity, coherence times).

In parallel, we should expect tight integration between quantum processors (“QPUs”) and classical resources. Many quantum algorithms require extensive classical supervision: from scheduling gates, to decoding error syndromes in real time, to analyzing measurement outputs. Thus, in the not-so-distant future, organizations may see quantum acceleration living alongside—or even within—existing HPC (High-Performance Computing) environments, rather than as a standalone box.

7. Why This Matters Now

7.1 Readiness and Early Prototypes

While “useful” quantum advantage for commercial applications remains an open question, many enterprises, governments, and researchers are investing heavily to prepare. This is no longer purely a world of fundamental physics. Cloud-accessible quantum hardware already exists, allowing scientists to test small algorithms in real environments.

7.2 Avoiding Missed Opportunities

Much like 3G networks in the early 2000s, large sums are being spent before all end uses are clear. Years ago, most of us did not foresee smartphones, let alone app stores. By building out quantum hardware and the surrounding ecosystem, we create a foundation upon which “unexpected applications” could flourish—ranging from new materials discovery to specialized optimization for finance, logistics, or pharmaceuticals.

7.3 Post-Quantum Cryptography

Shor’s algorithm famously looms over standard encryption. While the quantum threat might lie five, ten, or even more years away, industries responsible for long-lived sensitive data are transitioning toward post-quantum encryption today. The talk underscores that though full-scale code-breaking is not imminent, prudent cybersecurity strategies can’t ignore the forward march of quantum hardware.

8. The Path Forward

Jonathan Wurtz’s message is equal parts anticipation and pragmatism. Quantum computing is already advancing beyond the lab-bench prototype stage; companies, universities, and national labs around the world are using or building near-term systems for tasks in quantum simulation, small-scale algorithm demonstrations, and error-correction experiments.

Yet significant challenges remain, from scaling up qubit counts to refining error-correction strategies that will allow extended computations. As hardware designers continue to iterate and software developers learn how to run more clever quantum-classical “hybrid” workflows, it’s conceivable that quantum hardware could evolve into a specialized—but indispensable—accelerator in a future HPC ecosystem.

From a business standpoint, it’s wise to consider quantum as a strategic horizon technology. Many organizations are investigating potential “killer apps” or forging partnerships to access quantum hardware in the cloud. In the same way that HPC became essential for data-intensive tasks, quantum may become essential (in certain scenarios) where even HPC struggles.

9. Final Thoughts

Wurtz’s talk ends with a call to action for collaboration across all layers—hardware, algorithms, and real-world use cases. The quantum computing community is no longer just about theory or small proof-of-concept systems. It’s about bridging the gap between research and meaningful commercial or scientific application.\

• ‍Co-design stands out as a guiding principle: find problems that match the right kind of quantum platform, and tailor the approach to those strengths.‍
• Integration with powerful classical computing resources is equally important: as quantum devices scale, real-time error correction and large-scale data handling will require HPC-like infrastructures.‍
• Practical readiness must also guide CIOs and strategists: it’s never too early to investigate possible use-cases, skill up teams, or start pilot programs—even if “full-scale” quantum won’t arrive for several more years.

In sum, quantum computing has the potential to transform entire sectors, from materials science to finance to cryptography. As Wurtz’s lecture makes clear, this technology has matured significantly in a short time. While hurdles remain, the pace of breakthroughs—in hardware fidelity, logical qubit demonstrations, and real-world tests—suggests that quantum computing is swiftly moving from a theoretical frontier to a strategic resource that forward-looking organizations should keep firmly on their radars.