14/04/2025
“I don’t like it, and I’m sorry I ever had anything to do with it.” These were Erwin Schrödinger’s words on the probability interpretation of quantum mechanics (as cited in John Gribbin’s “In Search of Schrödinger’s Cat” [1]. 100 years on from Erwin Schrödinger’s first formulation of wave mechanics and 2025 has been designated by the United Nations as International Year of Quantum Science and Technology. Even more, today marks World Quantum Day 2025 – a global celebration promoting public awareness and understanding of quantum technology. To honour this ‘superposition’ of quantum events, this blog post looks at some recent developments in quantum computing followed by its potential applications to the healthcare sector.
In the past few months, the world of quantum computing has been a hive of activity. It’s no secret that thorny issues like background noise, instability and error correction have long-held-back scalable, real-world quantum applications. However, a series of big ticket announcements such as the development of the first quantum operating system [2] and D-Wave’s recent claim to quantum advantage [3] have renewed optimism that fault tolerant quantum computing’s ‘ChatGPT moment’ may be years, not decades, away. In particular, many chip makers are announcing collaborations with companies in the healthcare sector – something that perhaps even Schrödinger would not have foreseen at the time.
What is quantum computing and why does it matter?
Classical computers operate on the basis of bits, a digital unit that can be either 0 or 1. In modern computers this is implemented with transistors. Quantum computers differ from normal computers in that their quantum bits, or qubits, can be both 0 and 1 at the same time, a so-called superposition state. A single qubit can therefore simultaneously be in two states (0 and 1), two qubits in four states (00, 01, 10 and 11) and four qubits in sixteen states. N qubits can therefore store 2N states simultaneously. A classical computer, in contrast, could only store one such state at a time.
In the same way that classical computers use logic gates to manipulate bits and perform operations, a quantum computer uses quantum gates to manipulate qubits. A quantum gate is a mathematical operation that changes the state of a qubit (or multiple qubits) in a controlled, reversible way. More importantly, in order to make a universal quantum computer that can do any kind of operation, much like classical computers today, one needs a universal quantum gate. This is a gate that can be combined to perform any possible quantum operation.
A new ‘wave’ of quantum hardware
Each quantum computing approach implements qubits (and gates) in different ways, be it in atoms, ions, ‘holes’ inside of diamonds or even more exotic quantum matter such as topological states in superconducting nanowires. It would be beyond the scope of this blog to address all of the different possibilities for implementing qubits. Instead, let’s look at a few examples:
- After years of research, Microsoft announced Majorana I — a step toward developing ultra-stable topological qubits built on Majorana zero modes (MZMs) [4]. These MZMs are exotic quasiparticles that emerge in specially engineered superconducting nanowires under precise conditions. What makes them particularly compelling is their topological nature: they obey non-Abelian statistics, meaning that quantum information is stored nonlocally — not in individual particles, but in the braiding patterns formed by moving MZMs around each other. This approach offers powerful intrinsic protection against local noise and decoherence, potentially paving the way for scalable, fault-tolerant quantum computing.
- AWS recently unveiled the Ocelot chip which encodes quantum information in superpositions of coherent photon states within a superconducting microwave cavity – so-called bosonic cat qubits (named in reference to Schrodinger’s feline thought experiment). The ‘cat states’ represent quantum information across macroscopic field configurations and can benefit from hardware-level error suppression.
- Google, IBM, and China’s Zuchongzhi team are leading the charge in developing quantum processors based on superconducting transmon qubits — a well-established platform prized for its relative scalability, high gate speeds, and compatibility with integrated circuit fabrication. All three are making steady advances toward fault-tolerant quantum computing, particularly by improving gate fidelities, qubit coherence times, and circuit depth. IBM’s Heron chip focuses on scalability and noise-aware circuit optimization, while Google’s Sycamore 2 enhances qubit coherence and two-qubit gate fidelities, pushing closer to fault tolerance. Meanwhile, researchers in China unveiled the Zuchongzhi-3 chip, a new 105-qubit quantum processor with which the team recently published a result claiming quantum advantage – the ability of a quantum computer to outperform a classical computer [5].
- Pasqal, a company co-founded by 2022 Nobel Prize winner Alain Aspect, uses Rubidium atoms cooled to micro Kelvin temperatures and trapped in an optical lattice to create and control qubits. The methods at Pasqal use laser light to bring the Rubidium atoms into an inflated state known as a Rydberg state. Whilst in this Rydberg state, the size of the atomic radius grows to the size of a small bacterium. This induces strong interactions between neighbouring qubits which is essential for implementing multi-qubit gates. Their approach is particularly promising for quantum simulation and machine learning problems and the company collaborates with industry partners in a variety of fields.
- D-Wave are unlike the previous examples in that they are not aiming for universal quantum computing. Instead D-wave have shifted their focus to a particular class of quantum computing referred to as quantum annealing. Quantum annealing leverages the quantum tools described before but uses them to solve a subclass of problems that can be expressed as finding the maximum (or minimum) of a quadratic equation. This focus has allowed D-Wave to develop chips with up to 5000 qubits. Using their quantum annealer, D-Wave recently published their own claim to quantum advantage.
When it comes to telling development in a certain field, who is registering what intellectual property is a good indicator of the level of progress. Similarly, in the field of quantum computing, patents are becoming the currency of competition. IBM is one of the most prolific patentees in the quantum space. At time of writing, IBM has filed more than 3000 patent applications related to quantum technology globally, with Google (1700 application globally) and Microsoft (1200 applications globally) on their tails. Other players are also growing their IP portfolio, D-Wave even trademarking “Advantage” in relation to their quantum system.
Small particles, big applications
The computational problems most likely to benefit from quantum computing are those that are massively parallel and require solving inherently quantum systems — and one particularly illustrative example lies in drug discovery.
Today, computational chemistry plays a vital role in early-stage pharmaceutical research. Techniques such as molecular dynamics (MD) and Monte Carlo simulations are routinely used to model the behaviour of molecular systems, predict properties of candidate compounds, and guide lead optimization. These simulations are built on a variety of theoretical frameworks — from ab initio (e.g. Hartree-Fock, coupled-cluster, DFT) to semi-empirical and coarse-grained models — each trading off between accuracy and computational tractability.
However, even the most approximate models struggle to scale efficiently. As molecular systems grow in size, the number of electronic interactions and degrees of freedom increases exponentially. When simulations aim to solve the many-body Schrödinger equation, the computational burden quickly becomes overwhelming for classical processors. This bottleneck limits our ability to simulate complex biomolecules or accurately predict reaction pathways.
This is where quantum computers promise a transformational leap. Because quantum systems naturally obey the same rules as the quantum chemistry they aim to simulate, they offer the tantalizing possibility of exponentially more efficient modelling. In principle, a fault-tolerant quantum processor with millions of qubits could simulate the full electronic structure of a drug-receptor interaction, enzyme mechanism, or conformational change without the approximations that classical methods require.
Such capabilities could dramatically accelerate lead identification, enable in silico screening of complex molecular libraries, and reduce the time and cost of drug development. Quantum algorithms like Quantum Phase Estimation (QPE) and Variational Quantum Eigensolver (VQE) have already shown promise on small model systems, and ongoing hardware advances are pushing us closer to chemical simulations that are hard classically but quantum-feasible. Many quantum computing companies are already announcing collaborations with partners in the healthcare sector. Both Pasqal and D-Wave, for example, have ongoing projects to utilise their technology to aid drug discovery.
In short, simulating quantum chemistry with quantum computers is not just elegant — it’s practical. And when scaled, it could redefine the way we discover and design drugs. One wonders whether, knowing this, Schrödinger would share the same pessimism as he once appeared to.
This article is for general information only. Its content is not a statement of the law on any subject and does not constitute advice. Please contact Reddie & Grose LLP for advice before taking any action in reliance on it.
[1] John Gribbin, In Search of Schrödinger’s Cat, Random House Publishing Group, (2011)
[2] Delle Donne, C., Iuliano, M., van der Vecht, B. et al. An operating system for executing applications on quantum network nodes. Nature 639, 321–328 (2025).
[3] Andrew D. King et al., Beyond-classical computation in quantum simulation. Science 388,199-204 (2025).
[4] Microsoft Azure Quantum., Aghaee, M., Alcaraz Ramirez, A. et al. Interferometric single-shot parity measurement in InAs–Al hybrid devices. Nature 638, 651–655 (2025).
[5] D. Gao et al. Establishing a New Benchmark in Quantum Computational Advantage with 105-wubit Zuchongzhi 3.0 Processor. Physical Review Letters 134, 090601 (2025).
