Quantum computing is evolving with practical applications in various fields, particularly in chemistry and logistics, although its advantages remain largely theoretical.
Quantum computing is increasingly being recognized for its potential applications in various industries, including chemistry, pharmacology, materials science, and logistics, rather than merely being viewed as a threat to current cryptographic systems due to algorithms like Shor's.
The final installment of the "Quantum & After" series delves into practical uses of quantum technologies and assesses where their commercial benefits are still theoretical.
Key Insights
Quantum computers excel in simulating quantum systems, particularly in fields like chemistry and pharmacology. By 2026, quantum processors were integrated into hybrid computations for protein-ligand complexes. While pilot projects in logistics are utilizing real data, the clear advantage of quantum computing remains unproven. Industries such as fintech and AI are still exploring their potential.
Significance
Quantum chemistry stands out as the most advanced application area. Accurate modeling of how ligand molecules bind to target proteins is crucial for evaluating drug candidates. Classical computers often struggle with the complex electronic structures of large proteins, leading to imprecise results. Quantum processors could enable faster and more accurate calculations, helping pharmaceutical companies identify unsuccessful drug candidates early in the process.
In May 2026, a collaboration between Cleveland Clinic, RIKEN, and IBM achieved a significant milestone by modeling the electronic structures of two large protein-ligand complexes, marking the largest quantum simulation of biologically relevant molecules. This effort utilized advanced quantum processors and supercomputers, completing over 9,200 quantum circuits in more than 100 hours.
Additionally, in March, IBM and Cleveland Clinic successfully modeled a mini-protein, achieving a 40-fold increase in scale and significantly improving accuracy. However, it's important to note that these results are preliminary and have not yet undergone peer review. The quantum aspect is part of a hybrid approach where classical computers handle simpler tasks while quantum processors tackle the most complex calculations.
On September 1, QC Ware and IonQ showcased a hybrid computation of cytochrome P450nor, a crucial enzyme, using a combination of quantum and classical methods. Although they demonstrated operational capabilities, these results did not showcase a quantum advantage over classical computing.
Logistics is another area where quantum methods are being explored. A July pilot project involving Telefónica and Würth combined quantum, classical, and AI techniques to enhance packaging efficiency, resulting in measurable improvements, although the quantum contribution's necessity remains uncertain.
Current Landscape
The evaluation of quantum computing projects is shifting from mere specifications to the specific problems they address, with a focus on the accuracy of energy calculations and other relevant metrics. Most quantum experiments still rely on hybrid systems, where classical computers do the majority of the work.
For quantum computing to claim a true advantage, it must outperform the best classical methods available.
Future Directions
The series began by questioning the profitability of quantum technologies. The conclusion is that the industry's future hinges on whether quantum computing can provide tangible benefits over existing methods, rather than just the number of qubits or ambitious announcements.
In the context of blockchain, the transition to quantum-resistant cryptography is critical, but timelines remain uncertain. Experts cannot predict when a quantum computer capable of breaking existing algorithms will emerge, making preemptive measures essential. The ability to shift to post-quantum standards in distributed networks will require years and consensus among participants.
The financial sustainability of quantum technologies will depend on their capacity to reduce discovery costs for catalysts, batteries, and molecules, allowing companies to fund these innovations through operational budgets instead of relying solely on R&D funding. The real milestone will be when quantum computation as a service is offered with a clear return on investment, marking a shift from theoretical discussions to practical applications.
