Quantum computing cuts 1,000 years off drug simulations

On November 15, UK-based quantum engineering company Riverlane announced [1] that new research outlines how advances in quantum algorithms have radically reduced the amount of resources researchers need to obtain useful results: the estimated resources required to run these calculations in an active space of 50 orbitals and electrons have been reduced from more than 1,000 years to just a few days.

The results of the study were published in the Journal of Chemical Theory and Computation under the title "Analysis on the state of the art of quantum computing in drug discovery applications" [2].

01A series of experiments: estimation of the resources required for quantum computing in pharmaceutical applications

This study briefly summarizes and compares the scaling properties of state-of-the-art quantum algorithms and provides new estimates of the cost of quantum computation for simulating progressively larger embedding regions of pharmacologically relevant covalent protein-drug complexes involving the drug ibrutinib; performing these computations requires Riverlane's error-correcting quantum architecture.

After discussing and comparing two leading quantum algorithms used to find the energy of the electronic Hamiltonian ground state: the Variational Quantum Elementary Solver (VQE) and Quantum Phase Estimation (QPE), the team concluded that QPE scales better. Therefore, the remaining comparative work was focused on this algorithm.

Circuit Outline of QPE

Top: Illustration of the experimental abstraction; Bottom left: Cluster containing some of the ibrutinib inhibitors, with various fragments of the selected active spatial orbitals represented using various colors; Bottom right: Comparison of resources (runtime and total number of physical quantum bits) using the two QPE algorithms, with the sparse quantum bitization approach effectively reducing computational costs.

After detailed costing of resource estimates for quantum computing in a range of pharmaceutical applications, quantum error correction (QEC) in QPE applications, the results show that algorithm improvements can reduce the cost of quantum computing by several orders of magnitude and can transform the potential capabilities of quantum computers.

02Resource savings are only expected on error correction architectures

This demonstrates the potential for quantum to transform the pharmaceutical industry. For example, quanta could help screen the number of drug candidates for a particular treatment by evaluating which ones might be the most promising. The entire process from drug candidate to product can take up to 10 years, and shortening this timeline has the potential to reduce costs by billions of dollars.

Crucially, these resource savings are only possible with error-correcting architectures. While future quantum computers will be able to accurately model nature at the molecular scale, today's quantum computers struggle with high error rates. Error correction is a major challenge in quantum computing, which is why Riverlane is assembling the world's best error correction team to tackle the entire quantum computing stack.

Commenting on this achievement, Riverlane CEO Steve Brierley said, "The latest research from Riverlane's world-class science team shows that quantum has the power to transform the industries that billions of people around the world rely on every day. However, if we can solve the fundamental challenges of error correction, we will only realize these benefits, whether in drug discovery, materials or climate."

Reference links:

[1]https://www.riverlane.com/press-release/riverlane-research-shows-potential-to-cut-quantum-drug-simulation-time-from-over-1-000-years-to-a-few-days

[2]https://pubs.acs.org/doi/10.1021/acs.jctc.2c00574