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Quantum Chemistry: Using Application-Specific Quantum Computers to Accelerate Pharmaceutical Drug Discovery

By Yu (Randy) Shee, Fabio Sanches

You can download the notebook here.

The outbreak of COVID-19 changed the way we live overnight — causing over 6 million deaths worldwide along with extraordinary economic, supply chain, and employment disruptions. Right after the disease became a global pandemic, pharmaceutical companies tackled this unprecedented public health emergency by developing drugs to target this virus, working in “wet labs” to determine the best mechanisms and dosages for these chemicals. A natural question is: How do scientists know which chemicals to test?

The almost innumerable number of naturally occurring molecules, in addition to those synthesized in a lab, means testing random molecules in wet labs is not an efficient way to find a functional drug. To save time and money scientists often resort to running computational analyses of chemicals in “dry labs.” (a.k.a. in silico).The first step is the generation of potential drug candidates and the validation of their effectiveness by simulation. Machine learning is often used to generate novel chemicals, while computational modeling of these potential drugs is required to pin down their electronic structures and dynamics.

Importantly, scientists are able to understand the properties and energies of molecules by solving for their electronic structures and dynamics. For example, when designing drugs that inhibit COVID-19, scientists can calculate if it is energetically favorable for the drug candidate to bind to the specific sites of vital proteins in the virus. Knowing the binding energies can help scientists understand if the drug is interfering with or blocking catalytic processes within the virus. Scientists can also calculate the stability of the drug to see if the chemical would work effectively in an aqueous environment.

However, these electronic structure problems are mathematically formulated as eigenvalue problems with high dimensions, which makes these calculations computationally difficult and expensive. This is where quantum computers come into play. As one might expect, quantum computers can provide an advantage in simulating quantum systems.

Quantum algorithms are a viable solution for large-scale pharmaceutical structure problems since quantum states of molecules can be efficiently encoded onto quantum bits (qubits) and qubit operations allow for certain states to be prepared efficiently. One example of a quantum algorithm that could be used in this setting is the Variational Quantum Eigensolver (VQE). VQE uses ansatz parametrized quantum circuits to describe quantum states — these are circuits that are built as a guess as to how to prepare the desired state and contain free parameters that are tuned to improve the guess. While there is no guarantee that VQE will efficiently compute ground state energies, its structure makes it runnable even on imperfect quantum computers. By variationally tuning the parameters of the quantum circuits, the ansatz can approximate the ground states (lowest eigenstates) of some given Hamiltonians (matrices). Here, we provide a notebook that demonstrates the implementation of the VQE algorithm and its application to obtaining molecular properties.

In this demonstration, the ansatz used consists of default gate sets. Qubit-efficient encoding (QEE) is constructed so that only physical states are mapped onto the qubit Hilbert space. This allows some added flexibility in the choice of gates used to construct the ansatz, along with the connectivity. It also means the molecule is mapped onto fewer qubits. By reducing qubit counts, we simultaneously reduce circuit depth, which is essential before the realization of fault-tolerant quantum computers.

This efficient encoding technique also allows us to take hardware constraints into account when designing the ansatz parametrized circuit. For example, we only need linear connectivity to achieve chemical accuracy in the examples shown in the notebook. This algorithmic and software layer design is an important aspect of Bleximo’s application-specific approach. Bleximo’s processor team can then in turn design efficient layouts and implement the gates natively appearing in these circuit.

Co-designing hardware and algorithms allows us to design hardware with better performance, and more quickly reach the point where quantum computers deliver value for chemical applications and similar complex simulations problems.

If you’re interested in learning more about our application-specific approach, and our algorithms, please reach out to us!


Bleximo is building full-stack, superconducting, application-specific quantum computers, co-designing algorithms and hardware to deliver first quantum advantage.

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Fabio Sanches

Fabio Sanches

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