Synergizing quantum techniques with machine learning for advancing drug discovery challenge

Contest problem

In this subsection, we first provide a comprehensive background of the contest, setting the stage for a deeper understanding of its relevance and significance. Following this, we delve into the objectives of the contest, detailing the specific goals and aspirations that it aims to achieve. This part of the discussion will focus on the intended impact of the competition, the challenges it seeks to address in the field, and how it aligns with broader trends and needs in the industry. By establishing this context, we lay a foundational understanding that enriches the reader’s appreciation of the contest’s significance and the innovations it fosters.

The landscape of drug discovery has been dynamically evolving, powered persistently by state-of-the-art innovations^25,26. The key to these advancements is the quest to decipher complex molecular interactions that stand at the heart of transformative medical treatments²⁷. Within the plethora of tools that have risen to prominence, computational methodologies hold a distinctive place, acting as the linchpin in understanding and navigating the labyrinthine world of molecules. Particularly, machine learning, with its prowess in unearthing deep-seated patterns and refining solutions, has emerged as an indispensable asset^28,29. Yet, the real game-changer beckons at the confluence of quantum computing and machine learning³⁰. This connection, while nascent, is bursting with potential and promises to overhaul the very essence of drug discovery. Not only does it unlock doors to challenges once believed to be insurmountable for traditional computing paradigms, but it also augments our capacities in predicting and fathoming molecular behaviors with a so far unimagined precision.

A critical molecule in the pharmacological landscape is the hydroxyl cation (\(\cdot\)OH+)³¹. This cation is more than just a molecular entity; it’s a central axis for numerous drug interactions. Its high reactivity is linked to oxidative stress, resulting in a range of health conditions, from neurodegenerative disorders and cardiovascular diseases to cancer^31,32. Beyond its pathogenic roles, the hydroxyl cation is also integral to the efficacy of many drugs. With the development of quantum computing, we can potentially achieve a deeper understanding of the interactions and effects of molecules like the hydroxyl cation, accelerating breakthroughs in therapeutic developments^33,34. Understanding the quantum mechanics of the hydroxyl cation deeply impacts drug discovery. The key is determining its ground state energy, a foundational element for modeling complex drug interactions. The quantum domain, constrained by qubit numbers and error rates, poses challenges, particularly for larger molecules¹⁸. The hydroxyl cation, with its smaller size, fits within current quantum computing capabilities, making it an ideal focus for quantum simulations. Moreover, the hydroxyl cation is selected to ensure that the competition target presents sufficient difficulty and complexity, while allowing us to evaluate accuracy, resource efficiency, and innovation in a clear and consistent manner. We believe that innovations on the simpler hydroxyl molecule not only address immediate research needs but also lay the groundwork for future exploration of larger molecular structures, which are crucial for advancing drug development.

In the realm of quantum chemistry and drug discovery, the significance of accurately estimating the ground state energy of molecules like OH+ cannot be overstated. The ground state energy is a critical indicator of a molecule’s chemical properties, including its stability and potential reactivity. For drug discovery, understanding these properties at the quantum level allows for the design of molecules with desired therapeutic effects and minimal side effects. Particularly for complex molecules, traditional computational methods may fall short, making quantum computations a promising alternative. This accurate quantum mechanical characterization of molecular systems opens new avenues in rational drug design, potentially leading to more effective and targeted therapies.

This approach is pivotal for the evolution of drug discovery in the quantum age. The drug discovery pipeline is a multi-step process and each task is critical to developing new therapeutics efficiently and effectively. Quantum computing has the potential to enhance not only the ground state energy estimation but also other stages such as molecular design³⁵. Even under the limitations of current NISQ devices, hybrid classical-quantum models effectively leverage affordable classical computational resource and harness quantum advantages to tackle challenges with unprecedented computational power. This synergy between quantum and classical approaches hold the potential to advance the drug discovery pipeline in a practical and scalable way.

Contest objective

The ACM/IEEE Quantum Computing Challenge marked the first quantum computing themed contest held at a top international computer science conference. This event provided a platform for teams to showcase their research and advancements in the intersection of machine learning and quantum computing technologies. Looking ahead, the integration of quantum computing technologies and machine learning, including but not limited to applications in drug discovery, aims to enable quantum computing to aid classical machine learning or for classical machine learning to benefit quantum computing. It is important to note that the goal is not to replace classical computing and machine learning with their quantum counterparts. Instead, the focus is on designing hybrid frameworks that complement each other, addressing problems that were previously challenging or impossible for classical computing to solve independently.

The objective of the Quantum Computing for Drug Discovery Challenge (QCDDC’23) is to advance the application of quantum computing in the field of pharmaceutical research. Contestants are tasked with developing an innovative quantum algorithm capable of accurately calculating the ground state energy of the molecule, and the hydroxyl cation is selected in the contest as an example. This challenge not only focuses on achieving precision in quantum computations but also emphasizes the practical implementation of these algorithms, considering the noise models of real quantum computers. Successful algorithms should demonstrate efficiency in quantum resource usage, including optimizing the number of shots and circuit duration, essential for feasible quantum simulations in drug discovery. This challenge aims to bridge the gap between theoretical quantum computing and practical applications in medicinal chemistry, potentially revolutionizing the approach to pharmaceutical development.

Evaluation

In this subsection, we first introduce the designated platform and the noise model derived from an actual quantum system. Following that, the evaluation metrics used to assess the overall performance of each team’s design will be discussed. Additionally, the evaluation method, which allows teams considerable flexibility in designing their methods based on the platform, will also be presented.

Platform, system and noise model from real quantum processor backend

We acknowledge the use of IBM Quantum services in this competition. Qiskit⁸, primarily developed by IBM, is an open-source quantum computing framework. It offers tools for creating and manipulating quantum programs and running them on prototype quantum devices and simulators. Designed with modularity in mind, Qiskit provides components that span all aspects of quantum computing, from foundational elements to advanced quantum algorithms. Noise presents a significant challenge in the field of contemporary quantum computing^36,37,38. Therefore, we urge all participants to consider inherent noise when designing their quantum algorithms or circuits. Qiskit⁸offers tools for simulating quantum algorithms or circuits as they would be executed on actual quantum devices, including the associated noise. Since not everyone has access to real machines, for fairness, we require all participants to train their models on a given noise model, which will be released on the registration deadline date. Simulating a quantum system with an integrated noise model is crucial for understanding the potential performance of quantum algorithms on current NISQ devices^36,39. We recognize that while participants might be inclined to optimize basis gates at the pulse level to shorten the duration of the quantum circuit and conserve quantum resources⁴⁰, characterizing a time-dependent noise model from a real quantum machine poses unsolved challenges in preparation for this quantum computing drug discovery challenge. Regrettably, we must resort to using the standard gate-based noise model for evaluating circuits. The noise models from ibmq_cairo, ibmq_kolkata, and ibmq_montreal were integrated into the testing process, giving participants a genuine feel of practical hardware conditions.

Evaluation metrics

Accuracy of ground state energy estimation In quantum computing, accuracy in estimating the ground state energy of quantum systems is paramount. This is a key problem in quantum mechanics and quantum chemistry, with implications in material science, pharmaceuticals, and beyond. Accurate estimations are essential for understanding the properties and behaviors of complex quantum systems.

The precision of ground state energy estimation is measured using the following formula:

where \(E_\text estimated\) represents the average result from the participant’s framework across ten different computational seeds, and \(E_\text ideal\) is the objective value determined using classical computational methods.

This scoring mechanism allows participants to earn up to 100 points, with a higher score indicating greater accuracy. The score is a percentage that reflects the closeness of the estimated energy to the ideal value. This component challenges participants to optimize their algorithms and enhances understanding of the quantum systems being studied. It emphasizes the need for rigorous testing against classical benchmarks, promoting accuracy and reliability in quantum research.

Efficient utilization of quantum resources The competition also evaluates participants on their efficient use of quantum resources, which is divided into two critical aspects: the total number of quantum circuit shots and circuit size.

Total Number of Shots of Quantum Circuits: The total number of shots for quantum circuits is a pivotal metric for assessing resource efficiency. The maximum allowable number of shots is 3,786,000, correlating to the unoptimized problem Hamiltonian consisting of 631 Pauli strings, with a default setting of 6,000 shots per measurement prior to optimization. This constraint encourages participants to optimize their quantum measurements efficiently. The scoring for quantum circuit shots is as follows:

• Under 1,800,000 shots: Participants achieving this threshold will be awarded 25 points, incentivizing the reduction of quantum resources used.

• Between 1,800,000 and 3,786,000 shots: Points are allocated on a sliding scale, starting from 15 points for 3,786,000 shots and increasing up to 25 points for 1,800,000 shots, calculated using the formula:

  $$\beginaligned 15 + \frac(3,786,000 – n)(3,786,000 – 1,800,000) \times 10 \endaligned$$

  (5)

  where \(n\) is the total number of shots utilized. This formula encourages participants to minimize the number of shots used, rewarding efficiency with higher scores.

Circuit Size (Duration): Circuit size, or duration, is another vital metric, reflecting the complexity and efficiency of the quantum circuits developed. The competition ranks participants based on the compactness and execution speed of their circuits, with the top rank earning 15 points. Subsequent ranks are awarded points with a decreasing scale, highlighting the importance of optimizing circuit design for both performance and resource conservation.

Efficient resource utilization is vital not just technically, but also economically and ecologically, encouraging sustainable development in quantum computing technology.

Technical reflection and description In addition to the quantitative evaluation of participants’ submissions in the quantum computing competition, a significant emphasis is placed on a qualitative assessment. This assessment revolves around a comprehensive introspection and articulate presentation of the methodologies employed by the participants. It’s not just about what was achieved, but also about how it was achieved and the thought process behind it.

The qualitative evaluation is segmented into several key areas:

• Innovation and Pre-processing Techniques: Participants are expected to clearly describe any innovative approaches or pre-processing techniques they have implemented. This could include unique methods of optimizing quantum circuits, novel approaches to problem decomposition, or creative algorithms that enhance the efficiency of the computation. The novelty and effectiveness of these techniques are of prime interest.

• Utilization of Classical Resources: Alongside quantum resource optimization, how participants have leveraged classical computing resources forms a critical part of their technical strategy. This includes, but is not limited to, the use of classical algorithms for preprocessing, data analysis, or hybrid quantum-classical computation approaches. A well-thought-out balance between quantum and classical resources can be indicative of a more sophisticated and practical approach to problem-solving in the realm of quantum computing.

• Technical Novelty: The degree of technical innovation will be a key factor. This includes the development of new methods or the novel application of existing techniques to the challenges presented in the competition. Originality and creativity in problem-solving are highly valued.

• Logical Coherence: The clarity and logic of the technical narrative are paramount. Participants should strive to present their methodologies and thought processes in a manner that is both coherent and comprehensible. This includes a clear articulation of the steps taken, logical progression of ideas, and a well-structured presentation of their work.

This reflective and descriptive component is allocated a total of 10 points. The allocation of these points will be judiciously carried out by a panel of three expert graders. The graders will evaluate submissions based on the depth of self-reflection, technical novelty, logical coherence, and other salient factors. The aim is to reward not only technical proficiency but also innovation, thoughtful analysis, and clarity of expression. This aspect of the evaluation underscores the significance of not only achieving results but also understanding the journey to those results. It encourages participants to think critically about their methodologies, fostering a culture of continuous learning and improvement.

Evaluation method

We delineate a comprehensive framework designed to ensure participants effectively navigate the complexities of quantum computing, particularly focusing on the integration of noise models and system models reflective of real-world quantum systems. To facilitate a standardized yet flexible approach in the competition, the following components are detailed:

Upon commencement, participants are provided with a detailed noise model and system model, meticulously crafted to mimic the intricacies of a real quantum environment. These models serve as the foundation upon which teams will build and evaluate their quantum computing solutions. A crucial requirement for participants is adherence to the topology map of FakeMontreal. This topology, representative of a quantum computing architecture, sets the stage for realistic simulation and testing of quantum algorithms, ensuring that designs are not only innovative but also applicable within the constraints of actual quantum hardware. To accommodate a diverse range of strategies and to reflect the variability in real quantum systems, we offer participants the choice among three distinct noise models extracted from FakeCairo, FakeMontreal, and FakeKolkata. Each model presents unique challenges and characteristics, allowing teams to tailor their approaches based on the specific nuances of these simulated environments. In the spirit of promoting robustness and adaptability in algorithmic design, we recommend participants utilize specific seeds for algorithmic processes, transpiling, and measurement within Qiskit. This guidance is aimed at fostering consistency and fairness in the evaluation process. The final scoring will be derived from the average outcomes across 10 distinct seeds: five from a provided list of seeds and the other five from a concealed set of test seeds. This methodology ensures that assessments are comprehensive, accounting for variability and promoting strategies that are resilient across a spectrum of conditions.

The overarching objective of this evaluation method is to challenge teams to devise quantum computing solutions that are not only theoretically sound but also practically viable within the constraints of current quantum technology. By navigating the specified topology, choosing among the provided noise models, and adhering to a structured approach for seed selection and result averaging, participants demonstrate their capacity to develop strategies that balance innovation with practical applicability. This detailed approach to evaluation underscores the competition’s commitment to advancing the field of quantum computing by fostering an environment where theoretical knowledge and practical skills converge, ultimately leading to the development of solutions that push the boundaries of what is currently achievable in quantum computing.

(a) Illustrates a molecular model of the OH+ ion, showcasing its atomic structure and bonding configuration. (b) Presents an example of a Variational Quantum Eigensolver (VQE) circuit, demonstrating the quantum computational approach to solving molecular energy states.

Dataset

The data provided for this challenge revolves around the central theme of drug discovery, focusing on the fundamental quantum mechanics of the hydroxyl cation (\(\cdot\)OH), as shown in Fig. 5. As a pivotal entity in numerous drug interactions and physiological processes, an intricate understanding of the hydroxyl cation is crucial. To aid participants in this endeavor, we’ve curated a specialized dataset tailored to encapsulate the hydroxyl cation’s Hamiltonian.

The process for obtaining the Hamiltonian begins with a high-precision quantum chemical calculation of the molecule’s electronic structure. Sophisticated computational methods, such as Hartree-Fock or more advanced post-Hartree-Fock techniques, are utilized. These methods are crucial for providing an accurate and detailed representation of the electronic orbitals. In the Hartree-Fock method, the many-electron wave function of the molecule is approximated by a single Slater determinant, which simplifies the complex many-body problem. However, to capture electron correlation effects more accurately, post-Hartree-Fock methods such as Configuration Interaction (CI), Møller-Plesset perturbation theory (MP2), or Coupled Cluster Theory are often employed. These approaches involve sophisticated calculations to model the interactions and correlations between electrons, providing a more comprehensive understanding of the electronic structure. Once the electronic structure is accurately determined, the electron orbitals are then mathematically represented using fermionic operators. The transformation from fermionic operators to Pauli strings is achieved through techniques like the Jordan-Wigner or Bravyi-Kitaev transformations, which map the fermionic algebra onto the algebra of Pauli matrices. This mapping is essential for quantum simulations, as quantum computers natively understand operations in terms of Pauli matrices.