The Daily Qubit

🧬 Hybrid-quantum-classical framework as a solution to the "protein folding problem", plus the first community college with a quantum computing bootcamp

Author

Cierra Choucair
May 06, 2024 • Estimated Reading Time: 11 minutes

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IN TODAY’S ISSUE:

  • ☝️ New format — tags to organize content

  • Quantum computing for protein structure prediction as demonstrated by framework that accurately predicts Zika Virus NS3 Helicase

  • QAOA gets a makeover — QIRO bypasses locality issues, matches classical heuristic performance

  • New Mexico Community College to offer first of its kind quantum computing bootcamp amidst concerns for related US programs

  • Plus, IBM releases execution modes, QTunisia hosting certificate-awarding workshop for all levels, and MIT technique for atom-proximity has big implications for quantum.

BRIEF BYTES

NEWS FOR THOSE IN A HURRY

TOP HEADLINES IN NEWS & RESEARCH

NEWS

Tags: EDUCATION

NEW MEXICO ROLE MODEL FOR QUANTUM WORKFORCE DEVELOPMENT

Central New Mexico Community College, the largest in New Mexico in terms of undergraduate enrollment, has announced plans to build a quantum science lab and develop a training boot camp to meet the rising workforce needs in quantum computing.

The 10-week program is supported by $862,000 in federal funding and partnerships with Sandia National Laboratories and the University of New Mexico. The bootcamp will offer hands-on training on quantum system operations and maintenance. Designed to help students transition into the quantum computing workforce, the first cohort will begin Spring 2025.

This is amidst recent insights from a research article highlighting disparities in access to quantum computing education across the US. The study revealed that many underserved groups (including those that come from low-income households and rural areas) have limited opportunities in this field.

CNM's new lab and training program is a big step towards bridging these gaps. By providing accessible and practical training in quantum science, this initiative could serve as a national precedent.

RESEARCH

Tags: CHEMISTRY APPLICATION

OVERVIEW OF A PERSPECTIVE ON PROTEIN STRUCTURE PREDICTION USING QUANTUM COMPUTERS

The Brief Byte: Protein structure prediction, difficult with classical computation due to complexity, is important for applications within biology and medicine such as drug discovery and understanding biological processes. Quantum computation stands out for its potential to tackle complexity. Researchers present a framework for determining which protein structure prediction problems are most ready for quantum computation and validate their work using a catalytic loop of Zika Virus NS3 Helicase.

Breakdown:

  • Understanding protein folding and structure is relevant to biological processes as well as disease. The Protein Data Bank's repository, an archive for 3D structures of biological molecules, now contains over 200,000 structures, but there are around 300 million known protein sequences. Exploration of the “protein-folding problem” has traditionally been done in expensive and time-consuming laboratory experiments. More recently, machine learning has been used as a less-resource-extensive approach, but the nuance associated with the underlying physics of protein folding is lacking. Quantum computers are more predisposed to handle thermodynamic and mathematical frameworks. As with all potential quantum computing solutions, its important to recognize which problems related to the “protein-folding problem” are most predisposed to quantum advantage.

  • The researchers, recognizing that a hybrid quantum-classical approach is the most reasonable way to integrate quantum computation in protein structure prediction, design a scientific workflow that consists of both aspects. The quantum algorithm is used to build the protein sequence and sample the energies of potential conformers. This is followed up by classical methods to refine the solution.

  • The structure presented for the sake of validation was the “P-loop” of the Zika virus NS3 helicase protein. The comparison included: PEP-FOLD3 (a classical PSP algorithm), a quantum algorithm solved by VQE, the quantum algorithm's Ising Hamiltonian solved classically by brute force and Gurobi, and AlphaFold2 (AI-based PSP tool).

  • The results showed that PEP-FOLD3 had the best performance. However, the quantum algorithm outperformed brute force classical methods and in terms of the radius of gyration, closely matched with the experimental structure. AlphaFold2 had the least accurate model. Ultimately, the quantum-classical hybrid approach allowed the researchers to accurately predict the structure of the Zika virus NS3 helicase P-loop in a scalable way.

Source: Cheng, J.; Novati, G.; Pan, J.; Bycroft, C.; Žemgulytedot, A.; Applebaum, T.; Pritzel, A.; Wong, L. H.; Zielinski, M.; Sargeant, T. et al. A Perspective on Protein Structure Prediction Using Quantum Computers. Science. (2024). https://doi.org/10.1126/science.adg7492

RESEARCH

Tags: ALGORITHMS

OVERVIEW OF QUANTUM-INFORMED RECURSIVE OPTIMIZATION ALGORITHMS

The Brief Byte: Researchers propose a set of quantum-informed recursive optimization algorithms for combinatorial optimization that use quantum resources to gather problem-specific information that guides classical reduction steps and in turn recursively simplifying the optimization problem. This approach overcomes the limitations of locality in the quantum approximation optimization algorithm. The algorithms are demonstrated on a neutral atom quantum processor and shown to perform comparably to classical heuristics on current-state devices.

Breakdown:

  • Hybrid algorithms offer the most promise for quantum technologies using NISQ devices and QAOA in particular is useful for combinatorial optimization. However, its reliance on locality (only qubits separated by distance threshold are able to communicate) tends to limit its effectiveness. Nonlocality would require more extensive hardware resources, which is ideal to limit in order to see quantum advantage current-state. Researchers have taken inspiration from RQAOA, recursive QAOA, to propose QIRO.

  • QIRO uses quantum resources to generate information that reduces optimization problems via classical subroutines that are tailored to specific problems. The algorithm also includes backtracking, which is a mechanism used to identify and correct errors made in earlier stages. QIRO implementations were provided for two NP-hard combinatorial optimization problems: maximum independent set and maximum satisfiability. Experimentally, QIRO was used to solve the maximum independent set on a QuEra Aquila analog processor via Braket.

  • The results show that although RQAOA generally outperforms QIRO on the maximum independent set problem due to QIRO's locality-focused update rules, improved quantum correlations from neutral atom devices greatly improve QIRO's performance. Backtracking has shown promising results, especially for the MAX-2-SAT problem. Ultimately, the intention is that QIRO is shown as a promising, proof-of-concept hybrid quantum-classical algorithm with potential for wider applications.

Source: Jernej Rudi and Kerschbaumer, Aron and Schuetz, Martin J.A. and Mendl, Christian B. and Katzgraber, Helmut G. et al. Quantum-Informed Recursive Optimization Algorithms. PRX Quantum. (2024). https://doi.org/10.1103/PRXQuantum.5.020327

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