Digitized-Counterdiabatic Quantum Algorithm for Protein Folding

Chandarana, Pranav; Hegade, Narendra N.; Montalban, Iraitz; Solano, E.; Chen, Xi

doi:10.48550/arxiv.2212.13511

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Protein folding and design have gained much attention in recent years through both classical (Callaway, 2022;Lin et al, 2022) and quantum advances. For instance, lattice model-based systems were explored using variational quantum algorithms, including QAOA (Fingerhuth and Babej, 2018), VQE (Robert et al, 2021), and other variational techniques (Chandarana et al, 2022), as well as Grover's algorithm (Khatami, 2023). For the VQE adaptation (Robert et al, 2021), it was even shown that the number of physical qubits required scales only as the square of the number of amino acids (but without a convergence guarantee), putting structures with 100þ amino acids within reach as quantum hardware develops over the next years (Gambetta, 2022).…”

Section: Genomics and Clinical Researchmentioning

confidence: 99%

The state of quantum computing applications in health and medicine

Flöther

2023

Res. dir. Quantum technol.

View full text Add to dashboard Cite

Quantum computing hardware and software have made enormous strides over the last years1. Questions around quantum computing’s impact on research and society have changed from “if” to “when/how”. The 2020s have been described as the “quantum decade”, and the first production solutions that drive scientific and business value are expected to become available over the next years. Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-related activities and experiments in the last few years (although medicine and quantum theory have arguably been entangled ever since Schrödinger’s cat2). The initial focus was on biochemical and computational biology problems3,4,5,6,7,8; recently, however, clinical and medical quantum solutions have drawn increasing interest. The rapid emergence of quantum computing in health and medicine necessitates a mapping of the landscape. In this review, clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective. These consist of over 40 experimental and theoretical studies from the last few years. The use case areas span genomics, clinical research and discovery, diagnostics, and treatments and interventions. Quantum machine learning (QML) in particular has rapidly evolved and shown to be competitive with classical benchmarks in recent medical research. Near-term QML algorithms, for instance, quantum support vector classifiers and quantum neural networks, have been trained with diverse clinical and real-world data sets. This includes studies in generating new molecular entities as drug candidates, diagnosing based on medical image classification, predicting patient persistence, forecasting treatment effectiveness, and tailoring radiotherapy. The use cases and the applied algorithms are summarized. In addition, this review provides an outlook on medicine in the quantum era. There has been much discussion about healthcare’s journey towards precision medicine and the quadruple aim (better health, lower costs, enhanced patient experiences, and improved healthcare practitioner work lives)9. While a range of technical and ethical challenges remain, quantum computing is poised to become a key enabler for advancing towards the holy grail: keeping people healthy through proactive medical care and guidance at the level of an individual.

show abstract

Section: Genomics and Clinical Researchmentioning

confidence: 99%

The state of quantum computing applications in health and medicine

Flöther

2023

Res. dir. Quantum technol.

View full text Add to dashboard Cite

show abstract

“…However, it has been shown that DC-QAOA can reach solutions that are not accessible by QAOA for constant-depth circuits [34]. Additionally, it has been shown that CD driving can be used to produce ansatz with lower circuit depths by removing the Hamiltonian term U c (γ) under certain conditions [79]. A more detailed examination of the trade-offs between implementing CD terms in terms of circuit depths and solution enhancement would be of interest for further research, but for this study, we compare the performance with constant p layers.…”

mentioning

confidence: 99%

Meta-learning digitized-counterdiabatic quantum optimization

Chandarana

Vieites

Hegade

et al. 2023

Quantum Sci. Technol.

View full text Add to dashboard Cite

The use of variational quantum algorithms for optimization tasks has emerged as a crucial application for the current noisy intermediate-scale quantum computers. However, these algorithms face significant difficulties in finding suitable ansatz and appropriate initial parameters. In this paper, we employ meta-learning using recurrent neural networks to address these issues for the recently proposed digitized-counterdiabatic quantum approximate optimization algorithm. By combining meta-learning and counterdiabaticity, we find suitable variational parameters and reduce the number of optimization iterations required.We demonstrate the effectiveness of our approach by applying it to the MaxCut problem and the Sherrington-Kirkpatrick (SK) model. Our method offers a short-depth circuit ansatz with optimal initial parameters, thus improving the performance of the state-of-the-art QAOA.

show abstract

Digitized-Counterdiabatic Quantum Algorithm for Protein Folding

Cited by 2 publications

References 0 publications

The state of quantum computing applications in health and medicine

The state of quantum computing applications in health and medicine

Meta-learning digitized-counterdiabatic quantum optimization

Contact Info

Product

Resources

About