a noiseless quantum circuit simulator, on the charge-transfer dataset confirmed that VQE-PDFT achieved results comparable to conventional MC-PDFT. Building upon this, we developed shallow-depth hardware-efficient ansatz circuits and integrated them into a QM/MM multiscale architecture to enable applications in complex biological systems. This extended framework, when applied to electron transfer in the European robin cryptochrome protein ErCRY4 with noiseless simulations, yielded transfer rates that aligned well with experimental measurements. Finally, as a proof-of-concept hardware demonstration, we executed reduced-density-matrix measurements for a single protein conformation on a 13-qubit superconducting device and showed the impact of noise through a comprehensive error analysis.
a noiseless quantum circuit simulator, on the charge-transfer dataset confirmed that VQE-PDFT achieved results comparable to conventional MC-PDFT. Building upon this, we developed shallow-depth hardware-efficient ansatz circuits and integrated them into a QM/MM multiscale architecture to enable applications in complex biological systems. This extended framework, when applied to electron transfer in the European robin cryptochrome protein ErCRY4 with noiseless simulations, yielded transfer rates that aligned well with experimental measurements. Finally, as a proof-of-concept hardware demonstration, we executed reduced-density-matrix measurements for a single protein conformation on a 13-qubit superconducting device and showed the impact of noise through a comprehensive error analysis.
• Published in Chemical Science of the Royal Society of Chemistry, successfully calculating the electron transfer process in the cryptochrome protein (ErCRY4) using quantum-classical hybrid computing. • Overcame the noise limits of current quantum hardware (NISQ) by introducing the VQE-PDFT method and a shallow-depth tailored quantum circuit (HEA). • Demonstrated that systematic errors caused by quantum noise are cancelled out when applying Marcus theory to calculate energy differences, presenting a practical algorithmic breakthrough. [Quantum Biology Society] Simulating strongly correlated electronic structure changes occurring inside complex biological proteins has been a daunting challenge, not only for conventional classical computers but also for current error-prone quantum computer (NISQ) devices. Recently, an innovative hybrid modeling technology combining only the strengths of quantum hardware and classical computers has been demonstrated, drawing attention from the academic community. A paper titled "Quantum-classical hybrid computation of electron transfer in a cryptochrome protein via VQE-PDFT and multiscale modeling" was published in the international journal Chemical Science, published by the Royal Society of Chemistry (RSC). A joint research team, including Dr. Jun-Han Huang of BGI Research and Professor Weitang Li of The Chinese University of Hong Kong, Shenzhen, presented a practical quantum algorithm for simulating biological electron transfer through this study. ■ The Meeting of Quantum and Classical, 'VQE-PDFT' Multiscale Architecture The research team targeted the electron transfer process between two tryptophan amino acids (TrpB, TrpC) within the cryptochrome (ErCRY4) of the European robin, known as the magnetic field-sensing protein of migratory birds. To compute this, they developed a hybrid framework called 'VQE-PDFT' (variational quantum eigensolver-pair-density functional theory). This approach is an efficient strategy that solves only the most complex multiconfigurational electronic correlations (static correlation) using the VQE algorithm on a quantum computer, while handling the remaining dynamic correlations with pair-density functional theory (PDFT) on a classical computer. Furthermore, they integrated multiscale QM/MM modeling, where the core region where the reaction occurs is calculated using quantum mechanics (QM), and the surrounding protein and solvent environment are treated with classical molecular mechanics (MM). ■ The Magic of Shallow-Depth Quantum Circuits and Error Cancellation To overcome the noise limitations of current quantum hardware, the researchers specially designed a shallow hardware-efficient ansatz (HEA) circuit with a depth of 4 to 6, optimized for the tryptophan structure, instead of the existing heavy quantum circuit (ROUCCSD) that reaches a depth of 35. In particular, the most groundbreaking discovery of this study emerged during the error analysis process. Although systematic errors occur in single-point energies calculated on noisy quantum devices, they demonstrated that these errors are dramatically cancelled out (error cancellation) during the process of calculating the energy difference between two states to obtain electron transfer parameters (reorganization energy and driving force) according to Marcus theory. ■ Successful Demonstration on a Superconducting Quantum Processor Thanks to this error cancellation effect, the electron transfer rate predictions obtained by the research team running the actual circuit on a 13-qubit superconducting quantum hardware matched very well with ideal noise-free simulator values and actual experimental ultrafast spectroscopy measurement results. This achievement is a practical milestone showing that the complex spin dynamics and electron transfer phenomena of biomolecules can be reliably calculated even with highly constrained quantum computers in the NISQ era. It is expected to provide a clear algorithmic breakthrough on how to utilize quantum technology in actual biological and chemical platforms, such as new drug discovery and massive enzyme complex analysis based on quantum computing in the future.
We analyzed the performance of quantum algorithms for studying protein-ligand systems, using both simulator and real hardware. To achieve this, we selected thrombin and 5 ligands as a test case and employed a decomposition strategy using density matrix embedded theory, dividing the ligand systems into three fragments each. First, the energy of one of the fragments was calculated using the Variational Quantum Eigensolver (VQE) using a state vector simulator, while the energy of the remaining fragments was calculated using Couple Cluster Singles and Doubles (CCSD), with the protein treated as point charges located at the respective atomic sites. Through these noiseless simulations, we evaluated the results under ideal conditions using the state-of-the-art Unitary CCSD ansatz to validate the efficacy of the strategy in a controlled setting without quantum noise. Subsequently, we evaluated the impact of approximations in the ansatz, optimizer, and active space size, which are necessary to decrease the computational cost, in order to target real hardware during the current Noisy Intermediate-Scale Quantum era. Finally, we performed the VQE calculations using a superconducting quantum computer developed by the RIKEN RQC-Fujitsu Collaboration Center and also analyzed the noise effect through simulations. The results demonstrated that the use of quantum algorithms can enhance the binding energy correlation value, offering potential applications in the workflow of computer-aided drug design.
Classical and current quantum approaches to protein structure prediction (QPSP) face limitations, notably massive qubit requirements restricting near-term models to simplistic on-lattice simulations. We propose a logarithmic-scale variational quantum eigensolver (VQE) that reduces qubit requirements for N torsional degrees of freedom to O(log2N), enabling off-lattice, all-atom simulations. Our architecture extracts molecular torsions from relative phases in statevector simulations. On quantum hardware, a decoder maps the empirical cumulative distribution function (CDF) from basis-state probabilities to bounded torsional variables. These feed a classical algorithm to build heavy-atom coordinates. We use an EfficientSU2 ansatz and multi-stage relaxation to mitigate barren plateaus. Structures are evaluated via a custom hybrid quantum-classical Hamiltonian, alongside Rosetta and OpenMM benchmarks.
Evaluation on chignolin and Trp-cage yielded native-like conformations. Chignolin reached a 0.623 Å Cα RMSD in retained snapshots and 1.199 Å in final models; Trp-cage achieved a 2.501 Å RMSD among snapshots (3.512 Å in final models). Execution on IBM processors (ibm_cleveland, ibm_miami) successfully recovered native-like structures with a best RMSD of 1.758 Å. The custom energy function performed best overall, though energy-ranking imbalances persisted across sampled landscapes for all functions.
This introduces the first all-atom, continuous-space quantum algorithm for QPSP. By converting physical qubit constraints into circuit depth constraints, it proves high-resolution prediction is feasible with exponentially fewer qubits. Despite current limits like computational overhead and energy function sensitivity, it establishes a scalable foundation for hybrid quantum biophysics.
The use of free energy perturbation (FEP) methods to study protein-ligand complexes is one of the most important tools in structure-based drug design. Because FEP methods typically rely on force fields, they may suffer from force field parameter-related issues. Herein, we present a quantum mechanics/molecular mechanics (QM/MM) hybrid method to overcome deficiencies in force-field models by using QM bookending approaches on both classical and quantum hardware. In the MM part of this QM/MM FEP method, AMBER is used to simulate the protein receptor and the unperturbed moiety of the ligand, with the ff19SB and GAFF2 force fields. In the QM part, QUICK was used to conduct Hartree-Fock (HF) calculations, followed by heat-bath configuration interaction (HCI) as a benchmark on classical devices. To enable the HCI function in QUICK, we developed a Python-based interface to execute HCI from IBM's qiskit-addon-dice-solver. Moreover, the same interface also enabled this work to execute QM/MM FEP calculations on quantum hardware using the Local Unitary Cluster Jastrow (LUCJ) ansatz, followed by sample-based diagonalization (SQD) and extended-SQD (extSQD) post-processing. Using a series of thermolysis inhibitors as an example, we find reasonable agreement with experiment between the classical HCI method and the LUCJ-SQD/extSQD method, with the latter yielding a result closer to the experimental value. The execution time between the HCI-based FEP method and the LUCJ-SQD/extSQD-based FEP method is also comparable, indicating a high potential for utility in the noisy intermediate-scale quantum (NISQ) era.
The Variational Quantum Eigensolver (VQE) is a promising quantum algorithm for applications in chemistry within the Noisy Intermediate-Scale Quantum (NISQ) era. The ability for a quantum computer to simulate electronic structures with high accuracy would have a profound impact on material and biochemical science with potential applications e.g., to the development of new drugs. However, considering the variety of quantum hardware architectures, it is still uncertain which hardware concept is most suited to execute the VQE for e.g., the simulation of molecules. Aspects to consider here are the required connectivity of the quantum circuit used, the size and the depth and thus the susceptibility to noise effects. Besides theo-retical considerations, empirical studies using available quantum hardware may help to clarify the question of which hardware technology might be better suited for a certain given application and algorithm. Going one step into this direction, within this work, we present results using the VQE for the simulation of the hydrogen molecule, comparing superconducting and ion trap quantum computers. The experiments are carried out with a standardized setup of ansatz and optimizer, selected to reduce the number of required iterations. The findings are analyzed considering different quantum processor types, calibration data as well as the depth and gate counts of the circuits required for the different hardware concepts after transpilation.
Predicting chemical reactions in biomolecules is challenging because reactive centers are embedded within complex environments that strongly influence their energetics and mechanisms. Here, we present a hybrid quantum-classical workflow that combines molecular dynamics, density functional theory, and correlated wavefunction methods, tightly integrating CPUs, GPUs, and quantum processing units (QPUs) to enable simulations across multiple length and accuracy scales. We apply this approach to ADGRG1 autoproteolysis, a maturation reaction in an adhesion G protein-coupled receptor that has not previously been studied using correlated electronic-structure methods. Our simulations treat a biologically realistic system containing more than one million atoms, including the full-length uncleaved receptor embedded in an endoplasmic-reticulum membrane, while incorporating a 54-qubit IQM quantum processor and up to 1,200 NVIDIA H100 GPUs. These results demonstrate how quantum computers can serve as accelerators within scalable scientific workflows, extending correlated electronic-structure methods to biologically relevant systems.