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Environment induced steady state quantum coherence (SSQC) is a captivating phenomenon that challenges conventional understandings of decoherence. In this letter, we delve into the foundational aspects of environment-induced SSQC, shedding light on its emergence within the framework of system-bath interactions. Starting from a microscopic system-bath coupled model, we investigate the dependence of SSQC on environmental memory effects, bath temperature, system-bath coupling strength, and squeezing parameters. Our findings reveal that the environment not only acts as a generator but also as a disruptor of SSQC. A peak will exist for a non-Markovian bath, which is a result of competition between these two mechanisms. Interestingly, the peak disappears in Markovian case. Additionally, we observe that the generated SSQC can be further amplified through environment squeezing., Comment: 8 pages, 3 figures

The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology. Researchers have increasingly leveraged novel computational techniques to streamline the drug development process to increase hit rates and reduce the costs associated with bringing a drug to market. To this end, we introduce a quantum-classical generative model that seamlessly integrates the computational power of quantum algorithms trained on a 16-qubit IBM quantum computer with the established reliability of classical methods for designing small molecules. Our hybrid generative model was applied to designing new KRAS inhibitors, a crucial target in cancer therapy. We synthesized 15 promising molecules during our investigation and subjected them to experimental testing to assess their ability to engage with the target. Notably, among these candidates, two molecules, ISM061-018-2 and ISM061-22, each featuring unique scaffolds, stood out by demonstrating effective engagement with KRAS. ISM061-018-2 was identified as a broad-spectrum KRAS inhibitor, exhibiting a binding affinity to KRAS-G12D at $1.4 \mu M$. Concurrently, ISM061-22 exhibited specific mutant selectivity, displaying heightened activity against KRAS G12R and Q61H mutants. To our knowledge, this work shows for the first time the use of a quantum-generative model to yield experimentally confirmed biological hits, showcasing the practical potential of quantum-assisted drug discovery to produce viable therapeutics. Moreover, our findings reveal that the efficacy of distribution learning correlates with the number of qubits utilized, underlining the scalability potential of quantum computing resources. Overall, we anticipate our results to be a stepping stone towards developing more advanced quantum generative models in drug discovery.

In this study, we conduct a study on magnetic hyperthermia treatment when a vessel is located near the tumor. The holistic framework is established to solve the process of tumor treatment. The interstitial tissue fluid, MNP distribution, temperature profile, and nanofluids are involved in the simulation. The study evaluates the cancer treatment efficacy by cumulative-equivalent-minutes-at-43 centigrade (CEM43), a widely accepted thermal dose. The influence of the nearby blood vessel is investigated, and parameter studies about the distance to the tumor, the width of the blood vessel, and the vessel direction are also conducted. After that, the effects of the fluid-structure interaction of moving vessel boundaries and blood rheology are discussed. The results demonstrate the cooling effect of a nearby blood vessel, and such effect reduces with the augment of the distance between the tumor and blood vessel. The combination of downward gravity and the cool effect from the lower horizontal vessel leads to the best performance with 97.77% ablation in the tumor and 0.87% injury in healthy tissue at distance d = 4 mm, but the cases of the vertical vessel are relatively poor. The vessel width and blood rheology affect the treatment by velocity gradient near the vessel wall. Additionally, the moving boundary almost has no impact on treatment efficacy. The simulation tool has been well-validated and the outcomes can provide a useful reference to magnetic hyperthermia treatment., Comment: arXiv admin note: substantial text overlap with arXiv:2308.12652

A holistic simulation framework is established on magnetic hyperthermia modeling to solve the treatment process of tumor, which is surrounded by a healthy tissue block. The interstitial tissue fluid, MNP distribution, temperature profile, and nanofluids are involved in the simulation. Study evaluates the cancer treatment efficacy by cumulative-equivalent-minutes-at-43 centigrade (CEM43), a widely accepted thermal dose coming from the cell death curve. Results are separated into the conditions of with or without gravity effect in the computational domain, where two baseline case are investigated and compared. An optimal treatment time 46.55 min happens in the baseline case without gravity, but the situation deteriorates with gravity effect where the time for totally killing tumor cells prolongs 36.11% and meanwhile causing 21.32% ablation in healthy tissue. For the cases without gravity, parameter study of Lewis number and Heat source number are conducted and the variation of optimal treatment time are both fitting to the inverse functions. For the case considering the gravity, parameters Buoyancy ratio and Darcy ratio are investigated and their influence on totally killing tumor cells and the injury on healthy tissue are matching with the parabolic functions. The results are beneficial to the prediction of various conditions, and provides useful guide to the magnetic hyperthermia treatment.

Quantum computers now show the promise of surpassing any possible classical machine. However, errors limit this ability and current machines do not have the ability to implement error correcting codes due to the limited number of qubits and limited control. Therefore, dynamical decoupling (DD) and encodings that limit noise with fewer qubits are more promising. For these reasons, we put forth a model of universal quantum computation that has many advantages over strategies that require a large overhead such as the standard quantum error correcting codes. First, we separate collective noise from individual noises on physical qubits and use a decoherence-free subspace (DFS) that uses just two qubits for its encoding to eliminate collective noise. Second, our bath model is very general as it uses a spin-boson type bath but without any Markovian assumption. Third, we are able to either use a steady global magnetic field or to devise a set of DD pulses that remove much of the remaining noise and commute with the logical operations on the encoded qubit. This allows removal of noise while implementing gate operations. Numerical support is given for this hybrid protection strategy which provides an efficient approach to deal with the decoherence problems in quantum computation and is experimentally viable for several current quantum computing systems. This is emphasized by a recent experiment on superconducting qubits which shows promise for increasing the number of gates that can be implemented reliably with some realistic parameter assumptions., Comment: 6 pages, 3 figures

Quantum state transfer (QST) through spin chains has been extensively investigated. Two schemes, the coupling set for perfect state transfer (PST) or adding a leakage elimination operator (LEO) Hamiltonian have been proposed to boost the transmission fidelity. However, these ideal schemes are only suitable for closed systems and will lose their effectiveness in open ones. In this work, we invoke a well explored optimization algorithm, Adam, to expand the applicable range of PST couplings and LEO to the open systems. Our results show that although the transmission fidelity decreases with increasing system-bath coupling strength, Markovianity and temperature for both ideal and optimized cases, the fidelities obtained by the optimized schemes always outweigh the ideal cases. The enhancement becomes more bigger for a stronger bath, indicating a stronger bath provides more space for the Adam to optimize. This method will be useful for the realization of high-fidelity information transfer in the presence of environment.

Control of the dynamics of an open quantum system is crucial in quantum information processing. Basically there are two ways: one is the control on the system and the other is tuning the bath parameters. In this paper, we use the latter to analyze the non-Markovian dynamics of the open system. The model is that the system is immersed in non-Markovian squeezed baths. For the dynamics, a non-Markovian master eqation is obtained using the quantum state diffusion (QSD) equation technique for the weak system-bath couplings. We use the adiabatic evolution or quantum state transmission as examples to analyze the effects of the bath parameters: non-Markovianity $\gamma$, the squeezed direction $\theta$ and squeezed strength $r$. For the adiabatic or state transmission fidelity, the calculation results show that they both can be enhanced by a smaller $\gamma$ or bigger $p$-quadrature. Interestingly, when $0<\theta<\pi/2$, the squeezed quadrature is determined by the combination of $r$ and $\theta$, and by numerical simulation we find that the fidelity peak occurs at $r=1-2\theta/\pi$. The fidelities increase with increasing $r$ when $r\in (0,1-2\theta/\pi]$. When $\theta\ge\pi/2$, lower fidelities are obtained due to the squeezed bath. Our results show that the dynamics of the open systems can be effectively controlled by reservoir enginerring., Comment: 9 pages, 7 figures

De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly-developing fields with lead candidate discovery in the drug development process. First, Artificial intelligence has already been demonstrated to successfully accelerate conventional drug design approaches. Second, quantum computing has demonstrated promising potential in different applications, such as quantum chemistry, combinatorial optimizations, and machine learning. This manuscript explores hybrid quantum-classical generative adversarial networks (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of a GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules. We also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of both generated molecule properties and the achieved KL divergence., Comment: 28 pages, 12 tables, 16 figures

Precise and efficient control of quantum systems is essential to perform quantum information processing tasks. In terms of adiabatic speedup via leakage elimination operator approach, for a closed system, the ideal pulse control conditions have been theoretically derived by P-Q partitioning technique. However, it is a challenge to design the corresponding control pulses for an open system, which requires to tackle noisy environments. In this paper, we apply the stochastic search procedures to an open qutrit system and successfully obtain the optimal control pulses for significant adiabatic speedup. The calculation results show that these optimal pulses allow us to acquire higher fidelities than the ideal pulses. The improvement of fidelity is large for relatively strong system-bath coupling strength and high bath temperature. For certain coupling strength and bath temperature, the maximal improvement can be achieved for a critical characteristic frequency which represents the memory time of the environment. Our investigation indicates that the stochastic search procedures are powerful tools to design control pulses for combating the detrimental effects of the environment., Comment: 8 pages, 5 figures

The compiling of quantum gates is crucial for the successful quantum algorithm implementations. The environmental noise as well as the bandwidth of control pulses pose a challenge to precise and fast qubit control, especially in a weakly anharmonic system. In this work, we propose an algorithm to approximately compile single-qubit gates with arbitrary accuracy. Evaluation results show that the overall rotation distance generated by our algorithm is significantly shorter than the commonly used $U3$ gate, then the gate time can be effectively shortened. The requisite number of pulses and the runtime of scheme design scale up as $\mathcal{O}[\mathrm{Log}(1/\epsilon)]$ with very small prefactors, indicating low overhead costs. Moreover, we explore the trade-off between effectiveness and cost, and find a balance point. In short, our work opens a new avenue for efficient quantum algorithm implementations with contemporary quantum technology., Comment: 5 pages, 2 figures

In present work an experiment has been designed to measure the Rayleigh ratio directly at $405$ nm and $432$ nm for linear alkylbenzene which is a common solvent used in liquid scintillator detectors of neutrino experiments. The perpendicularly polarized Rayleigh ratio is determined to be $(4.52\pm 0.28)\times 10^{-6}$ m$^{-1}\cdot$ sr$^{-1}$ at $405$ nm and $(3.82\pm 0.24)\times 10^{-6}$ m$^{-1}\cdot$ sr$^{-1}$ at $432$ nm, and the corresponding Rayleigh scattering length is $L_{Ray} = 22.9\pm 0.3(\mathrm{stat.})\pm 1.7(\mathrm{sys.})$ m at $405$ nm and $L_{Ray}= 27.0\pm 0.9(\mathrm{stat.})\pm 1.8(\mathrm{sys.})$ m at $432$ nm., Comment: 16 pages, 7 figures

A new Monte Carlo method has been implemented to describe the angular and polarization distributions of anisotropic liquids, like water and linear alkylbenzene, by considering orientational fluctuations of polarizability tensors. The scattered light of anisotropic liquids is depolarized with an angular distribution of $1+(1-\rho_v)/(1+3\rho_v)\cos^2\theta$, which is modified by the depolarization ratio $\rho_v$. A standalone experiment has validated the simulation results of LAB. The new method can provide more accurate knowledge on light propagation in large liquid detectors, which is beneficial to the developments of reconstruction for detectors., Comment: 13 pages, 5 figures

The AlphaFold computer program predicted protein structures for the whole human genome, which has been considered as a remarkable breakthrough both in artificial intelligence (AI) application and structural biology. Despite the varying confidence level, these predicted structures still could significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold in our end-to-end AI-powered drug discovery engines constituted of a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42, to identify a first-in-class hit molecule of a novel target without an experimental structure starting from target selection towards hit identification in a cost- and time-efficient manner. PandaOmics provided the targets of interest and Chemistry42 generated the molecules based on the AlphaFold predicted structure, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for CDK20 with a Kd value of 8.9 +/- 1.6 uM (n = 4) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, the second round of AI-powered compound generation was conducted and through which, a more potent hit molecule, ISM042-2 048, was discovered with a Kd value of 210.0 +/- 42.4 nM (n = 2), within 30 days and after synthesizing 6 compounds from the discovery of the first hit ISM042-2-001. To the best of our knowledge, this is the first reported small molecule targeting CDK20 and more importantly, this work is the first demonstration of AlphaFold application in the hit identification process in early drug discovery., Comment: 9 pages, 6 figures

Plasmonics is a rapid growing field, which has enabled both fundamental science and inventions of various quantum optoelectronic devices. An accurate and efficient method to calculate the optical response of metallic structures with feature size in the nanoscale plays an important role. Quantum hydrodynamic theory (QHT) provides an efficient description of the free-electron gas, where quantum effects of nonlocality and spill-out are taken into account. In this work, we introduce a general QHT that includes diffusion to account for the broadening, which is a key problem in practical applications of surface plasmon. We will introduce a density-dependent diffusion coefficient to give very accurate linewidth. It is a self-consistent method, in which both the ground and excited states are solved by using the same energy functional, with the kinetic energy described by the Thomas-Fermi and von Weizs\"{a}cker (vW) formalisms. In addition, our QHT method is stable by introduction of an electron density-dependent damping rate. For sodium nanosphere of various sizes, the plasmon energy and broadening by our QHT method are in excellent agreement with those by density functional theory and Kreibig formula. By applying our QHT method to sodium jellium nanorods, we clearly show that our method enables a parameter-free simulation, i.e. without resorting to any empirical parameter, such as size-dependent damping rate and diffusing coefficient. It is found that there exists a perfect linear relation between the resonance wavelength and aspect radio. The width decreases with increasing aspect ratio and height. The calculations show that our QHT method provides an explicit and unified way to account for size-dependent frequency shifts and broadening of arbitrarily shaped geometries. It is reliable and robust with great predicability, and hence provides a general and efficient platform to study plasmonics.