We numerically study the distribution of entanglement between the Dutch cities of Delft and Eindhoven realized with a processing-node quantum repeater and determine minimal hardware requirements for verifiable blind quantum computation using color centers and trapped ions. Our results are obtained considering restrictions imposed by a real-world fiber grid and using detailed hardware-specific models. By comparing our results to those we would obtain in idealized settings, we show that simplifications lead to a distorted picture of hardware demands, particularly on memory coherence and photon collection. We develop general machinery suitable for studying arbitrary processing-node repeater chains using NetSquid, a discrete-event simulator for quantum networks. This enables us to include time-dependent noise models and simulate repeater protocols with cut-offs, including the required classical control communication. We find minimal hardware requirements by solving an optimization problem using genetic algorithms on a high-performance-computing cluster. Our work provides guidance for further experimental progress, and showcases limitations of studying quantum-repeater requirements in idealized situations.

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Quantum Inspire has taken important steps to enable quantum applications by developing a setting that allows the execution of hybrid algorithms. Currently, the setting uses a classical server (HPC node) co-located with the quantum computer for the high frequency coupling needed by hybrid algorithms. A fast task manager (dispatcher) has been developed to orchestrate the interaction between the server and the quantum computer. Although successful, the setting imposes a specific hybrid job-structure. This is most likely always going to be the case and we are currently discussing how to make sure this does not hamper the uptake of the setting. Furthermore, first steps have been taken towards the integration with the Dutch National High-Performance Computing (HPC) Center, hosted by SURF. As a first approach we have setup a setting consisting of two SLURM clusters, one in the HPC (C1) and the second (C2) co-located with Quantum Inspire API. Jobs are submitted from C1 to C2. Quantum Inspire can then schedule with C2 the jobs to the quantum computer. With this setting, we enable control from both SURF and Quantum Inspire on the jobs being executed. By using C1 for the jobs submission we remove the accounting burden from Quantum Inspire. By having C2 co-located with Quantum Inspire API, we make the setting more resilient towards network failures. This setting can be extended for other HPC centers to submit jobs to Quantum Inspire backends.

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Long-distance quantum communication via entanglement distribution is of great importance for the quantum internet. However, scaling up to such long distances has proved challenging due to the loss of photons, which grows exponentially with the distance covered. Quantum repeaters could in theory be used to extend the distances over which entanglement can be distributed, but in practice hardware quality is still lacking. Furthermore, it is generally not clear how an improvement in a certain repeater parameter, such as memory quality or attempt rate, impacts the overall network performance, rendering the path toward scalable quantum repeaters unclear. In this work we propose a methodology based on genetic algorithms and simulations of quantum repeater chains for optimization of entanglement generation and distribution. By applying it to simulations of several different repeater chains, including real-world fiber topology, we demonstrate that it can be used to answer questions such as what are the minimum viable quantum repeaters satisfying given network performance benchmarks. This methodology constitutes an invaluable tool for the development of a blueprint for a pan-European quantum internet. We have made our code, in the form of NetSquid simulations and the smart-stopos optimization tool, freely available for use either locally or on high-performance computing centers.

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A mathematical model is used to predict adsorption isotherms from experimentally measured breakthrough curves. Using this approach, by performing only breakthrough experiments for a mixture of two (or more) components, one can obtain pure component adsorption isotherms up to the pressure of the experiment. As a case study, the adsorption of an equimolar mixture of CO₂ and CH₄ in zeolite ITQ-29 is investigated. Pure component linear adsorption isotherms for CO₂ and CH₄ are predicted by fitting the theoretical breakthrough curves to the experimental ones. Henry coefficients obtained from our approach are in excellent agreement with those measured experimentally. A similar procedure is applied to predict the complete Langmuir adsorption isotherm from breakthrough curves at high pressures. The resulting adsorption isotherms are in very good agreement with those measured experimentally. In our model for transient adsorption, mass transfer from the gas phase to the adsorbed phase is considered using the Linear Driving Force model and dispersion of the gas phase in the packed bed is taken into account. IAST is used to compute the equilibrium loadings for a mixture of gases. The influence of the dispersion coefficient and the effective mass transfer coefficient on the shape of breakthrough curves is investigated and discussed. Rough estimations of these values are sufficient to predict adsorption isotherms from breakthrough curves.

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Chemical potentials of coexisting gas and liquid phases for water, methanol, hydrogen sulphide and carbon dioxide for the temperature range (Formula presented.) K to (Formula presented.) K are computed using two different methodologies: (1) Widom’s test particle insertion (WTPI) method in the conventional Gibbs Ensemble (GE), and (2) the Continuous Fractional Component Gibbs Ensemble Monte Carlo (CFCGE MC) method. It is shown that the WTPI method fails to accurately compute the chemical potentials of water and methanol in the liquid phase at low temperatures, while accurate chemical potentials in the liquid phase are computed using CFCGE MC method. For the CFCGE MC method, the statistical uncertainty for computed chemical potentials of water and methanol in the liquid phase are considerably smaller compared to the WTPI method. For the water models considered in this study (SPC, TIP3P-EW, TIP4P-EW, TIP5P-EW), computed excess chemical potentials based on three-site models are in better agreement with the chemical potentials computed from an empirical equation of state from the NIST database. For water, orientational biasing is applied during test particle insertion to check whether certain orientations of test particle are energetically unfavourable. A two-dimensional Overlapping Distribution Method (ODM) in the NVT ensemble is derived for this purpose. It is shown that failure of the WTPI method for systems with a strong hydrogen bonding network does not depend on orientation of the test molecule in that system. For all systems in this study, the WTPI method breaks down when the void fraction of the system drops below approximately 0.50.

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It is shown that ensemble averages computed in the Gibbs Ensemble with Continuous Fractional Component Monte Carlo (CFCMC GE) are different from those computed in the conventional Gibbs Ensemble (GE). However, it is possible to compute averages corresponding to the conventional GE while performing simulations in the CFCMC GE. In this way, one can benefit from the nice features of CFCMC GE (e.g. more efficient particle exchange) and at the same time compute the ensemble averages that correspond to the conventional GE. As a case study, the equilibrium pressure and densities of the systems of 256 and 512 LJ particles at different reduced temperatures ((Formula presented.)) are computed in the conventional GE and CFCMC GE. The validity of the expressions derived for computation of the thermodynamic pressure and densities corresponding to the conventional GE and computed in the CFCMC GE is examined numerically. The thermodynamic pressure in the conventional GE and CFCMC GE typically differs by at most 4%. It is shown that a very good estimate of the average pressure and densities corresponding to the conventional GE can be obtained by performing simulation in CFCMC GE and ignoring the contributions of the fractional molecule. It is also shown that the fractional molecule does not have an influence on the structure of the liquid, even for very small system sizes (e.g. 40 particles). The approach used here to compute the equilibrium pressure and densities of the conventional GE using the CFCMC GE can be easily extended to other thermodynamic properties and other ensembles.

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Natural gas, synthesis gas, and flue gas typically contain a large number of impurities (e.g., acidic gases), which should be removed to avoid environmental and technological problems, and to meet customer specifications. One approach is to use physical solvents to remove the acidic gases. If no experimental data are available, the solubility data required for designing the sweetening process can be obtained from molecular simulations. Here, Monte Carlo (MC) simulations are used to compute the solubility of the gas molecules, i.e., carbonyl sulfide, carbon disulfide, sulfur dioxide, hydrogen sulfide, methyl mercaptan, carbon dioxide, and methane in the commercial solvents tetraethylene-glycol-dimethyl-ether (Selexol), n-methyl-2-pyrrolidone, propylene carbonate, methanol (Rectisol), and the ionic liquid 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([bmim][Tf₂N]). Henry coefficients of the gases in the investigated solvents are obtained from the computed solubilities. The ratio of Henry coefficients is used to compute ideal selectivities of the solvents. The solubilites and selectivities computed from MC simulations are compared with available experimental data. Some guidelines are provided to remove acidic gases using the investigated solvents. Rectisol is the best solvent for acid gas removal, but it should be used at low temperatures. Selexol and the ionic liquid have similar selectivity of sulfur compounds with respect to methane and may be used at elevated pressures and temperatures since both have low vapor pressures. The solubility of carbon disulfide, sulfur dioxide, and methyl mercaptan in these solvents is the highest. Hence, these components can be removed easily prior to hydrogen sulfide, carbonyl sulfide, and carbon dioxide in a pre-absorber.

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Many important industrial separation processes based on adsorption operate close to saturation. In this regime, the underlying adsorption processes are mostly driven by entropic forces. At equilibrium, the entropy of adsorption is closely related to the enthalpy of adsorption. Thus, studying the behavior of the enthalpy of adsorption as a function of loading is fundamental in the understanding of the separation processes. Unfortunately, close to saturation, the enthalpy of adsorption is hard to measure experimentally and hard to compute in simulations. In simulations the enthalpy of adsorption is usually obtained from energy/particle fluctuations in the grand-canonical ensemble, but this methodology is hampered by vanishing insertions/deletions at high loading. To investigate the fundamental behavior of the enthalpy and entropy of adsorption at high loading, we develop a simplistic model of adsorption in a channel and show that at saturation the enthalpy of adsorption diverges to large positive values due to repulsive intermolecular interactions. However, there are many systems that can avoid repulsive intermolecular interactions and hence do not show this drastic increase in enthalpy of adsorption close to saturation. We find that the conventional grand-canonical Monte Carlo (GCMC) method is incapable of determining the enthalpy of adsorption from energy/particle fluctuations at high loading. Here, we show that by using the Continuous Fractional Component Monte Carlo (CFCMC), the enthalpy of adsorption close to saturation conditions can be reliably obtained from the energy/particle fluctuations in the grand-canonical ensemble. The best method to study properties at saturation is the NVT energy (local-) slope methodology. @en