Energy Cost of Quantum Circuit Optimisation: Predicting That Optimising Shor’s Algorithm Circuit Uses 1 GWh

Quantum circuits are difficult to simulate, and their automated optimisation is complex as well. Significant optimisations have been achieved manually (pen and paper) and not by software. This is the first in-depth study on the cost of compiling and optimising large-scale quantum circuits with state-of-the-art quantum software. We propose a hierarchy of cost metrics covering the quantum software stack and use energy as the long-term cost of operating hardware. We are going to quantify optimisation costs by estimating the energy consumed by a CPU doing the quantum circuit optimisation. We use QUANTIFY, a tool based on Google Cirq, to optimise bucket brigade QRAM and multiplication circuits having between 32 and 8,192 qubits. Although our classical optimisation methods have polynomial complexity, we observe that their energy cost grows extremely fast with the number of qubits. We profile the methods and software and provide evidence that there are high constant costs associated to the operations performed during optimisation. The costs are the result of dynamically typed programming languages and the generic data structures used in the background. We conclude that state-of-the-art quantum software frameworks have to massively improve their scalability to be practical for large circuits.

Double-Shift: A Low-Power DNN Weights Storage and Access Framework based on Approximate Decomposition and Quantization

One major challenge in deploying Deep Neural Network (DNN) in resource-constrained applications, such as edge nodes, mobile embedded systems, and IoT devices, is its high energy cost. The emerging approximate computing methodology can effectively reduce the energy consumption during the computing process in DNN. However, a recent study shows that the weight storage and access operations can dominate DNN's energy consumption due to the fact that the huge size of DNN weights must be stored in the high-energy-cost DRAM. In this paper, we propose Double-Shift, a low-power DNN weight storage and access framework, to solve this problem. Enabled by approximate decomposition and quantization, Double-Shift can reduce the data size of the weights effectively. By designing a novel weight storage allocation strategy, Double-Shift can boost the energy efficiency by trading the energy consuming weight storage and access operations for low-energy-cost computations. Our experimental results show that Double-Shift can reduce DNN weights to 3.96%–6.38% of the original size and achieve an energy saving of 86.47%–93.62%, while introducing a DNN classification error within 2%.

Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

In-situ quantification of NO synthesis in a warm air glow discharge by WMS-based Mid-IR QCL absorption spectroscopy

Nitric oxide (NO) is one of the most crucial products in the plasma-based nitrogen fixation process. In this work, in-situ measurements were performed for quantifying the NO synthesis spatially in a warm air glow discharge, through the method of Mid-infrared quantum cascade laser absorption spectroscopy (QCL-AS). Two ro-vibrational transitions at 1900.076 cm-1 and 1900.517 cm-1 of the ground-state NO(X) were probed sensitively by the help of the wavelength modulation spectroscopy (WMS) approach to increase the signal/noise (S/N) level. The results show a decline trend of NO synthesis rate along the discharge channel from the cathode to the anode. However, from the point of energy efficiency, the cathode region is of significantly low energy efficiency of NO production. Severe disproportionality was found for the high energy consumption but low NO production in the region of cathode area, compared to that in the positive column zone. Further analysis demonstrates the high energy cost of NO production in the cathode region, is ascribed to the extremely high reduced electric field E/N therein not selectively preferable for the processes of vibrational excitation or dissociation of N2 and O2 molecules. This drags down the overall energy efficiency of NO synthesis by this typical warm air glow discharge, particularly for the ones with short electrode gaps. Limitations of further improving the energy cost of NO synthesis by variations of the discharge operation conditions, such as discharge current or airflow rate, imply other effective manners able to tune the energy delivery selectively to the NO formation process, are sorely needed.

Theoretical model of efficient phagocytosis driven by curved membrane proteins and active cytoskeleton forces

Phagocytosis is the process of engulfment and internalization of comparatively large particles by the cell, that plays a central role in the functioning of our immune system. We study the process of phagocytosis by considering a simplified coarse grained model of a three-dimensional vesicle, having uniform adhesion interaction with a rigid particle, in the presence of curved membrane proteins and active cytoskeletal forces. Complete engulfment is achieved when the bending energy cost of the vesicle is balanced by the gain in the adhesion energy. The presence of curved (convex) proteins reduces the bending energy cost by self-organizing with higher density at the highly curved leading edge of the engulfing membrane, which forms the circular rim of the phagocytic cup that wraps around the particle. This allows the engulfment to occur at much smaller adhesion strength. When the curved proteins exert outwards protrusive forces, representing actin polymerization, at the leading edge, we find that engulfment is achieved more quickly and at lower protein density. We consider spherical as well as non-spherical particles, and find that non-spherical particles are more difficult to engulf in comparison to the spherical particles of the same surface area. For non-spherical particles, the engulfment time crucially depends upon the initial orientation of the particles with respect to the vesicle. Our model offers a mechanism for the spontaneous self-organization of the actin cytoskeleton at the phagocytic cup, in good agreement with recent high-resolution experimental observations.

Prediction and Optimization of the Cost of Energy Resources in Greek Public Hospitals

The continuous operation and the specialized conditions needed for safely delivering healthcare services make hospitals among the most expensive buildings. Several studies in different countries have investigated the potential role and contribution of macroscopic indices of hospitals in total energy requirements. In this work, we tried to investigate the energy requirements of Greek hospitals in terms of cost. We collected data from all public hospitals in Greece over a 2 year period (2018–2019) and evaluated the contribution of various factors in the total energy cost. The data revealed large variability by region and by hospital, even regarding structures of the same category and size. The analysis also showed that structural and operational data of each hospital differently influence the hospitals’ energy requirements. Using regression methods, we developed two models for calculating annual energy costs. One only contains hospital structural data (number of beds, type of hospital, number of employees, and the non/use of alternative energy sources such as natural gas), and it reached an R² of 0.84. The second model contains not only structural but also operational data from each hospital (number of the internal patients, number of surgeries and number of medical imaging tests), and it reached an R² of 0.87. The former model is easier to compute since it only relies on data that can be easily gathered, but the latter has slightly better performance. These tools can help the Ministry of Health and hospitals’ management to identify the factors that contribute to the energy cost in order to plan targeted interventions, be well-prepared regarding budgeting, and be able to progressively measure, monitor, and improve the environmental footprint of hospitals by investing in renewable energy resources.