Quantum tangent kernel

Quantum kernel method is one of the key approaches to quantum machine learning, which has the advantages that it does not require optimization and has theoretical simplicity. By virtue of these properties, several experimental demonstrations and discussions of the potential advantages have been developed so far. However, as is the case in classical machine learning, not all quantum machine learning models could be regarded as kernel methods. In this work, we explore a quantum machine learning model with a deep parameterized quantum circuit and aim to go beyond the conventional quantum kernel method. In this case, the representation power and performance are expected to be enhanced, while the training process might be a bottleneck because of the barren plateaus issue. However, we find that parameters of a deep enough quantum circuit do not move much from its initial values during training, allowing first-order expansion with respect to the parameters. This behavior is similar to the neural tangent kernel in the classical literatures, and such a deep variational quantum machine learning can be described by another emergent kernel, quantum tangent kernel. Numerical simulations show that the proposed quantum tangent kernel outperforms the conventional quantum kernel method for an ansatz-generated dataset. This work provides a new direction beyond the conventional quantum kernel method and explores potential power of quantum machine learning with deep parameterized quantum circuits.Comment: 7 pages, 4 figure

Development Perspective of Bioelectrocatalysis-Based Biosensors

Bioelectrocatalysis provides the intrinsic catalytic functions of redox enzymes to nonspecific electrode reactions and is the most important and basic concept for electrochemical biosensors. This review starts by describing fundamental characteristics of bioelectrocatalytic reactions in mediated and direct electron transfer types from a theoretical viewpoint and summarizes amperometric biosensors based on multi-enzymatic cascades and for multianalyte detection. The review also introduces prospective aspects of two new concepts of biosensors: mass-transfer-controlled (pseudo)steady-state amperometry at microelectrodes with enhanced enzymatic activity without calibration curves and potentiometric coulometry at enzyme/mediator-immobilized biosensors for absolute determination

Flocculation phenomenon of a mutant flocculent Saccharomyces cerevisiae strain: Effects of metal ions, sugars, temperature, pH, protein-denaturants and enzyme treatments

The flocculation mechanism of a stable mutant flocculent yeast strainSaccharomyces cerevisiae KRM-1 was quantitatively investigated for potential industrial interest. It was found that the mutant flocculent strain was NewFlo phenotype by means of sugar inhibition test. The flocculation was completely inhibited by treatment with proteinase K, protein-denaturants and carbohydrate modifier. The absence of calcium ions significantly inhibited the flocculation, indicating that Ca2+ was specifically required for flocculation. The flocculation was stable when temperature below 70°C and pH was in the range of 3.0 - 6.0. The flocculation onset of the mutant flocculent strain was in the early stationary growth phase, which coincided with glucose depletion in the batch fermentation for the production of ethanol from kitchen refuse medium. The results are expected to help develop better strategies for the control of mutant flocculent yeast for future large-scale industrial ethanol fermentation

Direct electron transfer-type bioelectrocatalysis of redox enzymes at nanostructured electrodes

Direct electron transfer (DET)-type bioelectrocatalysis, which couples the electrode reactions and catalytic functions of redox enzymes without any redox mediator, is one of the most intriguing subjects that has been studied over the past few decades in the field of bioelectrochemistry. In order to realize the DET-type bioelectrocatalysis and improve the performance, nanostructures of the electrode surface have to be carefully tuned for each enzyme. In addition, enzymes can also be tuned by the protein engineering approach for the DET-type reaction. This review summarizes the recent progresses in this field of the research while considering the importance of nanostructure of electrodes as well as redox enzymes. This review also describes the basic concepts and theoretical aspects of DET-type bioelectrocatalysis, the significance of nanostructures as scaffolds for DET-type reactions, protein engineering approaches for DET-type reactions, and concepts and facts of bidirectional DET-type reactions from a cross-disciplinary viewpoint

Direct electron transfer-type bioelectrocatalysis by membrane-bound aldehyde dehydrogenase from Gluconobacter oxydans and cyanide effects on its bioelectrocatalytic properties

The bioelectrocatalytic properties of membrane-bound aldehyde dehydrogenase (AlDH) from Gluconobacter oxydans NBRC12528 were evaluated. AlDH exhibited direct electron transfer (DET)-type bioelectrocatalytic activity for acetaldehyde oxidation at several kinds of electrodes. The kinetic and thermodynamic parameters for bioelectrocatalytic acetaldehyde oxidation were estimated based on the partially random orientation model. Moreover, at the multi-walled carbon nanotube-modified electrode, the coordination of CN‾ to AlDH switched the direction of the DET-type bioelectrocatalysis to acetate reduction under acidic conditions. These phenomena were discussed from a thermodynamic viewpoint

Amperometric biosensor based on reductive H2O2 detection using pentacyanoferrate-bound polymer for creatinine determination.

Pentacyanoferrate-bound poly(1-vinylimidazole) (PVI[Fe(CN)5]) was selected as a mediator for amperometric creatinine determination based on the reductive H2O2 detection. Creatinine amidohydrolase (CNH), creatine amidohydrolase (CRH), sarcosine oxidase (SOD), peroxidase (POD), and PVI[Fe(CN)5] were crosslinked with poly(ethylene glycol) diglycidyl ether (PEGDGE) on a glassy carbon (GC) electrode for a creatinine biosensor fabrication. Reduction current was monitored at −0.1 V in the presence of creatinine and O2. It is revealed that PVI[Fe(CN)5] is suitable as a mediator for a bioelectrocatalytic reaction of POD, since PVI[Fe(CN)5] neither reacts with reactants nor works as an electron acceptor of SOD. The amounts of PVI[Fe(CN)5], PEGDGE, and enzymes were optimized toward creatinine detection. Nafion as a protecting film successfully prevented the enzyme layer from interferences. The detection limit and linear range in creatinine determination were 12 μM and 12–500 μM (R[2]= 0.993), respectively, and the sensitivity was 11 mA cm[−2] M[−1], which is applicable for urine creatinine tests. The results of the creatinine determination for four urine samples measured with this proposed method were compared with Jaffe method, and a good correlation was obtained between the results

PRODUCTION THROUGHPUT EVALUATION USING THE VASICEK MODEL

The Vasicek model, which is used in mathematical finance, was adopted to evaluate the production throughput of a production flow system. The production throughput was assumed to behave as an average regression. The production consisting of asynchronous and synchronous processes was evaluated theoretically using the average regression. Three patterns, which combined asynchronous and synchronous methods involving nine workers and six stages each, were also tested. Both experiment and calculations gave almost the same production throughput data, thereby validating the mathematical model proposed in this study

OPTIMAL CONTROL OF PRODUCTION PROCESSES THAT INCLUDE LEAD-TIME DELAYS

In this study, we investigate a method for optimal control of production processes that include lead-time delays. We propose a model that expresses lead-time lag in a strict mathematical model and a model with lead-time delay based on an average regression process, which is the Ornstein-Uhlenbeck process model that is used in mathematical finance. Optimal control is obtained using each state equation. Further, we present a simple example to verify the proposed optimal control. We additionally propose that the control system does not incorporate the lead-time delay into the control strategy and that it is simpler than the strict optimal control