Quantum Technology is claimed to disrupt everything from how we navigate, observe our reality, compute critical information and propagate that information over networks, however, the constraints of quantum information theory and practical economics may strictly limit what’s possible. This talk will highlight a few well promoted use cases, present the underlying assumptions, and then walk through the optimistic and pessimistic cases for and against each use case. We will follow a "bottoms up" approach supported by data models with the goal of understanding what the overall market might look like over the next approximately 5 years.Quantum Technology is claimed to disrupt everything from how we navigate, observe our reality, compute critical information and propagate that information over networks, however, the constraints of quantum information theory and practical economics may strictly limit what’s possible. This talk will highlight a few well promoted use cases, present the underlying assumptions, and then walk through the optimistic and pessimistic cases for and against each use case. We will follow a "bottoms up" approach supported by data models with the goal of understanding what the overall market might look like over the next approximately 5 years.

Optical and Quantum Sensing and Precision Metrology IIMar 5, 20260 citations

Beyond Traditional Probabilistic Methods in Econometrics

Hung T. Nguyen, Nguyễn Đức Trung, Nguyễn Ngọc Thạch

Studies in computational intelligenceNov 24, 201846 citations

Potential Functions and the Characterization of Economics-Based Information

Emmanuel E. Haven

Foundations of PhysicsJul 3, 20153 citations

Non iterative LDPC decoding by syndrome generation using Artificial Neural Network

Dinesh Kumar, Alkesh Agrawal, Gopal Singh Phartiyal

Low density parity-check code (LDPC) is an error correcting code used in noisy communication channel (e.g. AWGN) to reduce the probability of error in information. By using LDPC codes, this probability can be made comparatively small, so that the data transmission rate can be as close to Shannon's limit. The decoding of Low Density Parity Check (LDPC) codes by iterative process of belief propagation gives challenges for designers looking for real time performance in communication systems. This thesis work proposes the use of Artificial Neural Networks (ANN) to replace belief propagation to approach closer to Shannon's limit more closer than other traditional decoding methods. This thesis is intended to design a new methodology to decode LDPC codes in Non-iterative manner with the help of ANN and Look Up Table (LUT). This work is at initial stage and will be extended for better performance.Low density parity-check code (LDPC) is an error correcting code used in noisy communication channel (e.g. AWGN) to reduce the probability of error in information. By using LDPC codes, this probability can be made comparatively small, so that the data transmission rate can be as close to Shannon's limit. The decoding of Low Density Parity Check (LDPC) codes by iterative process of belief propagation gives challenges for designers looking for real time performance in communication systems. This thesis work proposes the use of Artificial Neural Networks (ANN) to replace belief propagation to approach closer to Shannon's limit more closer than other traditional decoding methods. This thesis is intended to design a new methodology to decode LDPC codes in Non-iterative manner with the help of ANN and Look Up Table (LUT). This work is at initial stage and will be extended for better performance.

National Conference on Recent Advances in Electronics & Computer Engineering (RAECE)Feb 1, 20151 citation

Generalised Statistical Equilibrium

Isha Kotecha

Springer thesesJan 1, 20220 citations

Migdal-type renormalization-group calculation for the kinetic Ising model

S. T. Chui, Gabor Forgács, H. L. Frisch

We make use of the calculation of Achiam and Kosterlitz and investigate the generalization of the Migdal-type renormalization-group calculation to critical dynamics, by looking at the one- and two-dimensional kinetic Ising model with no conserved magnetization. In the two-dimensional case the dynamical critical index ${Z}_{M}(\mathrm{magnetic}\mathrm{perturbation})=2.064$ and ${Z}_{E}(\mathrm{energylike}\mathrm{perturbation})=1.819$ for scale factor $\ensuremath{\lambda}=1$. ${Z}_{M}$ involves the static $\frac{\ensuremath{\beta}}{\ensuremath{\nu}}$ exponent, while ${Z}_{E}$ involves $\frac{1}{\ensuremath{\nu}}$, and therefore, it is not surprising that ${Z}_{M}$ is closer to the high-temperature expansion results, since $\frac{\ensuremath{\beta}}{\ensuremath{\nu}}$ in the Migdal approximation for statics is much closer to the exact value than $\frac{1}{\ensuremath{\nu}}$. In one dimension we obtain ${Z}_{M}={Z}_{E}=2$ , which is the exact result of Glauber.We make use of the calculation of Achiam and Kosterlitz and investigate the generalization of the Migdal-type renormalization-group calculation to critical dynamics, by looking at the one- and two-dimensional kinetic Ising model with no conserved magnetization. In the two-dimensional case the dynamical critical index ${Z}_{M}(\mathrm{magnetic}\mathrm{perturbation})=2.064$ and ${Z}_{E}(\mathrm{energylike}\mathrm{perturbation})=1.819$ for scale factor $\ensuremath{\lambda}=1$. ${Z}_{M}$ involves the static $\frac{\ensuremath{\beta}}{\ensuremath{\nu}}$ exponent, while ${Z}_{E}$ involves $\frac{1}{\ensuremath{\nu}}$, and therefore, it is not surprising that ${Z}_{M}$ is closer to the high-temperature expansion results, since $\frac{\ensuremath{\beta}}{\ensuremath{\nu}}$ in the Migdal approximation for statics is much closer to the exact value than $\frac{1}{\ensuremath{\nu}}$. In one dimension we obtain ${Z}_{M}={Z}_{E}=2$, which is the exact result of Glauber.

Physical review. B, Condensed matterJul 1, 197917 citations

{Z}_{M}(\mathrm{magnetic}\mathrm{perturbation})=2.064

and

{Z}_{E}(\mathrm{energylike}\mathrm{perturbation})=1.819

for scale factor $\ensuremath{\lambda}=1$.

{Z}_{M}

involves the static $\frac{\ensuremath{\beta}}{\ensuremath{\nu}}$ exponent, while

{Z}_{E}

involves $\frac{1}{\ensuremath{\nu}}$, and therefore, it is not surprising that

{Z}_{M}

is closer to the high-temperature expansion results, since $\frac{\ensuremath{\beta}}{\ensuremath{\nu}}$ in the Migdal approximation for statics is much closer to the exact value than $\frac{1}{\ensuremath{\nu}}$. In one dimension we obtain

{Z}_{M}={Z}_{E}=2

, which is the exact result of Glauber.