scholarly journals Quantization of Blackjack: Quantum Basic Strategy and Advantage

2021 ◽  
Author(s):  
Yushi Mura ◽  
Hiroki Wada
Keyword(s):  
Quantum Mechanics ◽  
Quantum Circuit ◽  
Quantum Computers ◽  
Quantum Game ◽  
Basic Strategy ◽  
Card Games ◽  
Gambling Theory ◽  
Quantum Version ◽  
Near Future ◽  
Classical Game

Abstract Quantum computers that process information by harnessing the remarkable power of quantum mechanics are increasingly being put to practical use. In the future, their impact will be felt in numerous fields, including in online casino games. This is one of the reasons why quantum gambling theory has garnered considerable attention. Studies have shown that the quantum gambling theory often yields nontrivial consequences that classical theory cannot interpret. We formulated blackjack game, which is one of the most famous card games, as a quantum game and found possible quantum entanglement between strategies. We also devised a quantum circuit reproducing classical blackjack. This circuit can be realized in the near future when quantum computers are commonplace. Furthermore, we showed that the player’s expectation increases compared to the classical game using quantum basic strategy, which is a quantum version of the popular basic strategy of blackjack.

scholarly journals Strawberry Fields: A Software Platform for Photonic Quantum Computing

Quantum ◽  
2019 ◽  
Vol 3 ◽  
pp. 129 ◽  
Author(s):  
Nathan Killoran ◽  
Josh Izaac ◽  
Nicolás Quesada ◽  
Ville Bergholm ◽  
Matthew Amy ◽  
...  
Keyword(s):  
Quantum Computer ◽  
Quantum Circuit ◽  
Continuous Variable ◽  
Quantum Computers ◽  
Circuit Optimization ◽  
Hamiltonian Simulation ◽  
Quantum Programming ◽  
Quantum Polynomial ◽  
Main Components ◽  
Near Future

We introduce Strawberry Fields, an open-source quantum programming architecture for light-based quantum computers, and detail its key features. Built in Python, Strawberry Fields is a full-stack library for design, simulation, optimization, and quantum machine learning of continuous-variable circuits. The platform consists of three main components: (i) an API for quantum programming based on an easy-to-use language named Blackbird; (ii) a suite of three virtual quantum computer backends, built in NumPy and TensorFlow, each targeting specialized uses; and (iii) an engine which can compile Blackbird programs on various backends, including the three built-in simulators, and - in the near future - photonic quantum information processors. The library also contains examples of several paradigmatic algorithms, including teleportation, (Gaussian) boson sampling, instantaneous quantum polynomial, Hamiltonian simulation, and variational quantum circuit optimization.

scholarly journals Asset–liability modelling in the quantum era

2021 ◽  
Vol 26 ◽  
Author(s):  
T. Berry ◽  
J. Sharpe
Keyword(s):  
Quantum Mechanics ◽  
Quantum Computer ◽  
Quantum Algorithm ◽  
Capital Requirements ◽  
Quantum Computers ◽  
Investment Management ◽  
Asset Liability Management ◽  
Liability Management ◽  
Insight Into ◽  
Current Practices

Abstract This paper introduces and demonstrates the use of quantum computers for asset–liability management (ALM). A summary of historical and current practices in ALM used by actuaries is given showing how the challenges have previously been met. We give an insight into what ALM may be like in the immediate future demonstrating how quantum computers can be used for ALM. A quantum algorithm for optimising ALM calculations is presented and tested using a quantum computer. We conclude that the discovery of the strange world of quantum mechanics has the potential to create investment management efficiencies. This in turn may lead to lower capital requirements for shareholders and lower premiums and higher insured retirement incomes for policyholders.

scholarly journals A divide-and-conquer algorithm for quantum state preparation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Israel F. Araujo ◽  
Daniel K. Park ◽  
Francesco Petruccione ◽  
Adenilton J. da Silva
Keyword(s):  
Computational Cost ◽  
Quantum Circuit ◽  
Divide And Conquer ◽  
Computational Time ◽  
Quantum Computers ◽  
Dimensional Vector ◽  
Quantum Devices ◽  
Divide And Conquer Algorithm ◽  
Quantum State Preparation ◽  
Quantum Device

AbstractAdvantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.

scholarly journals Deep neural networks for quantum circuit mapping

2021 ◽  
Author(s):  
Giovanni Acampora ◽  
Roberto Schiattarella
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Machine Learning Techniques ◽  
Quantum Computers ◽  
Physical Constraints ◽  
Proper Mapping ◽  
Correct Execution

AbstractQuantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy.

Quantum Game of Dual-channel Supply Chain under Free-riding Behavior

2021 ◽  
Vol 1 (1) ◽  
pp. 3-17
Author(s):  
Yu-Chung Chang ◽  
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Quantum Entanglement ◽  
Free Riding ◽  
Selling Price ◽  
Quantum Game ◽  
Optimal Price ◽  
Dual Channel ◽  
Dual Channel Supply Chain ◽  
Classical Game

Based on the perspective of the quantum game, this paper explores when the online direct sales channel takes the free-riding behavior after the retail channel provides high-quality experience and services and how the dual-channel supply chain establishes a commodity pricing strategy. The retailer’s selling price follows a decreasing function of the free-riding behavior coefficient. while the online direct selling price does an increasing function of the free-riding behavior coefficient. Under centralized decision-making, there is no quantum entanglement, so the quantum game solution is consistent with the classical game solution. Under decentralized decision-making, the optimal price and profit of the quantum game are higher than those of the classical game when the quantum entanglement degree is greater than zero. When the quantum entanglement tends to be infinite, the optimal price of the quantum game finally remains in convergence. The quantum game theory is a more optimal decision-making method than the classical game theory.

scholarly journals Open shell electronic state calculations on quantum computers: A quantum circuit for the preparation of configuration state functions based on Serber construction

2019 ◽  
Vol 1 ◽  
pp. 100002 ◽  
Author(s):  
Kenji Sugisaki ◽  
Satoru Yamamoto ◽  
Shigeaki Nakazawa ◽  
Kazuo Toyota ◽  
Kazunobu Sato ◽  
...  
Keyword(s):  
Electronic State ◽  
Quantum Circuit ◽  
Open Shell ◽  
Quantum Computers ◽  
State Functions

scholarly journals Quantum chemistry on quantum computers: quantum simulations of the time evolution of wave functions under the S2 operator and determination of the spin quantum number S

2019 ◽  
Vol 21 (28) ◽  
pp. 15356-15361 ◽  
Author(s):  
Kenji Sugisaki ◽  
Shigeaki Nakazawa ◽  
Kazuo Toyota ◽  
Kazunobu Sato ◽  
Daisuke Shiomi ◽  
...  
Keyword(s):  
Time Evolution ◽  
Quantum Number ◽  
Quantum Circuit ◽  
Wave Functions ◽  
Quantum Computers ◽  
Phase Estimation ◽  
Quantum Simulations ◽  
Spin Quantum ◽  
Spin Quantum Number

A quantum circuit to simulate time evolution of wave functions under an S2 operator is provided, and integrated it to the quantum phase estimation circuit to calculate the spin quantum number S of arbitrary wave functions on quantum computers.

scholarly journals Experimental study of quantum simulation for quantum chemistry with a nuclear magnetic resonance simulator

2012 ◽  
Vol 370 (1976) ◽  
pp. 4734-4747 ◽  
Author(s):  
Dawei Lu ◽  
Nanyang Xu ◽  
Boruo Xu ◽  
Zhaokai Li ◽  
Hongwei Chen ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Quantum Chemistry ◽  
Quantum Simulation ◽  
Isomerization Reaction ◽  
Quantum Computers ◽  
One Dimensional ◽  
Time Quantum ◽  
Near Future ◽  
Nuclear Magnetic

Quantum computers have been proved to be able to mimic quantum systems efficiently in polynomial time. Quantum chemistry problems, such as static molecular energy calculations and dynamical chemical reaction simulations, become very intractable on classical computers with scaling up of the system. Therefore, quantum simulation is a feasible and effective approach to tackle quantum chemistry problems. Proof-of-principle experiments have been implemented on the calculation of the hydrogen molecular energies and one-dimensional chemical isomerization reaction dynamics using nuclear magnetic resonance systems. We conclude that quantum simulation will surpass classical computers for quantum chemistry in the near future.

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