SPIN

Investing in stocks is a common choice for financial management. Technical indicators (TIs) assist investors in determining the best trading time to make a fortune. Moving average (MA) and relative strength index (RSI) are the most common TIs. The proposed hybrid technique maximizes the capabilities of these two indicators. This study utilizes the quantum-inspired algorithm to assist effectively in searching for the optimal solution in the vast solution space. The proposed trading system contains four innovative features. First, the traditional usage restriction of MA and RSI is removed to increase their potential effectiveness and identify the most profitable trading strategy. Second, this research proposes an innovative hybrid indicator (HI) that combines MA and RSI to simultaneously achieve both benefits. HI eliminates the restriction of employing a single indicator at once. Third, an efficient quantum-inspired algorithm, the Global-best-guided Quantum-inspired Tabu Search Algorithm with Quantum NOT Gate (GNQTS), effectively and efficiently explores optimized parameters. Fourth, this study proposes 60 sliding windows to determine the optimal period for training and testing. The investment targets include well-known indices: DJIA, S&P 500, NASDAQ Composite Index, and NYA, as well as high-reputation companies on the US stock market: DJIA components. By removing the restrictions imposed by these two indicators and the use of HI, the experiment results demonstrate that GNQTS can discover optimal parameters to generate higher returns than state-of-the-art methods and buy-and-hold (B&H) strategies. The proposed hybrid strategy provides for the promising prospect of quantum-inspired applications and the utilization of multiple indicators.

Quantum-secure communication and computation have garnered increasing attention due to their powerful search capabilities for certain complex problems and their ability to provide unconditional security. This indicates that classical cryptography, such as asymmetric ciphers based on complex mathematics, faces a significant threat from quantum algorithms. However, quantum cryptography can detect any attempt to eavesdrop on a channel. As a result, the investigation and implementation of quantum computers and quantum-secure communication networks have become essential objectives for numerous research teams. This paper provides an overview of quantum cryptography, including its development, and focuses on the theoretical aspect of quantum secure direct communication (QSDC). This helps readers understand the theoretical operating principles and gain a better understanding of the entire quantum characteristic and protocol operation in the QSDC field. QSDC enables secure communication without previously shared private keys, making it a crucial topic in quantum cryptography research. This paper provides an overview of several well-known versions of QSDC in theory, discussing their applications, challenges and potential future directions.

As the popularity of mobile devices and services continues to increase, the demand for more efficient and effective algorithms to manage network utility is increased. Here, we aim to optimize Network Bandwidth Utilization (NBU) by managing user equipments (UEs) and base stations (BSs) association in 5G ORAN architecture. We consider the network total throughput as an objective function and try to find the suitable Cell Individual Offset (CIO) to trigger UE handover with the A3 event such that the network throughput is optimal. We formulate this problem into a Quadratic Unconstrained Binary Optimization (QUBO) problem such that we can explore the power of Quantum Annealing (QA), which is a promising approach to finding the optimal solution for the NP-Hard problem. We also analyze the penalty coefficients setting problem which impacts the outcome of QA significantly. We test our QUBO formulation on a dataset of 7 BSs and 150 UEs with Fujitsu’s third generation Digital Annealer (DA3). Using our QUBO formulation with DA3 solver with fine-tuning, we can improve the throughput with 60% hit rate on this dataset.

Quantum computing promises to have a tremendous advantage over its classical counterpart for solving computationally hard problems, yet remains in a relatively early stage for practical applications owing to the limited capabilities of today’s quantum computers. The approach to the special purposes of quantum computers by exploiting the special-purpose physics-inspired or quantum-inspired computers is emerging as a novel alternative to its quantum counterpart in tackling hard problems in high-performance computing. Inspired by physics, the Ising machine — a type of special-purpose computer that implements or emulates physics or quantum effects of the Ising model to speed up finding solutions to optimization problems — has recently become an active research area in the field of combinatorial optimization. This paper is to address the key enabling software and hardware technology underlying physics-inspired optimization using Ising machines in the unified quadratic unconstrained binary optimization (QUBO) framework for modeling and solving computationally hard combinatorial optimization problems, and with an aim to shed some light on the challenges and opportunities associated with the ever-growing landscape of this novel high-performance computing.

A key to achieving quantum computing is the qubit-entanglement. Nevertheless, real-life qubits cannot be isolated from the environment completely. As a result, the entanglement between qubits decoheres via their interaction with the environment. Therefore, it is essential to understand how the qubit-entanglement changes over time. In this work, we study the entanglement between qubits when placed in a bulk of dielectric material embedded with a nanosized metal particle. Most importantly, we focus on the effects of the unavoidable localized surface plasmons induced at the surface of the metal particle. We discuss how the distance between the metal particles affects the entanglement of the qubits and how various entangled states are affected by the localized surface plasmons.

Bike sharing systems (BSSs) are gaining popularity worldwide, and with the rise of global warming, they have become even more critical for reducing greenhouse gas emissions in cities. One of the key operations of a BSS is rebalancing, which involves maintaining the number of bikes at each station to a target value by arranging vehicle loading and unloading operations. This paper proposes a quantum-inspired vehicle routing scheme to solve the rebalancing problem in BSSs. The problem is modeled as a variant of the Traveling Salesman Problem (TSP), which is an NP-hard problem that is computationally difficult to solve. The proposed approach formulates the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, which can be solved using a digital annealer. The proposed scheme’s performance is evaluated on the Fujitsu digital annealer and compared with the proposed greedy algorithm. The experimental results show that the proposed approach outperforms the greedy algorithm in finding a better feasible solution.

With the development of quantum communication technology, Shor’s algorithm is expected to crack RSA encryption. Quantum communication will change the method of classical encryption in the future. We propose a quantum image encryption method via reversible quantum logic gates computing as quantum computing is reversible. An entangled pair of qubits are used for data transmission. The plaintext image is encrypted into an encrypted image via the quantum gate by using superdense coding to encrypt and transmit the operating quantum gate. The encrypted image is decoded by reversible computing, which is expected to provide a way for quantum image encryption.

Transmon-type superconducting qubits have become a popular platform for quantum computing due to their robust qubit coherence properties. In recent years, different types of materials have been searched to replace the Al₂O₃ tunnel barrier in conventional Al-based Josephson junctions, in order to explore new functions of qubits utilizing the distinct nature of the chosen materials. In this report, we introduce our works involving using Weyl semimetals and graphene as a component in Josephson junctions for qubit designs and coupling to coplanar waveguide (CPW) resonators. We fabricate a MoTe₂ transmon and investigate whether it exhibits topological properties by tuning the resonator frequency with magnetic flux. Next, we introduce a graphene gatemon, in which the gate-tunability of resonator frequency has been demonstrated and the two-tone spectroscopy has shown a qubit state transition.

Gapless linear band structure quantum electronic materials play important roles in quantum computing developments due to their novel superb electronic transport properties and two-dimensional (2D) gateable device possibility. However, the sensitive surface of quantum electronic materials easily reacted with ambient air molecules so as to change their intrinsic transport properties. We review a series of our works to show the importance of surface reactions in quantum electronic materials, such as graphene. By using different electronic transport facilities and in situ vacuum annealing methods, we are able to gently clean the absorbed air molecules on graphene so as to change the carrier densities and the electronic transport properties, which is a great improvement for 2D-quantum material-based quantum chips and computing devices.

We experimentally investigated the energy spectrum of two strongly coupled tunable transmon atoms with a coupling energy of 300MHz. The energy levels are dispersively probed through a readout resonator coupled to the atoms. The higher excited level energies could be probed by 2-photon or 3-photon processes. The power dependence of the readout transmission showed that the n-photon process satisfies the power law with a power of $n-$1. We also observed the Autler-Townes splitting, by probing the Rabi frequencies resonant to one of the higher levels driven by an external microwave field. Coherent operations on the atoms reveal that the relaxation time (T1) and dephasing time (T2) are approximately 62ns and 51ns, respectively.