Search

In this work, we have applied the entanglement concentration protocols to partially entangled subsets of classes of highly entangled multi-qubit states, created on IBM quantum computer. The highly entangled multi-qubit states like Z-states and cluster states act as a powerful resource in measurement based computation model. The success of the protocol is analysed through calculating the success probability obtained from the IBM quantum computer and simulator with which the protocol can generate maximally entangled states. The results of the protocol obtained from the IBM simulator are in close agreement with the theoretical predictions of the protocol, however, the results of IBMQ computer show comparative deviations due to large amount of gate errors, qubit errors and readout errors.

Intelligent transportation systems (ITS) are a critical component of Industry 4.0 and 5.0, particularly having applications in logistic management. One of their crucial utilization is in supply-chain management and scheduling for optimally routing transportation of goods by vehicles at a given set of locations. This paper discusses the broader problem of vehicle traffic management, more popularly known as the Vehicle Routing Problem (VRP), and investigates the possible use of near-term quantum devices for solving it. For this purpose, we give the Ising formulation for VRP and some of its constrained variants. Then, we present a detailed procedure to solve VRP by minimizing its corresponding Ising Hamiltonian using a hybrid quantum-classical heuristic called Quantum Approximate Optimization Algorithm (QAOA), implemented on the IBM Qiskit platform. We compare the performance of QAOA with classical solvers such as CPLEX on problem instances of up to 15 qubits. We find that performance of QAOA has a multifaceted dependence on the classical optimization routine used, the depth of the ansatz parameterized by $p$ , initialization of variational parameters, and problem instance itself.

When the discrete time translation symmetry of a localized, periodically driven many-body quantum system is spontaneously broken, the new phase of matter emerges.This resultant new topological state of matter is emerged as “discrete-time crystal”. Here we report the observation of a discrete time crystal by theoretically simulating it on a digital quantum simulator provided by the IBM quantum experience platform. We designed the quantum circuit and simulated the dynamics of the discrete-time crystal in a system of spin half particles. The self-correction in time, of the discrete time crystal through the Fourier spectrum is analyzed. We noted the survival of the discrete-time crystal behavior when the system is subjected to an external delta potential. Furthermore, we also characterized how the system responds to real noise and observed the sensitivity of discrete-time crystal behavior.

In this work, we conduct a quantum simulation of a particle in a harmonic oscillator potential on a quantum chip provided by IBM quantum experience platform. The simulation is carried out in two spatial dimensions and the algorithm used is generalized for n-spatial dimensions. Thus, the mentioned approach can be used to simulate n-dimensional harmonic oscillator. We implement the time translation unitary operator on an arbitrary quantum state to show that the probability amplitudes of position oscillate in time. We propose a quantum circuit to effectuate the time translation operator. The proposed circuit is then generalized for a n-qubit system that can be used to realize more meticulous simulations.

The vehicle routing problem (VRP) is an example of a combinatorial optimization problem that has attracted academic attention due to its potential use in various contexts. VRP aims to arrange vehicle deliveries to several sites in the most efficient and economical manner possible. Quantum machine learning offers a new way to obtain solutions by harnessing the natural speedups of quantum effects, although many solutions and methodologies are modified using classical tools to provide excellent approximations of the VRP. In this paper, we employ 6and 12qubit circuits, respectively, to build and evaluate a hybrid quantum machine learning approach for solving VRP of 3- and 4-city scenarios. The approach employs quantum support vector machines (QSVMs) trained using a variational quantum eigensolver on a static or dynamic ansatz. Different encoding strategies are used in the experiment to transform the VRP formulation into a QSVM and solve it. Multiple optimizers from the IBM Qiskit framework are also evaluated and compared