Predicting sampling advantage of stochastic Ising machines for quantum simulations

Climate change is accelerating, necessitating sophisticated forecasting techniques to alleviate its impacts efficiently. The huge amount and diversity of climate data frequently exceed the endurance of traditional computing techniques, creating problems with accuracy and processing speed. This research presents a quantum-enhanced framework for big data analytics in climate science by combining quantum machine learning, optimisation, and simulation methods. The recommended strategy empowers specific analysis of high-dimensional climate datasets, contributing noteworthy enhancements in predicting critical phenomena such as temperature irregularities, rainfall trends, and catastrophic weather patterns. By including quantum algorithms, the framework attains scalability and real-time competencies, minimising computational processing times while improving predictive accuracy. These results demonstrate the advantage of quantum-enhanced models over classical approaches by giving innovative tools for policymakers and industry leaders to help them make data-driven, well-informed decisions. The study highlights the revolutionary potential of quantum computing in addressing global climate challenges, making new paths for environmentally friendly remedies. As quantum technologies develop, their use in climate science will motivate innovative solutions for the endurance and preservation of the environment. Major Findings: This study highlights the necessity for cross-disciplinary collaboration to completely integrate quantum computing advancements into climate modelling, guaranteeing actionable and accurate perceptions. The results show how important quantum-enhanced analytics can be in helping to develop sustainable decision-making to deal with the escalating climate crisis.

With the development of quantum computing, traditional combinatorial optimization problems have found new solving methods. This paper studies the excavator and mining truck matching optimization problem based on the QUBO model, aiming to optimize equipment configuration and operational plans by maximizing long-term profit. An integer optimization model is first established and converted into a QUBO model, which is solved using the simulated annealing algorithm. Furthermore, quantum computing is explored through the Coherent Ising Machine (CIM) simulator to examine its application in large-scale combinatorial optimization. The results show that the CIM simulator outperforms traditional algorithms in both accuracy and speed, significantly enhancing computational efficiency. Based on simulated annealing and quantum computing, an optimized procurement plan is provided, yielding a maximum total profit of 500 million yuan. The experiments also demonstrate the potential of quantum computing in optimizing equipment configuration in smart mining, driving the intelligent evolution of the field.

Charged particle reconstruction or track reconstruction is one of the most crucial components of pattern recognition in high-energy collider physics. It is known to entail enormous consumption of computing resources, especially when the particle multiplicity is high, which will be the conditions at future colliders, such as the High Luminosity Large Hadron Collider and Super Proton–Proton Collider. Track reconstruction can be formulated as a quadratic unconstrained binary optimization (QUBO) problem, for which various quantum algorithms have been investigated and evaluated with both a quantum simulator and hardware. Simulated bifurcation algorithms are a set of quantum-annealing-inspired algorithms, known to be serious competitors to other Ising machines. In this study, we show that simulated bifurcation algorithms can be employed to solve the particle tracking problem. The simulated bifurcation algorithms run on classical computers and are suitable for parallel processing and usage of graphical processing units, and they can handle significantly large amounts of data at high speed. These algorithms exhibit reconstruction efficiency and purity comparable to or sometimes improved over those of simulated annealing, but the running time can be reduced by as much as four orders of magnitude. These results suggest that QUBO models together with quantum-annealing-inspired algorithms are valuable for current and future particle tracking problems.

Quantum annealing machines are next-generation computers for solving combinatorial optimization problems. Although physical simulations are one of the most promising applications of quantum annealing machines, a method how to embed the target problem into the machines has not been developed except for certain simple examples. In this study, we focus on a method of representing real numbers using binary variables, or quantum bits. One of the most important problems for conducting physical simulation by quantum annealing machines is how to represent the real number with quantum bits. The variables in physical simulations are often represented by real numbers but real numbers must be represented by a combination of binary variables in quantum annealing, such as quadratic unconstrained binary optimization (QUBO). Conventionally, real numbers have been represented by assigning each digit of their binary number representation to a binary variable. Considering the classical annealing point of view, we noticed that when real numbers are represented in binary numbers, there are numbers that can only be reached by inverting several bits simultaneously under the restriction of not increasing a given Hamiltonian, which makes the optimization very difficult. In this work, we propose three new types of real number representation and compared these representations under the problem of solving linear equations. As a result, we found experimentally that the accuracy of the solution varies significantly depending on how the real numbers are represented. We also found that the most appropriate representation depends on the size and difficulty of the problem to be solved and that these differences show a consistent trend for two annealing solvers. Finally, we explain the reasons for these differences using simple models, the minimum required number of simultaneous bit flips, one-way probabilistic bit-flip energy minimization, and simulation of ideal quantum annealing machine.

The simulation of high-temperature superconducting materials by implementing strongly correlated fermionic models in optical lattices is one of the major objectives in the field of analog quantum simulation. Here we show that local control and optical bilayer capabilities combined with spatially resolved measurements create a versatile toolbox to study fundamental properties of both nickelate and cuprate high-temperature superconductors. On the one hand, we present a scheme to implement a mixed-dimensional (mixD) bilayer model that has been proposed to capture the essential pairing physics of pressurized bilayer nickelates. This allows for the long-sought realization of a state with long-range superconducting order in current lattice quantum simulation machines. In particular, we show how coherent pairing correlations can be accessed in a partially particle-hole transformed and rotated basis. On the other hand, we demonstrate that control of local gates enables the observation of d-wave pairing order in the two-dimensional (single-layer) repulsive Fermi-Hubbard model through the simulation of a system with attractive interactions. Lastly, we introduce a scheme to measure momentum-resolved dopant densities, providing access to observables complementary to solid-state experiments—which is of particular interest for future studies of the enigmatic pseudogap phase appearing in cuprates. Published by the American Physical Society 2024

Chiral edge states are a hallmark of topological physics, and the emergence of synthetic dimensions has provided ideal platforms for investigating chiral topology while overcoming the limitations of real space. Conventional studies have primarily concentrated on symmetric chiral behaviors, limited by complex and inflexible systems. Here, we demonstrate a programmable integrated photonic platform to generate and manipulate reconfigurable chiral edge states in synthetic dimensions within a single lithium niobate microring resonator. Our system is realized by integrating independent frequency and pseudospin degrees of freedom in the dynamically modulated resonator, which features tunable artificial gauge potentials and long-range couplings. We demonstrate a variety of reconfigurable chiral behaviors in synthetic dimensions, including the realization and frustration of chiral edge states in a synthetic Hall ladder, the generation of imbalanced chiral edge currents, and the regulation of chiral behaviors among chirality, single-pseudospin enhancement, and complete suppression. This work paves the way for exploring chiral edge states in high-dimensional synthetic space on a programmable photonic chip, showing promising potential for applications in optical communications, quantum simulations, signal processing, and photonic neuromorphic computing.

Integrated photonics is playing an always-increasing role in the development of new technologies and devices, with applications in the fields of optical communications and, more recently, in linear-optical quantum computing and simulations. Actually, the possibility of integrating complex and reconfigurable functionalities inside a small chip allows to develop ultra-stable platforms for the large-scale processing of classical and quantum optical signals [1] , [2] .

Timetable scheduling is a combinatorial optimization problem that presents formidable challenges for classical computers. This paper introduces a pioneering methodology for addressing the high-speed train timetabling problem through quantum computing. Initially, a comprehensive binary integer programming model, grounded in the space–time network, is proposed (M1). To manage the intricacy of model M1, a knapsack problem reformulation is employed to establish a simplified binary integer programming model (M2). Both M1 and M2 are subsequently converted into quadratic unconstrained binary optimization (QUBO) models to harness the potential of quantum computing. Several techniques, including the Gurobi solver, simulated annealing, and the coherent Ising machine (CIM) quantum simulator, are deployed to solve the model across four distinct scenarios of varying complexity. The findings indicate that CIM quantum simulator outperforms the simulated annealing method in terms of solution quality for medium-scale problems.

Photonic honeycomb lattices have attracted broad interests for their fruitful ways in manipulating light, which yet hold difficulties in achieving arbitrary reconfigurability and hence flexible functionality due to fixed geometry configurations. Here we theoretically propose to construct the honeycomb lattice in a one-dimensional ring array under dynamic modulations, with an additional synthetic dimension created by connecting the frequency degree of freedom of light. Such a system is highly re-configurable with parameters flexibly controlled by external modulations. Therefore, various physical phenomena associated with graphene including Klein tunneling, valley-dependent edge states, effective magnetic field, as well as valley-dependent Lorentz force can be simulated in this lattice, which exhibits important potentials for manipulating photons in different ways. Our work unveils an alternative platform for constructing the honeycomb lattice in a synthetic space, which holds complex functionalities and could be important for optical signal processing as well as quantum simulation.

Combinatorial optimization problems are ubiquitous in industrial applications. However, finding optimal or close-to-optimal solutions can often be extremely hard. Because some of these problems can be mapped to the ground-state search of the Ising model, tremendous effort has been devoted to developing solvers for Ising-type problems over the past decades. Recent advances in controlling and manipulating both quantum and classical systems have enabled novel computing paradigms such as quantum simulators and coherent Ising machines to tackle hard optimization problems. Here, we examine and benchmark several physics-inspired optimization algorithms, including coherent Ising machines, gain-dissipative algorithms, simulated bifurcation machines, and Hopfield neural networks, which we collectively refer to as stochastic driven nonlinear dynamical systems. Most importantly, we benchmark these algorithms against random Ising problems with planted solutions and compare them to simulated annealing as a baseline leveraging the same software stack for all solvers. We further study how different numerical integration techniques and graph connectivity affect performance. This work provides an overview of a diverse set of new optimization paradigms.

Photonic integration is as an enabling technology for photonic quantum science, providing great experimental scalability, stability, and functionality. Although the increasing complexity of quantum photonic circuits has allowed proof-of-principle demonstrations of quantum computation, simulation, and metrology[1], further development is severely limited by the on-chip photon flux that can be made available from external quantum light sources[2]. Overcoming such limitations would allow a significant scaling of quantum photonic experiments, and enable quantum-level investigation of many physical processes observable on-chip through nanophotonic and nanoplasmonic structures (e.g., Kerr, optomechanical, single-photon nonlinearities). Towards such goals, we have developed a scalable, heterogeneous III-V/Si 3 N 4 integration platform for quantum photonic circuits based on passive Si 3 N 4 waveguides which directly incorporate nanophotonic single-photon sources based on self-assembled InAs quantum dots (QDs)[3]. InAs quantum dots constitute the most promising solid-state triggered single-photon sources to date[4], while SI3N4 waveguides offer low-loss propagation, tailorable dispersion and high Kerr nonlinearities which can be used for linear and nonlinear optical signal processing down to the quantum level. In our platform, the building blocks of which are shown in Fig. 1(a), active GaAs waveguide-based geometries containing InAs QDs are designed to efficiently capture QD-emitted single-photons. Captured photons, confined within the GaAs core, are then transferred with high efficiency into a passive Si 3 N 4 waveguide network via adiabatic mode transformers. Figure 1(b) shows an example device fabricated with our platform: a GaAs microring resonator containing InAs quantum dots, evanescently coupled to a GaAs bus waveguide, which is in turn coupled to an underlying Si 3 N 4 waveguide through adiabatic mode-transformers. The photoluminescence spectrum for this device, in Fig. 1(b), shows that a single QD exciton near 1125 nm, coupled to a microring whispering-gallery mode, acts as a source of single-photons that are launched directly into the Si3N4 waveguide. This geometry also allows us to effectively control the QD spontaneous emission decay lifetime by spectrally detuning the WGM with respect to the QD, as shown in Fig. 1(d).

A new, to the best of our knowledge, technique is demonstrated for carrying out exact positive-P phase-space simulations of the coherent Ising machine quantum computer. By suitable design of the coupling matrix, general hard optimization problems can be solved. Here, computational quantum simulations of a feedback type of photonic parametric network are carried out, which is the implementation of the coherent Ising machine. Results for success rates are obtained using this scalable phase-space algorithm for quantum simulations of quantum feedback devices.

We give a detailed theoretical derivation of the master equation for the coherent Ising machine. This is a quantum computational network with feedback, that approximately solves NP-hard combinatoric problems, including the traveling salesman problem and various extensions and analogs. There are two possible types of master equation, either conditional on the feedback current or unconditional. We show that both types can be accurately simulated in a scalable way using stochastic equations in the positive-P phase-space representation. This depends on the nonlinearity present, and we use parameter values that are typical of current experiments. While the two approaches are in excellent agreement, they are not equivalent with regard to efficiency. We find that unconditional simulation has much greater efficiency, and is more scalable to large sizes. This is a case where too much knowledge is a dangerous thing. Conditioning the simulations on the feedback current is not essential to determining the success probability, but it greatly increases the computational complexity. To illustrate the speed improvements obtained with the unconditional approach, we carry out full quantum simulations of the master equation with up to 1000 nodes.