Search

Physical review applied 19(2), 024047 (2023). doi:10.1103/PhysRevApplied.19.024047, Published by American Physical Society, College Park, Md. [u.a.]

Annals of physics 453, 169314 (2023). doi:10.1016/j.aop.2023.169314, We take the point of view that building a one-way bridge from experimental data to mathematical models instead of the other way around avoids running into controversies resulting from attaching meaning to the symbols used in the latter. In particular, we show that adopting this view offers new perspectives for constructing mathematical models for and interpreting the results of Einstein–Podolsky–Rosen–Bohm experiments. We first prove new Bell-type inequalities constraining the values of the four correlations obtained by performing Einstein–Podolsky–Rosen–Bohm experiments under four different conditions. The proof is “model-free” in the sense that it does not refer to any mathematical model that one imagines to have produced the data. The constraints only depend on the number of quadruples obtained by reshuffling the data in the four data sets without changing the values of the correlations. These new inequalities reduce to model-free versions of the well-known Bell-type inequalities if the maximum fraction of quadruples is equal to one. Being model-free, a violation of the latter by experimental data implies that not all the data in the four data sets can be reshuffled to form quadruples. Furthermore, being model-free inequalities, a violation of the latter by experimental data only implies that any mathematical model assumed to produce this data does not apply. Starting from the data obtained by performing Einstein–Podolsky–Rosen–Bohm experiments, we construct instead of postulate mathematical models that describe the main features of these data. The mathematical framework of plausible reasoning is applied to reproducible and robust data, yielding without using any concept of quantum theory, the expression of the correlation for a system of two spin-1/2 objects in the singlet state. Next, we apply Bell’s theorem to the Stern–Gerlach experiment and demonstrate how the requirement of separability leads to the quantum-theoretical description of the averages and correlations obtained from an Einstein–Podolsky–Rosen–Bohm experiment. We analyze the data of an Einstein–Podolsky–Rosen–Bohm experiment and debunk the popular statement that Einstein–Podolsky–Rosen–Bohm experiments have vindicated quantum theory. We argue that it is not quantum theory but the processing of data from EPRB experiments that should be questioned. We perform Einstein–Podolsky–Rosen–Bohm experiments on a superconducting quantum information processor to show that the event-by-event generation of discrete data can yield results that are in good agreement with the quantum-theoretical description of the Einstein–Podolsky–Rosen–Bohm thought experiment. We demonstrate that a stochastic and a subquantum model can also produce data that are in excellent agreement with the quantum-theoretical description of the Einstein–Podolsky–Rosen–Bohm thought experiment., Published by Elsevier, Amsterdam [u.a.]

In recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in quantum machine learning and in particular for the popular quantum SVM. Several versions of the quantum SVM have been proposed, and quantum annealing has been shown to be effective in them. Extensions to multiclass problems have also been made, which consist of an ensemble of multiple binary classifiers. This work proposes a novel quantum SVM formulation for direct multiclass classification based on quantum annealing, called Quantum Multiclass SVM (QMSVM). The multiclass classification problem is formulated as a single Quadratic Unconstrained Binary Optimization (QUBO) problem solved with quantum annealing. The main objective of this work is to evaluate the feasibility, accuracy, and time performance of this approach. Experiments have been performed on the D-Wave Advantage quantum annealer for a classification problem on remote sensing data. The results indicate that, despite the memory demands of the quantum annealer, QMSVM can achieve accuracy that is comparable to standard SVM methods and, more importantly, it scales much more efficiently with the number of training examples, resulting in nearly constant time. This work shows an approach for bringing together classical and quantum computation, solving practical problems in remote sensing with current hardware., Comment: 12 pages, 10 figures, 3 tables. Submitted to IEEE JSTARS

An accurate understanding of the Josephson effect is the keystone of quantum information processing with superconducting hardware. Here we show that the celebrated $\sin\varphi$ current-phase relation (C$\varphi$R) of Josephson junctions (JJs) fails to fully describe the energy spectra of transmon artificial atoms across various samples and laboratories. While the microscopic theory of JJs contains higher harmonics in the C$\varphi$R, these have generally been assumed to give insignificant corrections for tunnel JJs, due to the low transparency of the conduction channels. However, this assumption might not be justified given the disordered nature of the commonly used AlO$_x$ tunnel barriers. Indeed, a mesoscopic model of tunneling through an inhomogeneous AlO$_x$ barrier predicts contributions from higher Josephson harmonics of several %. By including these in the transmon Hamiltonian, we obtain orders of magnitude better agreement between the computed and measured energy spectra. The measurement of Josephson harmonics in the C$\varphi$R of standard tunnel junctions prompts a reevaluation of current models for superconducting hardware and it offers a highly sensitive probe towards optimizing tunnel barrier uniformity.

The implementation of scalable processing workflows is essential to improve the access to and analysis of the vast amount of high-resolution and multi-source Remote Sensing (RS) data and to provide decision-makers with timely and valuable information. The Modular Supercomputing Architecture (MSA) systems that are operated by the Jülich Supercomputing Centre (JSC) are a concrete solution for data-intensive RS applications that rely on big data storage and processing capabilities. To meet the requirements of applications with more complex computational tasks, JSC plans to connect the High Performance Computing (HPC) systems of its MSA environment to different quantum computers via the Jülich UNified Infrastructure for Quantum computing (JUNIQ). The paper describes this unique computing environment and highlights its potential to address real RS application scenarios through high-performance and hybrid quantum-classical processing workflows.

Frontiers in physics 10, 907160 (2022). doi:10.3389/fphy.2022.907160 special issue: "Quantum Computing and Machine Learning", Published by Frontiers Media, Lausanne

Solving combinatorial optimization problems of the kind that can be codified by quadratic unconstrained binary optimization (QUBO) is a promising application of quantum computation. Some problems of this class suitable for practical applications such as the traveling salesman problem (TSP), the bin packing problem (BPP), or the knapsack problem (KP) have inequality constraints that require a particular cost function encoding. The common approach is the use of slack variables to represent the inequality constraints in the cost function. However, the use of slack variables considerably increases the number of qubits and operations required to solve these problems using quantum devices. In this work, we present an alternative method that does not require extra slack variables and consists of using an unbalanced penalization function to represent the inequality constraints in the QUBO. This function is characterized by larger penalization when the inequality constraint is not achieved than when it is. We evaluate our approach on the TSP, BPP, and KP, successfully encoding the optimal solution of the original optimization problem near the ground state cost Hamiltonian. Additionally, we employ D-Wave Advantage and D-Wave hybrid solvers to solve the BPP, surpassing the performance of the slack variables approach by achieving solutions for up to 29 items, whereas the slack variables approach only handles up to 11 items. This new approach can be used to solve combinatorial problems with inequality constraints with a reduced number of resources compared to the slack variables approach using quantum annealing or variational quantum algorithms., Comment: 13 pages, 16 figures

Quantum Computing (QC) describes a new way of computing based on the principles of quantum mechanics. From a High Performance Computing (HPC) perspective, QC needs to be integrated: at a system level, where quantum computer technologies need to be integrated in HPC clusters; at a programming level, where the new disruptive ways of programming devices call for a full hardware-software stack to be built; at an application level, where QC is bound to lead to disruptive changes in the complexity of some applications so that compute-intensive or intractable problems in the HPC domain might become tractable in the future. The White Paper QC for HPC focuses on the technology integration of QC in HPC clusters, gives an overview of the full hardware-software stack and QC emulators, and highlights promising customised QC algorithms for near-term quantum computers and its impact on HPC applications. In addition to universal quantum computers, we will describe non-universal QC where appropriate. Recent research references will be used to cover the basic concepts. Thetarget audience of this paper is the European HPC community: members of HPC centres, HPC algorithm developers, scientists interested in the co-design for quantum hardware, benchmarking, etc., {"references":["Anguita, Davide, Sandro Ridella, Fabio Rivieccio, and Rodolfo Zunino. 2003. \"Quantum optimization for training support vector machines.\" Neural Networks 16 (5-6): 763-770. doi:https://doi.org/10.1016/S0893-6080(03)00087-X.","Anschuetz, Eric R., Jonathan P. Olson, Alán Aspuru-Guzik, and Yudong Cao. 2018. \"Variational Quantum Factoring.\" https://arxiv.org/abs/1808.08927.","Atos. n.d. \"Q-Score: measure what truly matters.\" Accessed 2021. https://atos.net/en/solutions/q-score.","Bichsel, Benjamin, Maximilian Baader, and Timon Gehr. 2020. \"Silq: a high-level quantum language with safe uncomputation and intuitive semantics.\" PLDI 2020: Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation. 286-300. doi:https://doi.org/10.1145/3385412.3386007.","Bitkom. n.d. \"Bitkom-Übersicht Deutsches Quanten-Ökosystem v1.1.\" Accessed 2021. https://www.bitkom.org/sites/default/files/2021-03/deutsches_quanten-okosystem_v1.1_public.pdf.","Bobier, Jean-François, Matt Langione, Edward Tao, and Antoine Gourévitch. 2021. \"What Happens When 'If' Turns to 'When' in Quantum Computing?\" BCG. 21 07. https://www.bcg.com/fr-fr/publications/2021/building-quantum-advantage.","Bravo-Prieto, Carlos, Ryan LaRose, M. Cerezo, Yigit Subasi, Lukasz Cincio, and Patrick J. Coles. 2020. \"Variational Quantum Linear Solver.\" https://arxiv.org/abs/1909.05820.","Britt, Keith A., and Travis S. Humble. 2017. \"High-Performance Computing with Quantum Processing Units.\" ACM Journal on Emerging Technologies in Computing Systems 13 (3): 1-13. doi:https://doi.org/10.1145/3007651.","CEA LETI. n.d. \"Five advantages of silicon spin.\" Accessed 2021. https://www.leti-cea.com/cea-tech/leti/english/Pages/Applied-Research/Strategic-Axes/Quantum-Computing/Fundamental-Advantage-of-Silicon-Spin/Five-advantages-of-silicon-spin.aspx.","Classiq. n.d. https://www.classiq.io/.","Dalyac, Constantin, Loïc Henriet, Emmanuel Jeandel, Wolfgang Lechner, Simon Perdrix, Marc Porcheron, and Margarita Veshchezerova. 2021. \"Qualifying quantum approaches for hard industrial optimization problems. A case study in the field of smart-charging of electric vehicles.\" EPJ Quantum Technlogy 8: 12. doi:https://doi.org/10.1140/epjqt/s40507-021-00100-3.","Deutsch, David. 1985. \"Quantum theory, the Church–Turing principle and the universal quantum computer.\" Proceedings of the Royal Society A (Royal Society) 400 (1818). doi:https://doi.org/10.1098/rspa.1985.0070.","Devoret, M H, A Wallraff, and J M Martinis. 2004. \"Superconducting Qubits: A Short Review.\" https://arxiv.org/abs/cond-mat/0411174.","Farhi, Edward, and Hartmut Neven. 2018. \"Classification with Quantum Neural Networks on Near Term Processors.\" https://arxiv.org/abs/1802.06002.","Farhi, Edward, Jeffrey Goldstone, and Sam Gutmann. 2014. \"A Quantum Approximate Optimization Algorithm.\" https://arxiv.org/abs/1411.4028.","Google Quantum AI. n.d. \"Cirq.\" Accessed 2021. https://quantumai.google/cirq.","Grant, Erica, Travis S. Humble, and Benjamin Stump. 2021. \"Benchmarking Quantum Annealing Controls with Portfolio Optimization.\" Physical Review Applied 15 (1): 014012. doi:https://doi.org/10.1103/PhysRevApplied.15.014012. Häffner, H., C.F. Roos, and R. Blatt. 2008. \"Quantum computing with trapped ions.\" Physics Reports (Elsevier) 469 (4): 155-203. doi:https://doi.org/10.1016/j.physrep.2008.09.003.","Häffner, H., C.F. Roos, and R. Blatt. 2008. \"Quantum computing with trapped ions.\" Physics Reports (Elsevier) 469 (4): 155-203. doi:https://doi.org/10.1016/j.physrep.2008.09.003.","Henriet, Loïc, Lucas Beguin, Adrien Signoles, Thierry Lahaye, Antoine Browaeys, Georges-Olivier Reymond, and Christophe Jurczak. 2020. \"Quantum computing with neutral atoms.\" Quantum 4: 327. doi:https://doi.org/10.22331/q-2020-09-21-327.","IBM. n.d. \"IBM's roadmap for scaling quantum technology.\" Accessed 2021. https://research.ibm.com/blog/ibm-quantum-roadmap.","Kitaev, A. Yu. 1995. \"Quantum measurements and the Abelian Stabilizer Problem.\" Electronic Colloquium on Computational Complexity (ECCC). https://arxiv.org/abs/quant-ph/9511026.","Kurek, Michel. 2020. \"Technologies quantiques: vers la seconde révolution.\" https://www.researchgate.net/publication/350521248_TECHNOLOGIES_QUANTIQUES_VERS_LA_SECONDE_REVOLUTION.","Lloyd, Seth, Masoud Mohseni, and Patrick Rebentrost. 2013. \"Quantum algorithms for supervised and unsupervised machine learning.\" https://arxiv.org/abs/1307.0411.","Lucas, Andrew. 2014. \"Ising formulations of many NP problems.\" Frontiers in Physics 2: 5. doi:https://doi.org/10.3389/fphy.2014.00005.","Martiel, Simon, Thomas Ayral, and Cyril Allouche. 2021. \"Benchmarking Quantum Coprocessors in an Application-Centric, Hardware-Agnostic, and Scalable Way.\" IEEE Transactions on Quantum Engineering 2. doi:https://doi.org/10.1109/TQE.2021.3090207.","Peruzzo, Alberto, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J. Love, Alán Aspuru-Guzik, and Jeremy L. O'Brien. 2014. \"A variational eigenvalue solver on a photonic quantum processor.\" Nature Communications 5: 4213. doi:https://doi.org/10.1038/ncomms5213.","Pulser. n.d. Accessed 2021. https://pulser.readthedocs.io.","Qiskit. n.d. Accessed 2021. https://qiskit.org/.","Quantum Computing Report. n.d. \"Players.\" Accessed 2021. https://quantumcomputingreport.com/players/.","Sarkar, Aritra, Zaid Al-Ars, and Koen Bertels. 2021. \"QuASeR: Quantum Accelerated de novo DNA sequence reconstruction.\" PLoS ONE 16 (4). doi:https://doi.org/10.1371/journal.pone.0249850.","Veldhorst, M., H. G. J. Eenink, C. H. Yang, and A. S. Dzurak. 2017. \"Silicon CMOS architecture for a spin-based quantum computer.\" Nature Communications 8: 1766. doi:https://doi.org/10.1038/s41467-017-01905-6.","Wang, Chi, Huo Chen, and Edmond Jonckheere. 2016. \"Quantum versus simulated annealing in wireless interference network optimization.\" Scientific Reports 6: 25797. doi:https://doi.org/10.1038/srep25797."]}

Quantum Computing (QC) describes a new way of computing based on the principles of quantum mechanics. From a High Performance Computing (HPC) perspective, QC needs to be integrated: at a system level, where quantum computer technologies need to be integrated in HPC clusters; at a programming level, where the new disruptive ways of programming devices call for a full hardware-software stack to be built; at an application level, where QC is bound to lead to disruptive changes in the complexity of some applications so that compute-intensive or intractable problems in the HPC domain might become tractable in the future. The White Paper QC for HPC focuses on the technology integration of QC in HPC clusters, gives an overview of the full hardware-software stack and QC emulators, and highlights promising customised QC algorithms for near-term quantum computers and its impact on HPC applications. In addition to universal quantum computers, we will describe non-universal QC where appropriate. Recent research references will be used to cover the basic concepts. Thetarget audience of this paper is the European HPC community: members of HPC centres, HPC algorithm developers, scientists interested in the co-design for quantum hardware, benchmarking, etc.

We study the performance of quantum annealing for two sets of problems, namely, 2-satisfiability (2-SAT) problems represented by Ising-type Hamiltonians, and nonstoquastic problems which are obtained by adding extra couplings to the 2-SAT problem Hamiltonians. In addition, we add to the transverse Ising-type Hamiltonian used for quantum annealing a third term, the trigger Hamiltonian with ferromagnetic or antiferromagnetic couplings, which vanishes at the beginning and end of the annealing process. We also analyze some problem instances using the energy spectrum, average energy or overlap of the state during the evolution with the instantaneous low lying eigenstates of the Hamiltonian, and identify some non-adiabatic mechanisms which can enhance the performance of quantum annealing., Comment: 16 pages, 17 figures

We use a massively parallel simulator of a universal quantum computer to benchmark some of the most powerful supercomputers in the world. We find nearly ideal scaling behavior on the Sunway TaihuLight, the K computer, the IBM BlueGene/Q JUQUEEN, and the Intel Xeon based clusters JURECA and JUWELS. On the Sunway TaihuLight and the K computer, universal quantum computers with up to 48 qubits can be simulated by means of an adaptive two-byte encoding to reduce the memory requirements by a factor of eight. Additionally, we discuss an alternative approach to alleviate the memory bottleneck by decomposing entangling gates such that low-depth circuits with a much larger number of qubits can be simulated., This article is a book contribution based on arXiv:1805.04708

In many laboratories, conventional bright-field transmission microscopes are available to study the structure and organization principles of fibrous tissue samples, but they usually provide only 2D information. To access the third (out-of-plane) dimension, more advanced techniques are employed. An example is 3D Polarized Light Imaging (3D-PLI), which measures the birefringence of histological brain sections to derive the spatial nerve fiber orientations. Here, we show how light scattering in transmission microscopy measurements can be leveraged to gain 3D structural information about fibrous tissue samples like brain tissue. For this purpose, we developed a simulation framework using finite-difference time-domain (FDTD) simulations and high performance computing, which can easily be adapted to other microscopy techniques and tissue types with comparable fibrous structures (e.g., muscle fibers, collagen, or artificial fibers). As conventional bright-field transmission microscopy provides usually only 2D information about tissue structures, a three-dimensional reconstruction of fibers across several sections is difficult. By combining our simulations with experimental studies, we show that the polarization-independent transmitted light intensity (transmittance) contains 3D information: We demonstrate in several experimental studies on brain sections from different species (rodent, monkey, human) that the transmittance decreases significantly (by more than 50%) with the increasing out-of-plane angle of the nerve fibers. Our FDTD simulations show that this decrease is mainly caused by polarization-independent light scattering in combination with the finite numerical aperture of the imaging system. This allows to use standard transmission microscopy techniques to obtain 3D information about the fiber inclination and to detect steep fibers, without need for additional measurements., Comment: 33 pages, 19 figures

A revised version of the massively parallel simulator of a universal quantum computer, described in this journal eleven years ago, is used to benchmark various gate-based quantum algorithms on some of the most powerful supercomputers that exist today. Adaptive encoding of the wave function reduces the memory requirement by a factor of eight, making it possible to simulate universal quantum computers with up to 48 qubits on the Sunway TaihuLight and on the K computer. The simulator exhibits close-to-ideal weak-scaling behavior on the Sunway TaihuLight,on the K computer, on an IBM Blue Gene/Q, and on Intel Xeon based clusters, implying that the combination of parallelization and hardware can track the exponential scaling due to the increasing number of qubits. Results of executing simple quantum circuits and Shor's factorization algorithm on quantum computers containing up to 48 qubits are presented., Comment: Substantially rewritten + new data. Published in Computer Physics Communication

We present a detailed analysis of the set theoretical proof of Wigner for Bell type inequalities with the following result. Wigner introduced a crucial assumption that is not related to Einstein's local realism, but instead, without justification, to the existence of certain joint probability measures for possible and actual measurement outcomes of Einstein-Podolsky-Rosen (EPR) experiments. His conclusions about Einstein's local realism are, therefore, not applicable to EPR experiments and the contradiction of the experimental outcomes to Wigner's results has no bearing on the validity of Einstein's local realism., Comment: Revised Table 1 (after publication)

Counterfactual definiteness must be used as at least one of the postulates or axioms that are necessary to derive Bell-type inequalities. It is considered by many to be a postulate that is not only commensurate with classical physics (as for example Einstein's special relativity), but also separates and distinguishes classical physics from quantum mechanics. It is the purpose of this paper to show that Bell's choice of mathematical functions and independent variables implicitly includes counterfactual definiteness and reduces the generality of the physics of Bell-type theories so significantly that no meaningful comparison of these theories with actual Einstein-Podolsky-Rosen experiments can be made.

John Bell is generally credited to have accomplished the remarkable "proof" that any theory of physics, which is both Einstein-local and "realistic" (counterfactually definite), results in a strong upper bound to the correlations that are measured in space and time. He thus predicts that Einstein-Podolsky-Rosen experiments cannot violate Bell- type inequalities. We present a counterexample to this claim, based on discrete-event computer simulations. Our model-results fully agree with the predictions of quantum theory for Einstein-Podolsky-Rosen-Bohm experiments and are free of the detection- or a coincidence-loophole.

It is demonstrated that the third-order interference, as obtained from explicit solutions of Maxwell's equations for realistic models of three-slit devices, including an idealized version of the three-slit device used in a recent three-slit experiment with light (U. Sinha et al., Science 329, 418 (2010)), is generally nonzero. The hypothesis that the third-order interference should be zero is shown to be fatally flawed because it requires dropping the one-to-one correspondence between the symbols in the mathematical theory and the different experimental configurations., Comment: Replaced Figs. 4,5 and caption of Fig.4