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Report for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking - 2023

Employing quantum mechanical resources in computing and networking opens the door to new computation and communication models and potential disruptive advantages over classical counterparts. However, quantifying and realizing such advantages face extensive scientific and engineering challenges. Investments by the Department of Energy (DOE) have driven progress toward addressing such challenges. Quantum algorithms have been recently developed, in some cases offering asymptotic exponential advantages in speed or accuracy, for fundamental scientific problems such as simulating physical systems, solving systems of linear equations, or solving differential equations. Empirical demonstrations on nascent quantum hardware suggest better performance than classical analogs on specialized computational tasks favorable to the quantum computing systems. However, demonstration of an end-to-end, substantial and rigorously quantifiable quantum performance advantage over classical analogs remains a grand challenge, especially for problems of practical value. The definition of requirements for quantum technologies to exhibit scalable, rigorous, and transformative performance advantages for practical applications also remains an outstanding open question, namely, what will be required to ultimately demonstrate practical quantum advantage?

97 MATHEMATICS AND COMPUTING↗

Report for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking - 2023

Employing quantum mechanical resources in computing and networking opens the door to new computation and communication models and potential disruptive advantages over classical counterparts. However, quantifying and realizing such advantages face extensive scientific and engineering challenges. Investments by the Department of Energy (DOE) have driven progress toward addressing such challenges. Quantum algorithms have been recently developed, in some cases offering asymptotic exponential advantages in speed or accuracy, for fundamental scientific problems such as simulating physical systems, solving systems of linear equations, or solving differential equations. Empirical demonstrations on nascent quantum hardware suggest better performance than classical analogs on specialized computational tasks favorable to the quantum computing systems. However, demonstration of an end-to-end, substantial and rigorously quantifiable quantum performance advantage over classical analogs remains a grand challenge, especially for problems of practical value. The definition of requirements for quantum technologies to exhibit scalable, rigorous, and transformative performance advantages for practical applications also remains an outstanding open question, namely, what will be required to ultimately demonstrate practical quantum advantage?

97 MATHEMATICS AND COMPUTING↗

Computational projects with the Landau–Zener problem in the quantum mechanics classroom

The Landau–Zener problem, where a minimum energy separation is passed with constant rate in a two-state quantum-mechanical system, is an excellent model quantum system for a computational project. It requires a low-level computational effort, but has a number of complex numerical and algorithmic issues that can be resolved through dedicated work. It can be used to teach computational concepts, such as accuracy, discretization, and extrapolation, and it reinforces quantum concepts of time-evolution via a time-ordered product and of extrapolation to infinite time via time-dependent perturbation theory. Additionally, we discuss the concept of compression algorithms, which are employed in many advanced quantum computing strategies, and easy to illustrate with the Landau–Zener problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Advances and opportunities in materials science for scalable quantum computing

We report that by harnessing unique quantum mechanical phenomena, such as superposition and entanglement, quantum computers offer the possibility to drastically outperform classical computers for certain classes of problems. The realization of this potential, however, presents a substantial challenge, because noise and imperfections associated with the materials used to fabricate devices can obscure the delicate quantum mechanical effects that enable quantum computing. Hence, progress in synthesis, characterization, and modeling of materials for quantum computing have driven many exciting advances in recent years and will become increasingly important in the years to come. As progressively more complex, multi-qubit systems come online, and as significant government and industrial investment drives research forward, new challenges and opportunities for materials science continue to emerge. The articles in this issue survey the current state of materials science progress and obstacles for some leading quantum computing platforms; opportunities for deeper involvement by materials scientists abound. Ultimate realization of the full potential of quantum computers will require a multidisciplinary effort spanning many traditional areas of expertise.

36 MATERIALS SCIENCE↗

Understanding the Origin of Structural Diversity of DNA Double Helix

Deciphering the contribution of DNA subunits to the variability of its 3D structure represents an important step toward the elucidation of DNA functions at the atomic level. In the pursuit of that goal, our previous studies revealed that the essential conformational characteristics of the most populated “canonic” BI and AI conformational families of Watson–Crick duplexes, including the sequence dependence of their 3D structure, preexist in the local energy minima of the elemental single-chain fragments, deoxydinucleoside monophosphates (dDMPs). Those computations have uncovered important sequence-dependent regularity in the superposition of neighbor bases. The present work expands our studies to new minimal fragments of DNA with Watson–Crick nucleoside pairs that differ from canonic families in the torsion angles of the sugar-phosphate backbone (SPB). To address this objective, computations have been performed on dDMPs, cdDMPs (complementary dDMPs), and minimal fragments of SPBs of respective systems by using methods of molecular and quantum mechanics. These computations reveal that the conformations of dDMPs and cdDMPs having torsion angles of SPB corresponding to the local energy minima of separate minimal units of SPB exhibit sequence-dependent characteristics representative of canonic families. In contrast, conformations of dDMP and cdDMP with SPB torsions being far from the local minima of separate SPB units exhibit more complex sequence dependence.

59 BASIC BIOLOGICAL SCIENCES↗

Quinonoid versus Aromatic π-Conjugated Oligomers and Polymers and Their Diradical Characters

Quinonoid elements in π-conjugated oligomers and polymers have promising characteristics by providing unique structures and electronic properties, including varying degrees of ground state open shell diradical character and small band gaps. So far, quinonoid ground state polymers were rarely identified, and one of the goals of this work was to expand the portfolio of such systems in which over 17 different repeat units were considered, about half preferred a quinonoid and half preferred aromatic structures plus several with the alternating A-Q composition were also included. The dependency of the diradical character as a function of size and composition opens a rich space to discover trends affecting the aromaticity and diradical character. Herein, we show that the diradical character is a useful parameter to classify a wide variety of polymers based on their ground states in a systematic study of a large number of homo- and heterosystems. Homo-polymers with quinonoid ground states show a high diradical character, while those with the aromatic ground state behave as closed-shell systems with no or a very small diradical character. The diradical character of quinonoid systems depends strongly on the size, which increases with the size of the oligomer. Here, we found a correlation between the diradical character and the interring C–C distance as well as the singlet–triplet energy gaps. In alternating copolymers mixing quinonoid and aromatic subunits, we found open-shell hetero-polymers with moderate to high diradical characters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Sub-system self-consistency in coupled cluster theory

Here, in this article, we provide numerical evidence indicating that the single-reference coupled-cluster (CC) energies can be calculated alternatively to their copybook definition. We demonstrate that the CC energy can be reconstructed by diagonalizing the effective Hamiltonians describing correlated sub-systems of the many-body system. In the extreme case, we provide numerical evidence that the CC energy can be reproduced through the diagonalization of the effective Hamiltonian describing sub-system composed of a single electron. These properties of the CC formalism can be exploited to design protocols to define effective interactions in sub-systems used as probes to calculate the energy of the entire system and introduce a new type of self-consistency for approximate CC approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The accuracies of effective interactions in downfolding coupled-cluster approaches for small-dimensionality active spaces

Here, this paper evaluates the accuracy of the Hermitian form of the downfolding procedure using the double unitary coupled cluster (DUCC) ansatz on the benchmark systems of linear chains of hydrogen atoms, H6 and H8. The computational infrastructure employs the occupation-number-representation codes to construct the matrix representation of arbitrary second-quantized operators, allowing for the exact representation of exponentials of various operators. The tests demonstrate that external amplitudes from standard single-reference coupled cluster methods that sufficiently describe external (out-of-active-space) correlations reliably parameterize the Hermitian downfolded effective Hamiltonians in the DUCC formalism. The results show that this approach can overcome the problems associated with losing the variational character of corresponding energies in the corresponding SR-CC theories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Artificial-intelligence-driven shot reduction in quantum measurement

Variational Quantum Eigensolver (VQE) provides a powerful solution for approximating molecular ground state energies by combining quantum circuits and classical computers. However, estimating probabilistic outcomes on quantum hardware requires repeated measurements (shots), incurring significant costs as accuracy increases. Optimizing shot allocation is thus critical for improving the efficiency of VQE. Current strategies rely heavily on hand-crafted heuristics requiring extensive expert knowledge. This paper proposes a reinforcement learning (RL)-based approach that automatically learns shot assignment policies to minimize total measurement shots while achieving convergence to the minimum of the energy expectation in VQE. The RL agent assigns measurement shots across VQE optimization iterations based on the progress of the optimization. This approach reduces VQE's dependence on static heuristics and human expertise. When the RL-enabled VQE is applied to a small molecule, a shot reduction policy is learned. The policy demonstrates transferability across systems and compatibility with other wavefunction Ansätze. In addition to these specific findings, this work highlights the potential of RL for automatically discovering efficient and scalable quantum optimization strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

N-Terminal domain homologs of the orange carotenoid protein increase quenching of cyanobacterial phycobilisomes

Stress exerted by excess captured light energy in cyanobacteria is prevented by the photoprotective activity of the orange carotenoid protein (OCP). Under high light, the OCP converts from an orange, inactive form (OCP O ) into the red form (OCP R ) that binds to and quenches the phycobilisome (PBS). Structurally, the OCP consists of 2 domains: the N-terminal effector domain and a C-terminal regulatory domain. Structural analysis of the OCP-PBS complex showed that the N-terminal domains of an OCP dimer interact with the PBS core. These N-terminal OCP domains have single-domain protein paralogs known as helical carotenoid proteins (HCPs). Using PBS quenching assays, we show that the HCP4 and HCP5 homologs efficiently quench PBS fluorescence in vitro, surpassing the quenching ability of the OCP. This is consistent with computational quantum mechanics/molecular mechanics results. Interestingly, when using a maximum quenching concentration of OCP with PBSs, HCP5 addition further increases PBS quenching. Our results provide mechanistic insight into the quenching capacity and roles of HCP4 and HCP5 in cyanobacteria, suggesting that they are more than simply functionally redundant to the OCP.

Sheppard, Damien I.↗

Quantum computing hardware for HEP algorithms and sensing

Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Typical applications range from quantum chemistry to optimization problems and also include simulations for high energy physics. The recent maturing of quantum hardware has triggered preliminary explorations by several institutions (including Fermilab) of quantum hardware capable of demonstrating quantum advantage in multiple domains, from quantum computing to communications, to sensing. The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in quantum computing and sensing, mediating quantum engineering and HEP based material science. The main goal of the Center is to deploy quantum systems with superior performance tailored to the algorithms used in high energy physics. In this Snowmass paper, we discuss the two most promising superconducting quantum architectures for HEP algorithms, i.e. three-level systems (qutrits) supported by transmon devices coupled to planar devices and multi-level systems (qudits with arbitrary N energy levels) supported by superconducting 3D cavities. For each architecture, we demonstrate exemplary HEP algorithms and identify the current challenges, ongoing work and future opportunities. Furthermore, we discuss the prospects and complexities of interconnecting the different architectures and individual computational nodes. Finally, we review several different strategies of error protection and correction and discuss their potential to improve the performance of the two architectures. This whitepaper seeks to reach out to the HEP community and drive progress in both HEP research and QIS hardware.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian Simulation

Quantum computing is a promising technology that harnesses the peculiarities of quantum mechanics to deliver computational speedups for some problems that are intractable to solve on a classical computer. Current generation noisy intermediate-scale quantum (NISQ) computers are severely limited in terms of chip size and error rates. Shallow quantum circuits with uncomplicated topologies are essential for successful applications in the NISQ era. In this work, based on matrix analysis, we derive localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions. The depth of the compressed circuits is independent of simulation time and grows linearly with the number of spins. The proposed numerical circuit compression algorithm behaves backward stable and scales cubically in the number of spins enabling circuit synthesis beyond O(10 3 ) spins. The resulting quantum circuits have a simple nearest-neighbor topology, which makes them ideally suited for NISQ devices.

Hamiltonian simulation↗

Comparison of Quantum Mechanical and Empirical Potential Energy Surfaces and Computed Rate Coefficients for N2 Dissociation

Physics-based modeling of hypersonic flows is predicated on the availability of chemical reaction rate coefficients and cross sections for the collisional processes. This approach has been built around the use of quantum mechanical calculations to describe the interaction between the colliding particles. In this approach a potential energy surface (PES) is computed by solving the electronic Schrödinger equation and collision cross sections are determined for that PES using classical, semiclassical or quantum mechanical scattering methods. The rate coefficients are computed by integrating the thermally weighted cross sections. State-to-state rate coefficients are determined by only integrating over a thermal distribution of collisional energies. Finally, thermal rate coefficients are determined by summation of the state-to-state rate coefficients for reactions of molecules in all relevant ro-vibrational energy levels. If the flow is in thermal non-equilibrium, the translational, vibrational and rotational energy modes can be represented in different ways: three unique temperatures can be used to describe the distributions, the populations of individual ro-vibrational energy levels can be determined by solving the Master Equation, or through the use of direct simulation in particle-based Monte Carlo sampling. The PES-to-rate coefficient approach had been proposed and attempted in the early days of digital computing, but it is only in the last 15 years that computer hardware and software have been up to the task of calculating accurate interatomic and intermolecular potentials.

Jaffe, Richard L.↗

Quantum Analog Computing

Quantum analog computing is based upon similarity between mathematical formalism of quantum mechanics and phenomena to be computed. It exploits a dynamical convergence of several competing phenomena to an attractor which can represent an externum of a function, an image, a solution to a system of ODE, or a stochastic process.

Quantum analog computing quantum mechanics attract↗

Quantum Computation: Entangling with the Future

Commercial applications of quantum computation have become viable due to the rapid progress of the field in the recent years. Efficient quantum algorithms are discovered to cope with the most challenging real-world problems that are too hard for classical computers. Manufactured quantum hardware has reached unprecedented precision and controllability, enabling fault-tolerant quantum computation. Here, I give a brief introduction on what principles in quantum mechanics promise its unparalleled computational power. I will discuss several important quantum algorithms that achieve exponential or polynomial speedup over any classical algorithm. Building a quantum computer is a daunting task, and I will talk about the criteria and various implementations of quantum computers. I conclude the talk with near-future commercial applications of a quantum computer.

Jiang, Zhang↗

Request for Information: Access to Quantum Systems

Quantum science and technology is a focal point of research at Lawrence Livermore National Laboratory (LLNL). Quantum-coherent devices offer the potential for unprecedented precision in sensing and the ability to directly simulate complex quantum phenomena that have no known efficient classical algorithms. Thus, development and implementation of quantum technologies is expected to have a significant impact on our ability to address some of the most complex national security problems. LLNL maintains projects involving a broad spectrum of Quantum Information Science (QIS) research activities including investigating sources of decoherence, the 3DQ microscope, studies into quantum interconnects, 3-D printed ion traps, fusion energy and nuclear physics simulations, modeling for data-starved environments, and quantum sensing, including accelerometry and timing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗