Advancing Chemistry and Quantum Information Science An Assessment of Research Opportunities at the Interface of Chemistry and Quantum Information Science in the United States (2023) / Chapter Skim
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From page 149... ...
To mitigate these steep scalings, attention has turned toward using embedding algorithms, which partition a system into a region that is treated with near-exact levels of theory and a surrounding region that is treated with less accuracy. In the context of quantum information, high-accuracy regions may, for example, be placed around transition metal or lanthanide atoms that host different spin states, while surrounding organic groups may be treated with lower levels of theory.
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The high cost to use classical computational tools for modeling quantum phenomena is inhibiting key research activities such as direct simulation of large quantum information processors. 4.2.3d Validating the Modeling of Quantum Information Systems The synergy between experiment and theory can help uncover the molecular and electronic structure that can further develop and improve qubits.
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In the case of QIS applications, both theoretical and experimental data, including calculations of decoherence times and entanglement, spectra, and structures, are invaluable not only for validating one another but for facilitating mutual method development. Some of these QIS-relevant data can be harvested from other existing molecular and materials databases, and such efforts to aggregate data have to be undertaken and supported.
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4.3 MODELING CHEMISTRY PROBLEMS USING QUANTUM COMPUTERS Up to this point, the discussions have focused on the current challenges of and progress made with classical computational tools to study complex quantum systems (e.g., qubits) and large molecules.
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4.3.2 Studying How Quantum Computing Algorithms for Dynamics Can Be Used to Accelerate Chemistry and Spectroscopy The most promising problems to solve on quantum computers are electronic structures, which can have exponential computational complexity and for which there are several known quantum algorithms to provide exponential speedup. Electronic structure is the foundation for understanding chemical properties and reactivities.
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From page 154... ...
Thus, the highaccuracy treatment of strong correlation offered by quantum computers is relevant for understanding mechanisms in inorganic catalysis, for example. Solving the modern electronic structure problem will provide deeper insight into the properties and behaviors of other strongly correlated systems, such as transition metal and heavy metal complexes and biradicals.
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Quantum computing holds promise to accomplish this goal, provided that a sufficiently large quantum computer can be built. The most studied approach to solving the electronic structure problem is quantum phase estimation (QPE)
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Examples of quantum data include wave function output by quantum simulations as well as data that are obtained from quantum sensors and then transduced to quantum computers. Section 4.2.3a discusses using classical computers to simulate "quantum data"; however, this technique is limited in providing "enough" data due to computational power and cost.
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This direction has many synergies with molecular sensors and molecular qubits in the context of quantum signal transduction, and both technologies would likely need to be further developed to make this a reality. 4.3.3 Developing More Efficient Quantum Algorithms for Fault-Tolerant Quantum Computers to Simulate Molecular Systems Quantum simulations, like classical theory, are facing accuracy issues in predicting large molecules.
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From page 158... ...
Because the architecture of quantum computers is still premature, the technology is understandably plagued with defects that ultimately cause data to be highly erroneous. Having an understanding of the basic architecture of quantum computers will help the chemist determine which types of chemistry problems will be suitable for a quantum computer versus a classical computer.
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Thus, quantum error correction is based on the idea that if quantum information is encoded in global topological properties of quantum states, it will be robust to errors. A well-developed theory of FTQC now exists.
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On the other hand, this challenge presents a grand and fascinating opportunity for using quantum computers to model future versions of themselves -- and other quantum information devices. Although modern quantum computers are inherently noisy, they have shown substantial speedups in determining the properties and dynamics of a wide array of quantum systems, including popular tight-binding models, small molecules, and simple materials.
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2008. "Fault-Tolerant Quantum Computation Against Biased Noise." Physical Review A 78(5)
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2022. "Enhancing Spin Coherence in Optically Addressable Molecular Qubits through Host-Matrix Control." Physical Review X 12(3)
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2020. "Molecular Nanomagnets as Qubits with Embedded Quantum-Error Correction." Physical Review Letters 11(20)
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2017. "Operating Quantum States in Single Magnetic Molecules: Implementation of Grover's Quantum Algorithm." Physical Review Letters 119(18)
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2018. "Strain Annealing of SiC Nanoparticles Revealed Through Bragg Coherent Diffraction Imaging for Quantum Technologies." Physical Review Materials 2(8)
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2011. "Molecular Prototypes for Spin-Based CNOT and SWAP Quantum Gates." Physical Review Letters 107(11)
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2005. "Decoherence in Josephson Qubits from Dielectric Loss." Physical Review Letters 95(21)
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2022. "Mapping Renormalized Coupled Cluster Methods to Quantum Computers through a Compact Unitary Representation of Nonunitary Operators." Physical Review Research 4(4)
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2018. "Ultrahigh Error Threshold for Surface Codes with Biased Noise." Physical Review Letters 120(5)
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2020. "Direct Com parison of Many-Body Methods for Realistic Electronic Hamiltonians." Physical Review X 10(1)
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From page 171... ...
• Traditional industry players, including those involved in the chemical sciences, are increasingly partnering with quantum computing companies. • Continued and future research and development at the intersection of QIS and chemistry holds strong potential to transform science and technology in the private sector and foster economic development.
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5.1 GETTING THE SCIENCE RIGHT FOR QIS AND CHEMISTRY STUDENTS As highlighted at the beginning of this report, the United States has made significant progress in developing a few key quantum technologies, such as atomic clocks, electric field sensors, and superconducting qubits. This acceleration can be attributed to the support of the National Quantum Initiative Act (NQIA)
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The following recommendation discusses the specific actors and activities needed at a broad level to strengthen the chemistry–QIS workforce. 5.2 QIS AND CHEMISTRY EDUCATION DEVELOPMENT Ensuring a continued knowledge and expertise base that is familiar with QIS requires preparing students and workers at various educational and skill set levels.
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5.2.1 Preparing Curricular Resources Related to Chemistry Concepts Guided by QIS Principles for K–12 and Undergraduate Educators K–12 educators have many responsibilities, from academic instruction, to oversight, to fostering student social development and interaction. And to task them with the additional expectation to create specialized curricula and concepts (e.g., quantum chemistry, quantum mechanics, quantum algorithms)
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The Quantum Information Science and Technology Workforce Development National Strategic Plan (see Raymer and Monroe 2019; Subcommittee on Quantum Information Science of the National Science and Technology Council 2022) outlined four key strategies to advance the quantum information science and technology (QIST)
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The National Quantum Information Science Research Centers have made inroads in reaching talent at the undergraduate, graduate, and postdoctoral levels, and beyond, by hosting workshops and by offering internships, apprenticeships, fellowships, postdoctoral positions, and visiting-faculty appointments. However, the opportunities for developing the skills needed to pursue careers in QIS are largely concentrated at the graduate level, where the percentage of people from historically marginalized communities is low.
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Motivated by her own experience, Sharkey established and leveraged the framework of an L3C to advance educational goals at the intersection of QIS and chemistry. For example, theory and computation, which are often part of the physical chemistry discipline, are invaluable for providing models and predictions to understand experimental observables like chemical processes and properties.
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Although its sole focus is quantum computers, Quantum Futures (based in the United Kingdom) is another organization that adopts different techniques to acquire and connect talent to their optimal nodes inside the ecosystem (Quantum Futures n.d.)
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The closest universal instrument in QIS is tran sient electron paramagnetic resonance (EPR) /electron spin resonance instrumentation.
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Investment in chemistry in QIS instrumentation is integral and congruent for the more funded QIS and quantum computing fields in terms of startups and large industry but currently is not a recognized goal of these programs, which mainly focus only on the end product. Characterization and spectroscopy started in chemistry in QIS could be instrumental for almost every industry, quantum or not, and should be considered in the same funding portfolios where possible.
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More than 50 percent of the public and about 20 percent of the private institutions were two-year degree-granting institutions (National Center for Education Statistics 2021)
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Efforts could be made to ensure that the fields of QIS and chemistry are accessible to people from diverse backgrounds, which would help to ensure that the future workforce is diverse and inclusive. These needs underscore the importance of placing more emphasis on reaching out to nontraditional QIS and chemistry students, particularly from two-year colleges.
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. Similarly, the Quantum Systems Accelerator offers new education and workforce development programs to provide immediate retraining and to feed the pipeline (Quantum Systems Accelerator n.d.)
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Box 5-3 highlights how the semiconductor industry has a strong overlap with the quantum space in terms of skill sets required of the workforce and is thus a potential area from which to recruit QIS and chemistry candidates.
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Additionally, the development of QIS is connected with the semiconductor industry's research to overcome the computing power limitations of Moore's law. As illustrated in some examples in Appendix C, academic and government researchers have made advances in quantum computing by working in tandem or in partnership with the semiconductor industry.
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. Understanding the theory of such methods fits within the traditional physical chemistry major and can prepare students to further develop, combine, or improve analytical instruments.
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Staff scientists are uncommon, except for some specialized positions such as instrument facility management. In government laboratories, the typical researchers are staff scientists at senior and assistant levels.
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