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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4 Experimental and Computational Approaches for Scaling Qubit Design and Function
4 Experimental and Computational Approaches for Scaling Qubit Design and Function
Pages 137-170
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From page 137... ...
At the same time, quantum computers have the potential to simulate the dynamics of any chemical process efficiently and prepare ground states of some chemical systems believed to be classically intractable. • The chemistry problems that are most difficult for classical computers, but seem to provide an advantage for quantum computers, are so-called "strongly correlated" systems that tend to involve the breaking of multiple chemical bonds, transition metals, free radicals, or some unusual states of the electrons leading to high entanglement.
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From page 138... ...
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. • Based on current knowledge of quantum algorithms, noisy intermediate-scale quantum computers, quantum annealers, and analog quantum computers are unlikely to solve problems near the classically intractable regime without error correction.
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From page 139... ...
Design and synthesis of chemical environments that are robust to decoherence that can decouple the fundamental degrees of freedom of qubits from unwanted environment-induced noise; 4. Bottom-up and top-down fabrication of heterogeneous material platforms that can transduce quantum information coherently from one chemical environment to another by leveraging surface chemistry and single-digit nanofabrication; and 5.
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From page 140... ...
Next, an overview of architecture-specific challenges to scaling is provided. 4.2.1 Investigating and Controlling the Interactions among Qubits and between Qubits and Their Environments 4.2.1a Chemical Design of Spin in Molecules Several transition metal and f-block elements can serve as nuclear or electronic spin qubits.
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4.2.1c Chemical Design of Superconducting Qubits Chemistry also offers a potential path toward improving the coherence times of superconducting qubits. While currently one of the most scalable platforms (Arute et al.
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. Decoherence for trapped ions is related to both the electric field noise of the laser/trap system and materials-based losses from the electrode surfaces, which are shown to reduce multiqubit FIGURE 4-3 Superconducting qubits.
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, the topological qubit should be inherently robust to certain external perturbations such as defects, strain, and interfaces -- in stark contrast to other qubit platforms -- and does not require complex error correction. However, while these perturbations often do not destroy the topology of the material, they can make identifying reciprocal-space features extremely difficult by inducing mid-gap states.
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From page 144... ...
4.2.1f Designing Hybrid Quantum Architectures That Mutually Enhance Each Other's Quantum Properties Hybrid quantum computers synergistically combine the strengths of classical computing with the opportunities of quantum computing. For instance, a classical optimization algorithm can be used to guide the quantum circuit parameters, while the quantum components solve subtasks more efficiently than would be possible classically.
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From page 145... ...
However, for their practical implementation, hybrid quantum computers must additionally combine the hardware requirements of quantum computers (i.e., scalability, coherence, and low error rates) with traditional classical computing components.
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Developing new techniques that can exploit physical insights to cheaply yet accurately model quantum information systems thus remains both a challenge and an opportunity for the community. Potential ways of addressing this challenge include the use of physically inspired embedding techniques, new ways of more directly and efficiently computing entanglement and modeling decoherence, the curation and more active use of QIS materials databases, and the employment of increasingly available quantum computing resources.
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Key classes of electronic structure methods for QIS and other applications and their estimated scalings in conventional implementations. (right)
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From page 148... ...
is modeled surrounded by a larger portion of the system that is treated using lower-accuracy quantum or classical methods. In the context of quantum information systems, the atoms, vacancies, or complexes that constitute the system's physical qubits are typically modeled with high accuracy, while their hosts are modeled with lower accuracy.
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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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From page 150... ...
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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From page 151... ...
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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From page 152... ...
4.3.1 Identifying Important Open Chemistry Problems for Quantum Computers That Are Unresolved Due to Classically Intractable Electronic Structure While advancements are being made in developing the hardware for quantum computers, many challenges associated with adopting this technology remain (Argüello-Luengo et al.
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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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4.3.2c Exploring How Quantum Machine Learning Can Be Used to Accelerate Chemical Research by Processing Quantum Data from Entangled Sensor Arrays or Quantum Simulations of Chemistry Another widely anticipated application area for quantum computing is machine learning. Quantum machine learning (QML)
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From page 157... ...
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... ...
The standard model of quantum computing, also known as the digital or gate model, involves algo rithms that are instantiated as quantum circuits consisting of a series of quantum logic gates (Barends et al. 2014; Paz-Silva and Lidar 2013; Van Meter and Itoh 2005)
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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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From page 160... ...
• Understand and advance the limits of classical electronic structure algorithms and modeling approaches that can guide the design of molecular or solid-state qubits and scalable quantum architectures. • Leverage and develop machine learning, chemical informatics, chemical databases, and molecular simulations to inform and facilitate qubit design.
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• Study how quantum computing algorithms for dynamics can be used to accelerate chemistry and spectroscopy. • Explore how quantum machine learning can be used to accelerate chemical research by processing quantum data from entangled sensor arrays or quantum simulations of chemistry.
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2019. "Engineering Electronic Structure to Prolong Relaxation Times in Molecular Qubits by Minimising Orbital Angular Momentum." Nature Communications 10(1)
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2019. "Decoherence Benchmark ing of Superconducting Qubits." npj Quantum Information 5:54.
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2022. "Reliably Assessing the Electronic Structure of Cytochrome P450 on Today's Classical Computers and Tomorrow's Quantum Computers." Proceedings of the National Academy of Sciences USA 119(38)
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2022. "Simulating the Electronic Structure of Spin Defects on Quantum Computers." PRX Quantum 3(1)
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2021. "Advances and Opportunities in Materials Science for Scalable Quantum Computing." MRS Bulletin 46(7)
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2020. "Quantum Simulations of Materials on Near-Term Quantum Computers." npj Computational Materials 6(1)
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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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1989. Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory.
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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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