Quantum Computer Systems for Scientific Discovery (1912.07577v3)

Abstract: The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications. We argue that these two challenges should be considered together, by co-designing full-stack quantum computer systems along with their applications in order to hasten their development and potential for scientific discovery. In this context, we identify scientific and community needs, opportunities, a sampling of a few use case studies, and significant challenges for the development of quantum computers for science over the next 2--10 years. This document is written by a community of university, national laboratory, and industrial researchers in the field of Quantum Information Science and Technology, and is based on a summary from a U.S. National Science Foundation workshop on Quantum Computing held on October 21--22, 2019 in Alexandria, VA.

• The paper presents a co-design framework integrating quantum hardware development with application discovery to accelerate scientific breakthroughs.
• The paper identifies algorithmic complexity and technical obstacles, such as qubit control and error correction, as major challenges in quantum computing.
• The paper details a multi-layer quantum computer stack and provides case studies in simulation and optimization to illustrate practical applications.

Quantum Computer Systems for Scientific Discovery: A Synopsis

The reviewed paper discusses a comprehensive approach to developing quantum computer systems, emphasizing the intertwined challenges of their construction and application discovery. The authors propose a co-design strategy that integrates the development of quantum computer systems with their scientific applications to expedite the realization of their capabilities in scientific discovery.

Dual Challenges of Quantum Computing

Quantum computing holds the promise of solving complex problems far beyond the reach of classical computation due to its inherent quantum properties such as superposition and entanglement. However, the advancement of quantum computing is hindered by two major challenges:

1. Algorithmic Complexity: While quantum computers can theoretically outperform classical computers, accessing the vast amount of information encoded in quantum states remains problematic due to quantum measurement collapse. Developing robust algorithms that can harness these states effectively is an open question, encompassing broad potential applications in areas like material science and complex optimizations.
2. Technological Hurdles: Building quantum computers involves significant technical challenges, including maintaining coherent superpositions and controlling qubits with high fidelity. These requirements impose stringent conditions, such as low temperatures and precise control environments, making large-scale quantum computers difficult to construct.

Co-Design Approach

The authors advocate for a vertically-integrated co-design framework where quantum hardware development proceeds in tandem with the applications being developed. This approach involves various layers of the quantum computing stack, from the physical qubits and quantum gates to software algorithms and high-level applications, suggesting that scientific opportunities exist at each layer and at their interfaces.

Quantum Computer Stack

• Qubit Platforms: Presently viable qubit technologies include superconducting circuits, trapped ions, and neutral atoms, each with distinct advantages and challenges in terms of gate speed, connectivity, and noise resilience.
• Control Engineering: The complexity of control systems for quantum gates necessitates further advances in precision and scalability, paralleling developments in classical communication technologies.
• Quantum Algorithms: These include provably efficient algorithms like Shor's and Grover's, as well as heuristic approaches such as variational algorithms (e.g., VQE, QAOA) that are relevant for near-term quantum devices.
• Quantum Software: The software stack spans compilers, simulators, and operating systems tailored to optimize and manage quantum computations effectively across the stack.

Case Studies and Use Cases

1. Gate-Based Quantum Simulation: Illustrated using trapped ion systems, quantum simulations aim to model complex quantum systems, leveraging the quantum computer's ability to efficiently explore vast quantum state spaces.
2. Combinatorial Optimization: Utilizing Rydberg atom systems for solving the Maximum Independent Set problem, showcasing quantum advantages in addressing NP-hard problems with novel algorithmic structures like QAOA.
3. Quantum Error Correction: The focus on developing robust error correction strategies to stabilize quantum information against noise is essential for scalable quantum computing.

Future Prospects

The research implies that the creation of Quantum Computing Laboratory User Facilities could bridge the gap between academic research, industrial development, and foundational technology, fostering innovation through collaboration. These facilities would facilitate iterative co-design processes, enhancing both hardware and application domains of quantum computing.

The paper positions quantum computing as a critical tool for scientific advancement, with the anticipated development over the next two to ten years hinging on coordinated efforts across academia, industry, and government sectors. The establishment of dedicated facilities and continuous collaboration are seen as pivotal to sustaining and accelerating progress in the field.