Assessing requirements to scale to practical quantum advantage (2211.07629v1)

Abstract: While quantum computers promise to solve some scientifically and commercially valuable problems thought intractable for classical machines, delivering on this promise will require a large-scale quantum machine. Understanding the impact of architecture design choices for a scaled quantum stack for specific applications, prior to full realization of the quantum system, is an important open challenge. To this end, we develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required across these layers for large-scale quantum applications. Using a tool that implements this framework, we assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage. We identify three qubit parameters, namely size, speed, and controllability, that are critical at scale to rendering these applications practical. A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack, from algorithms to qubits.

• The paper develops a modular framework that quantifies key metrics such as qubit count, error rates, and execution times for scalable quantum computing.
• It applies the framework to applications like quantum dynamics, quantum chemistry, and integer factorization, revealing resource demands from hundreds of logical qubits to billions of operations.
• The study underscores the need for scalable, fast, and compact qubit architectures to bridge experimental achievements with practical quantum advantage.

Assessing Requirements to Scale to Practical Quantum Advantage

The paper provides a comprehensive framework built for estimating the quantum resources needed to achieve practical quantum advantage. This involves moving beyond mere experimental demonstrations with small, noisy qubit systems, to applications where quantum computers can solve significant scientific and commercial problems that are intractable with classical computing technologies.

Resource Framework and Estimation

The authors develop a modular framework that allows researchers to systematically estimate the resources required by large-scale quantum applications. This is crucial for making informed decisions on the architectural design of quantum computers—from algorithms at the high-level programming layers down to the details of the qubit implementation on the hardware side.

The framework, realized in the form of the Azure Quantum Resource Estimator tool, introduces a top-down compilation approach from high-level applications to fault-tolerant logical operations, using the planar quantum instruction set architecture (ISA). It models quantum error correction (QEC) codes and detailed descriptions of error rates, qubit count, and execution times across these layers. By estimating physical resources such as runtime and power consumption, the tool explores a wide landscape of hardware parameters and architecture choices to arrive at a comprehensive picture of what scaling up a quantum computer really requires.

Applications and Implications

Three pivotal applications are examined: quantum dynamics, quantum chemistry, and integer factorization using Shor's algorithm. Each offers insights into differing resource demands:

1. Quantum Dynamics: Even the smallest scientific tasks, deemed intractable classically, require hundreds of logical qubits and operations at a fault-tolerant level. For a 100-spin Ising model simulation, the estimates underscore the necessity of not just more qubits, but extremely low error rates at the logical level.
2. Quantum Chemistry: With applications in understanding catalytic reaction mechanisms, this domain demands intense computational efforts with qubitization and advanced phase-estimation techniques resulting in hundreds of billions of logical operations.
3. Integer Factorization: Factorization of a 2048-bit integer used in cryptography can be achieved with quantum computers equipped with tens of thousands of perfect, noiseless qubits in minutes, but with error-prone qubits, the resource overhead dramatically increases, accentuating the need for quantum error correction.

Technological Requirements for Scaling

The authors extract critical scaling requirements from their extensive analysis, advocating that quantum computers capable of achieving practical advantage will need a qubit architecture that exhibits:

• Controllability: Reliable control over a vast array of qubits with 2D connectivity is mandatory. Fault-tolerant thresholds, set at sub-percent levels for error rates, imply a substantial barrier to surmount with current technologies.
• Speed: Real-world quantum advantage mandates incredibly rapid qubit operation times below a microsecond to ensure applications can run on a feasible timetable, such as within a month.
• Compactness: The physical size of qubits is pivotal. To facilitate parallel operation and control across millions of qubits on a single wafer, each qubit's footprint must be minimized to tens of microns, which presents another engineering challenge.

This research primarily impacts quantum hardware development, emphasizing the need for scalable control systems, faster gates, and compact designs in the engineering of qubits.

Concluding Thoughts

This paper provides a holistic view of the path toward and the requirements of achieving practical quantum advantage. The authors suggest that bridging theory and experiment with such detailed insights into qubit requirements across varied applications establishes a thorough groundwork for the quantum computing community. Consequently, this invites extensive future work in areas like error correction, algorithm optimization, and hardware development, critical to overcoming the identified challenges on the quantum continuum to practical advantage.