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ACM Great Lakes Symposium on VLSI (GLSVLSI)

Closing the Gap between Quantum Algorithms and Machines with Hardware-Software Co-Design

Frederic T. Chong

Published May 31, 2023• 0 citations
Publisher

Abstract

Quantum computing is at an inflection point, where 127-qubit machines are deployed, and 1000-qubit machines are only a few years away. These machines have the potential to fundamentally change our concept of what is computable and demonstrate practical applications in areas such as quantum chemistry, optimization, and quantum simulation.
DOI
10.1145/3583781.3592462

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Quantum computing is at an inflection point, where 50-qubit (quantum bit) machines have been built, 100-qubit machines are just around the corner, and even 1000-qubit machines are perhaps only a few years away. These machines have the potential to fundamentally change our concept of what is computable and demonstrate practical applications in areas such as quantum chemistry, optimization, and quantum simulation. Yet a significant resource gap remains between practical quantum algorithms and real machines. There is an urgent shortage of the necessary computer scientists to work on software and architectures to close this gap. I will outline several grand research challenges in closing this gap, including programming language design, software and hardware verification, defining and perforating abstraction boundaries, cross-layer optimization, managing parallelism and communication, mapping and scheduling computations, reducing control complexity, machine-specific optimizations, learning error patterns, and many more. I will also describe the resources and infrastructure available for starting research in quantum computing and for tackling these challenges.
Quantum computing is at an inflection point, where 127-qubit (quantum bit) machines are deployed, and 1000-qubit machines are perhaps only a few years away.These machines have the potential to fundamentally change our concept of what is computable and demonstrate practical applications in areas such as quantum chemistry, optimization, and quantum simulation.Yet a significant resource gap remains between practical quantum algorithms and real machines.A promising approach to closing this gap is to design architectural interfaces that selectively expose to programming languages and compilers some of the key physical properties of emerging quantum technologies.I will describe some of our recent work that focuses on techniques that break traditional abstractions, including compiling programs directly to analog control pulses, adapting programs for machine variations, computing with ternary quantum bits, efficient error correction using 2.5D structures, and designing just enough tunability in qubits to avoid crosstalk.
Quantum computing is at an inflection point, where 53-qubit (quantum bit) machines are deployed, 100qubit machines are just around the corner, and even 1000-qubit machines are perhaps only a few years away.These machines have the potential to fundamentally change our concept of what is computable and demonstrate practical applications in areas such as quantum chemistry, optimization, and quantum simulation.Yet a significant resource gap remains between practical quantum algorithms and real machines.A promising approach to closing this gap is to selectively expose to programming languages and compilers some of the key physical properties of emerging quantum technologies.I will describe some of our recent work that focuses on compilation techniques that break traditional abstractions, including compiling directly to analog control pulses, compiling for machine variations, and compiling with ternary quantum bits.
Quantum computers are domain-specific accelerators that can solve commercially important problems that are beyond the capability of conventional computers. Quantum computing is at an inflection point where small systems with a few dozen qubits have been demonstrated and the number of qubits is expected to increase to several hundred over the coming years. However, the qubit quality is likely to remain quite low making it difficult to execute programs reliably. Furthermore, scaling the designs to a large number of qubits would require specialized hardware that operates at cryogenic conditions. This article describes the compiler and hardware support for enabling reliable and scalable quantum computers.
Quantum computing, as an emerging computing paradigm, is expected to tackle problems such as quantum chemistry, optimization, quantum chemistry, information security, and artificial intelligence, which are intractable with using classical computing. Quantum computing hardware and software continue to develop rapidly, but they are not expected to realize universal quantum computation in the next few years. Therefore, the use of quantum hardware to solve practical problems in the near term has become a hot topic in the field of quantum computing. Exploration of the applications of near-term quantum hardware is of great significance in understanding the capability of quantum hardware and promoting the practical process of quantum computing. Hybrid quantum-classical algorithm (also known as variational quantum algorithm) is an appropriate model for near-term quantum hardware. In the hybrid quantum-classical algorithm, classical computers are used to maximize the power of quantum devices. By combining quantum computing with machine learning, the hybrid quantum-classical algorithm is expected to achieve the first practical application of quantum computation and play an important role in the studying of quantum computing. In this review, we introduce the framework of hybrid quantum-classical algorithm and its applications in quantum chemistry, quantum information, combinatorial optimization, quantum machine learning, and other fields. We further discuss the challenges and future research directions of the hybrid quantum-classical algorithm.