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IEEE International Symposium on Circuits and Systems proceedings

Automated Synthesis of Quantum Circuits Using Symbolic Abstractions and Decision Procedures

Alvaro Velasquez, Sumit Kumar Jha, Rickard Ewetz, Susmit Jha

Published Apr 27, 2021• 5 citations
Publisher

Abstract

Quantum algorithms are notoriously hard to design and require significant human ingenuity and insight. We present a new methodology called Quantum Automated Synthesizer (QUASH) that can automatically synthesize quantum circuits using decision procedures that perform symbolic reasoning for combinatorial search. Our automated synthesis approach constructs finite symbolic abstract models of the quantum gates automatically and discovers a quantum circuit as a composition of quantum gates using these symbolic models. Our key insight is that most current quantum algorithms work on a finite number of classical inputs, and hence, their correctness proof relies only on reasoning about a finite set of quantum states that can be represented using finite symbolic systems. We demonstrate the potential of our approach by automatically synthesizing four quantum circuits and re-discovering the Bernstein-Vazirani quantum algorithm using state-of-the-art decision procedures. Our synthesis approach only requires distinguishing between a finite set of symbolic quantum states; for example, the synthesis of the Bernstein-Vazirani quantum algorithm only requires reasoning about the following qubit states: |0, |1, -i|0, i|1, |+, |-, e1/2|1i, eiπ/4|1 and a remaining symbolic state representing all other possible quantum states. Our approach leverages decision procedures and theorem provers to assist in the discovery of new quantum algorithms and is a step towards the automation of quantum algorithm design.

DOI
10.1109/iscas51556.2021.9401587

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Quantum algorithms are notoriously hard to design and require significant human ingenuity and insight. We present a new methodology called Quantum Automated Synthesizer (QUASH) that can automatically synthesize quantum circuits using decision procedures that perform symbolic reasoning for combinatorial search. Our automated synthesis approach constructs finite symbolic abstract models of the quantum gates automatically and discovers a quantum circuit as a composition of quantum gates using these symbolic models. Our key insight is that most current quantum algorithms work on a finite number of classical inputs, and hence, their correctness proof relies only on reasoning about a finite set of quantum states that can be represented using finite symbolic systems. We demonstrate the potential of our approach by automatically synthesizing four quantum circuits and re-discovering the Bernstein-Vazirani quantum algorithm using state-of-the-art decision procedures. Our synthesis approach only requires distinguishing between a finite set of symbolic quantum states; for example, the synthesis of the Bernstein-Vazirani quantum algorithm only requires reasoning about the following qubit states: |0, |1, -i|0, i|1, |+, |-, e1/2|1i, eiπ/4|1 and a remaining symbolic state representing all other possible quantum states. Our approach leverages decision procedures and theorem provers to assist in the discovery of new quantum algorithms and is a step towards the automation of quantum algorithm design.
While the ability to build quantum computers is improving dramatically, developing quantum algorithms is very limited and relies on human insight and ingenuity. Although several quantum programming languages have been developed, it is challenging for software developers unfamiliar with quantum computing to learn and use these languages. It is, therefore, necessary to develop tools to support developing new quantum algorithms and programs automatically. This paper proposes AutoQC, an approach to automatically synthesizing quantum circuits using the neural network from input and output pairs. We consider a quantum circuit a sequence of quantum gates and synthesize a quantum circuit probabilistically by prioritizing through a neural network at each step. The experimental results highlight the ability of AutoQC to synthesize some essential quantum circuits at a lower cost.
Abstract Despite rapid progress in the field, it is still challenging to discover new ways to leverage quantum computation: all quantum algorithms must be designed by hand, and quantum mechanics is notoriously counterintuitive. In this paper, we study how artificial intelligence, in the form of program synthesis, may help overcome some of these difficulties, by showing how a computer can incrementally learn concepts relevant to quantum circuit synthesis with experience, and reuse them in unseen tasks. In particular, we focus on the decomposition of unitary matrices into quantum circuits, and show how, starting from a set of elementary gates, we can automatically discover a library of useful new composite gates and use them to decompose increasingly complicated unitaries.
While the ability to build quantum computers is improving dramatically, developing quantum algorithms is limited and relies on human insight and ingenuity. Although a number of quantum programming languages have been developed, it is challenging for software developers who are not familiar with quantum computing to learn and use these languages. It is, therefore, necessary to develop tools to support developing new quantum algorithms and programs automatically. This paper proposes AutoQC, an approach to automatically synthesizing quantum circuits using the neural network from input and output pairs. We consider a quantum circuit a sequence of quantum gates and synthesize a quantum circuit probabilistically by prioritizing with a neural network at each step. The experimental results highlight the ability of AutoQC to synthesize some essential quantum circuits at a lower cost.
Quantum circuit verification is a critical yet notoriously challenging task in quantum computing. Full simulation of quantum circuits scales exponentially with the number of qubits and circuit depth, rendering it impractical for even moderately sized circuits. This paper proposes a novel approach to automated theorem proving for quantum circuit verification leveraging abstract interpretation. Our core idea is to approximate the behavior of quantum circuits using abstract interpretations, significantly reducing the computational complexity of the verification process. We outline a framework where abstract domains are constructed to represent the key properties of quantum circuits, such as qubit state fidelities and gate operations. The framework then employs logical reasoning and inference rules based on these abstract representations to automatically determine the correctness of a circuit against a given specification. We demonstrate the feasibility of this approach and discuss its potential to dramatically improve the efficiency and scalability of quantum circuit verification. The key benefit lies in shifting the verification burden from exact simulation to a more tractable abstract analysis.
With the rapid development of quantum computing, automatic verification of quantum circuits becomes more and more important. While several decision diagrams (DDs) have been introduced in quantum circuit simulation and verification, none of them supports symbolic computation. Algorithmic manipulations of symbolic objects, however, have been identified as crucial, if not indispensable, for several verification tasks. This paper proposes the first decision-diagram approach for operating symbolic objects and verifying quantum circuits with symbolic terms. As a notable example, our symbolic tensor decision diagrams (symbolic TDD) could verify the functionality of the 160-qubit quantum Fourier transform circuit within three minutes. Moreover, as demonstrated on Bernstein-Vazirani algorithm, Grover's algorithm, and the bit-flip error correction code, the symbolic TDD enables efficient verification of quantum circuits with user-supplied oracles and/or classical controls.
We apply numerical optimization and linear algebra algorithms for classical computers to the problem of automatically synthesizing algorithms for quantum computers. Using our framework, we apply several common techniques from these classical domains and numerically examine their suitability for and performance on this problem. Our methods are evaluated on single-qubit systems as well as on larger systems. While the first part of our proposed method outputs a single unitary matrix representing the composite effects of a quantum circuit or algorithm, we use existing tools—and assess the performance of these—to factor such a matrix into a product of elementary quantum gates. This enables our pipeline to be truly end-to-end: starting from desired input/output examples, our code ultimately results in a quantum circuit diagram. The code implementation is publicly available at https://github.com/DABH/qule .