Quantum Program Synthesis: Swarm Algorithms and Benchmarks

Atkinson, Timothy; Karsa, Athena; Drake, John H.; Swan, Jerry

doi:10.1007/978-3-030-16670-0_2

Cited by 11 publications

(14 citation statements)

References 18 publications

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“…Table 3 outlines the computational complexities of the Opg ¤ p ¤logppqq [16] Opg ¤w d q [20] Opu ¤t ¤n c q [25] Op f 2 ¤n¤log f q [31] Opn 2 q [33] Op2 n q [39] Opn 2 q -Opn!q [33], [43] Opr 2 ¤m r 3 ¤bq [50] X n is the number of qubits and gates in a given quantum circuit to be mapped by a QCC method. g and p are the number of generations and population size, respectively, in a GA. w and d are the width and recursion depth of BNF, respectively, used in a GP.…”

Section: Evaluation Of Methods and Future Directionsmentioning

confidence: 99%

“…Finding an optimal path in a graph can be intuitively mapped into an ant biological system (e.g., finding the shortest route to a food source in Figure 7). [25], directed graphs are typical tools to represent a search space. Internal nodes of a directed graph are the basic quantum logical gates, whereas the edges are the connections among gates.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

“…After QAP completes, the most suitable circuit is selected as the solution for QCC mapping. QAP generates better results compared to random search based methods for a set of benchmark problems (e.g., implementation of quantum Fourier Transformation, Grover's diffusion operator and Toffoli gate) [25].…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

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Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics

Kusyk

Saeed

2021

IEEE Trans. Quantum Eng.

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Computationally expensive applications, including machine learning, chemical simulations and financial modeling, are promising candidates for noisy intermediate scale quantum (NISQ) computers. In these problems, one important challenge is mapping a quantum circuit onto NISQ hardware while satisfying physical constraints of an underlying quantum architecture. Quantum circuit compilation (QCC) aims to generate feasible mappings such that a quantum circuit can be executed in a given hardware platform with acceptable confidence in outcomes. Physical constraints of a NISQ computer change frequently, requiring QCC process to be repeated often. When a circuit cannot directly be executed on a quantum hardware due to its physical limitations, it is necessary to modify the circuit by adding new quantum gates and auxiliary qubits, increasing its space and time complexity. An inefficient QCC may significantly increase error rate and circuit latency for even the simplest algorithms. In this survey, we present AI and heuristic based methods recently reported in the literature that attempt to address these QCC challenges. We group them based on underlying techniques that they implement, such as AI algorithms including genetic algorithms, genetic programming, ant colony optimization and AI planning, and heuristics methods employing greedy algorithms, satisfiability problem solvers, dynamic and graph optimization techniques. We discuss performance of each QCC technique and evaluate its potential limitations.

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Section: Evaluation Of Methods and Future Directionsmentioning

confidence: 99%

Section: Ant Colony Optimizationmentioning

confidence: 99%

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Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics

Kusyk

Saeed

2021

IEEE Trans. Quantum Eng.

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“…Basak et al presented an algorithm using the ESOP expressions [9]. Some automated reversible logic synthesis methods, such as genetic algorithms (GAs) are also presented [11][12] [13][14][15] [16]. These algorithms use optimization algorithms.…”

Section: -Backgroundmentioning

confidence: 99%

ARASP: An ASIP Processor for Automated Reversible Logic Synthesis

Kalantari¹,

Gerami²,

Eshghi³

2022

jist

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Reversible logic has been emerged as a promising computing paradigm to design low power circuits in recent years. The synthesis of reversible circuits is very different from that of non-reversible circuits. Many researchers are studying methods for synthesizing reversible combinational logic. Some automated reversible logic synthesis methods use optimization algorithms Optimization algorithms are used in some automated reversible logic synthesis techniques. In these methods, the process of finding a circuit for a given function is a very time-consuming task, so it"s better to design a processor which speeds up the process of synthesis. Application specific instruction set processors (ASIP) can benefit the advantages of both custom ASIC chips and general DSP chips. In this paper, a new architecture for automatic reversible logic synthesis based on an Application Specific Instruction set Processors is presented. The essential purpose of the design was to provide the programmability with the specific necessary instructions for automated synthesis reversible. Our proposed processor that we referred to as ARASP is a 16-bit processor with a total of 47 instructions, which some specific instruction has been set for automated synthesis reversible circuits. ARASP is specialized for automated synthesis of reversible circuits using Genetic optimization algorithms. All major components of the design are comprehensively discussed within the processor core. The set of instructions is provided in the Register Transform Language completely. Afterward, the VHDL code is used to test the proposed architecture.

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“…Techniques to synthesize quantum circuits directly such as genetic programming [19][23] or swarm algorithm [1] have also been proposed. They are an interesting alternative once it enables the optimization of the circuit over the synthesis process.…”

Section: Introductionmentioning

confidence: 99%

Quantum Circuit Synthesis Using Projective Simulation

Pires

Duzzioni

Marchi

et al. 2021

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Quantum Computing has been evolving in the last years. Although nowadays quantum algorithms performance has shown superior to their classical counterparts, quantum decoherence and additional auxiliary qubits needed for error tolerance routines have been huge barriers for quantum algorithms efficient use.These restrictions lead us to search for ways to minimize algorithms costs, i.e the number of quantum logical gates and the depth of the circuit. For this, quantum circuit synthesis and quantum circuit optimization techniques are explored.We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis. The agent had the task of creating quantum circuits up to 5 qubits. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased.

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Quantum Program Synthesis: Swarm Algorithms and Benchmarks

Cited by 11 publications

References 18 publications

Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics

Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics

ARASP: An ASIP Processor for Automated Reversible Logic Synthesis

Quantum Circuit Synthesis Using Projective Simulation

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