Quantum Register Machine: Efficient Implementation of Quantum Recursive Programs
- URL: http://arxiv.org/abs/2408.10054v2
- Date: Thu, 7 Nov 2024 01:20:35 GMT
- Title: Quantum Register Machine: Efficient Implementation of Quantum Recursive Programs
- Authors: Zhicheng Zhang, Mingsheng Ying,
- Abstract summary: We propose a notion of quantum register machine, the first purely quantum architecture (including an instruction set) that supports quantum control flows.
Based on quantum register machine, we describe the first comprehensive implementation process of quantum recursion programs.
Our efficient implementation of quantum algorithms also offers automatic parallelisation of quantum algorithms.
- Score: 7.042810171786408
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum recursive programming has been recently introduced for describing sophisticated and complicated quantum algorithms in a compact and elegant way. However, implementation of quantum recursion involves intricate interplay between quantum control flows and recursive procedure calls. In this paper, we aim at resolving this fundamental challenge and develop a series of techniques to efficiently implement quantum recursive programs. Our main contributions include: 1. We propose a notion of quantum register machine, the first purely quantum architecture (including an instruction set) that supports quantum control flows and recursive procedure calls at the same time. 2. Based on quantum register machine, we describe the first comprehensive implementation process of quantum recursive programs, including the compilation, the partial evaluation of quantum control flows, and the execution on the quantum register machine. 3. As a bonus, our efficient implementation of quantum recursive programs also offers automatic parallelisation of quantum algorithms. For implementing certain quantum algorithmic subroutine, like the widely used quantum multiplexor, we can even obtain exponential parallel speed-up (over the straightforward implementation) from this automatic parallelisation. This demonstrates that quantum recursive programming can be win-win for both modularity of programs and efficiency of their implementation.
Related papers
- Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
We develop a reinforcement learning-based quantum compiler for a superconducting processor.
We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths.
Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
arXiv Detail & Related papers (2024-06-18T01:49:48Z) - Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
We present a model for parallelizing simulation of quantum circuit executions.
The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
arXiv Detail & Related papers (2024-06-05T17:16:07Z) - On Reducing the Execution Latency of Superconducting Quantum Processors via Quantum Program Scheduling [48.142860424323395]
We introduce the Quantum Program Scheduling Problem (QPSP) to improve the utility efficiency of quantum resources.
Specifically, a quantum program scheduling method concerning the circuit width, number of measurement shots, and submission time of quantum programs is proposed to reduce the execution latency.
arXiv Detail & Related papers (2024-04-11T16:12:01Z) - Quantum Dynamic Programming [0.0]
We show how to coherently generate unitaries of recursion steps using memorized intermediate quantum states.
We find that quantum dynamic programming yields an exponential reduction in circuit depth for a large class of fixed-point quantum recursions.
We apply quantum dynamic programming to a recently proposed double-bracket quantum algorithm for diagonalization to obtain a new protocol for obliviously preparing a quantum state in its Schmidt basis.
arXiv Detail & Related papers (2024-03-14T08:59:22Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Dynamic Runtime Assertions in Quantum Ternary Systems [1.5410557873153832]
We investigate assertions in quantum ternary systems, which are more challenging than those in quantum binary systems.
We propose quantum ternary circuit designs to assert classical, entanglement, and superposition states.
arXiv Detail & Related papers (2023-12-23T17:46:51Z) - Quantum Recursive Programming with Quantum Case Statements [8.320147245667124]
A simple programming language for supporting this kind of quantum recursion is defined.
A series of examples are presented to show that some quantum algorithms can be elegantly written as quantum recursion programs.
arXiv Detail & Related papers (2023-11-03T05:44:52Z) - Dynamic quantum circuit compilation [11.550577505893367]
Recent advancements in quantum hardware have introduced mid-circuit measurements and resets, enabling the reuse of measured qubits.
We present a systematic study of dynamic quantum circuit compilation, a process that transforms static quantum circuits into their dynamic equivalents.
arXiv Detail & Related papers (2023-10-17T06:26:30Z) - Parametric Synthesis of Computational Circuits for Complex Quantum
Algorithms [0.0]
The purpose of our quantum synthesizer is enabling users to implement quantum algorithms using higher-level commands.
The proposed approach for implementing quantum algorithms has a potential application in the field of machine learning.
arXiv Detail & Related papers (2022-09-20T06:25:47Z) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Quantum walk processes in quantum devices [55.41644538483948]
We study how to represent quantum walk on a graph as a quantum circuit.
Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
arXiv Detail & Related papers (2020-12-28T18:04:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.