Xu Zhou, Yuchen Wang, Wenxuan Tao, Zhuojun Zhou, Le Luo
{"title":"NISQ时代的分布式量子算法:一种用减少资源解决西蒙问题的新方法","authors":"Xu Zhou, Yuchen Wang, Wenxuan Tao, Zhuojun Zhou, Le Luo","doi":"10.1002/qute.202500067","DOIUrl":null,"url":null,"abstract":"<p>Distributed quantum computation has gained significant interest in the noisy intermediate-scale quantum (NISQ) era. This paradigm requires each computing node to possess a reduced number of qubits and quantum gates. In this study, a Distributed Simon's Algorithm (DSA) is designed to tackle Simon's problem, which entails the discovery of a hidden string <span></span><math>\n <semantics>\n <mrow>\n <mi>s</mi>\n <mo>∈</mo>\n <msup>\n <mrow>\n <mo>{</mo>\n <mn>0</mn>\n <mo>,</mo>\n <mn>1</mn>\n <mo>}</mo>\n </mrow>\n <mi>n</mi>\n </msup>\n </mrow>\n <annotation>$s \\in \\lbrace 0,1\\rbrace ^n$</annotation>\n </semantics></math> of a promised Boolean function <span></span><math>\n <semantics>\n <mrow>\n <mi>f</mi>\n <mo>:</mo>\n <msup>\n <mrow>\n <mo>{</mo>\n <mn>0</mn>\n <mo>,</mo>\n <mn>1</mn>\n <mo>}</mo>\n </mrow>\n <mi>n</mi>\n </msup>\n <mo>→</mo>\n <msup>\n <mrow>\n <mo>{</mo>\n <mn>0</mn>\n <mo>,</mo>\n <mn>1</mn>\n <mo>}</mo>\n </mrow>\n <mi>m</mi>\n </msup>\n </mrow>\n <annotation>$f: \\lbrace 0,1\\rbrace ^n \\rightarrow \\lbrace 0,1\\rbrace ^m$</annotation>\n </semantics></math>, where <span></span><math>\n <semantics>\n <mrow>\n <mi>f</mi>\n <mo>(</mo>\n <mi>x</mi>\n <mo>)</mo>\n <mo>=</mo>\n <mi>f</mi>\n <mo>(</mo>\n <mi>y</mi>\n <mo>)</mo>\n </mrow>\n <annotation>$f(x)=f(y)$</annotation>\n </semantics></math> if and only if <span></span><math>\n <semantics>\n <mrow>\n <mi>x</mi>\n <mo>=</mo>\n <mi>y</mi>\n </mrow>\n <annotation>$x=y$</annotation>\n </semantics></math> or <span></span><math>\n <semantics>\n <mrow>\n <mi>x</mi>\n <mi>⊕</mi>\n <mi>y</mi>\n <mo>=</mo>\n <mi>s</mi>\n </mrow>\n <annotation>$x \\oplus y = s$</annotation>\n </semantics></math>. Specifically, 1) our algorithm is capable of being partitioned into any <span></span><math>\n <semantics>\n <mi>t</mi>\n <annotation>$t$</annotation>\n </semantics></math> nodes, where <span></span><math>\n <semantics>\n <mrow>\n <mn>2</mn>\n <mo>≤</mo>\n <mi>t</mi>\n <mo>≤</mo>\n <mi>n</mi>\n </mrow>\n <annotation>$2 \\le t \\le n$</annotation>\n </semantics></math>; 2) the number of queries required by the DSA is <span></span><math>\n <semantics>\n <mrow>\n <mi>n</mi>\n <mo>−</mo>\n <mi>t</mi>\n </mrow>\n <annotation>$n-t$</annotation>\n </semantics></math>, while that of the original Simon's algorithm (SA) is <span></span><math>\n <semantics>\n <mrow>\n <mi>n</mi>\n <mo>−</mo>\n <mn>1</mn>\n </mrow>\n <annotation>$n-1$</annotation>\n </semantics></math>; 3) the maximum number of qubits required by our approach at a single node is <span></span><math>\n <semantics>\n <mrow>\n <mi>max</mi>\n <mfenced>\n <msub>\n <mi>n</mi>\n <mn>0</mn>\n </msub>\n <mo>,</mo>\n <msub>\n <mi>n</mi>\n <mn>1</mn>\n </msub>\n <mo>,</mo>\n <mi>⋯</mi>\n <mo>,</mo>\n <msub>\n <mi>n</mi>\n <mrow>\n <mi>t</mi>\n <mo>−</mo>\n <mn>1</mn>\n </mrow>\n </msub>\n </mfenced>\n <mo>+</mo>\n <mi>m</mi>\n </mrow>\n <annotation>$\\max \\left(n_0,n_1,\\dots,n_{t-1} \\right) + m$</annotation>\n </semantics></math>, which is fewer than the qubits required by both the SA and existing distributed Simon's algorithm. Here, <span></span><math>\n <semantics>\n <msub>\n <mi>n</mi>\n <mi>j</mi>\n </msub>\n <annotation>$n_j$</annotation>\n </semantics></math> denotes the number of computing qubits needed for the <span></span><math>\n <semantics>\n <mi>j</mi>\n <annotation>$j$</annotation>\n </semantics></math>-th node and satisfies <span></span><math>\n <semantics>\n <mrow>\n <msubsup>\n <mo>∑</mo>\n <mrow>\n <mi>j</mi>\n <mo>=</mo>\n <mn>0</mn>\n </mrow>\n <mrow>\n <mi>t</mi>\n <mo>−</mo>\n <mn>1</mn>\n </mrow>\n </msubsup>\n <msub>\n <mi>n</mi>\n <mi>j</mi>\n </msub>\n <mo>=</mo>\n <mi>n</mi>\n </mrow>\n <annotation>$\\sum _{j=0}^{t-1} n_j =n$</annotation>\n </semantics></math>; 4) the optimal circuit depth of the DSA is <span></span><math>\n <semantics>\n <mrow>\n <mrow>\n <mo>(</mo>\n <mi>m</mi>\n <mo>+</mo>\n <mn>1</mn>\n <mo>)</mo>\n </mrow>\n <mo>·</mo>\n <msup>\n <mn>2</mn>\n <mfenced>\n <mfrac>\n <mi>n</mi>\n <mi>t</mi>\n </mfrac>\n </mfenced>\n </msup>\n <mo>+</mo>\n <mn>2</mn>\n </mrow>\n <annotation>$(m + 1) \\cdot 2^{\\left\\lceil \\frac{n}{t} \\right\\rceil } + 2$</annotation>\n </semantics></math>, which is reduced compared to the circuit depth of the SA, <span></span><math>\n <semantics>\n <mrow>\n <mrow>\n <mo>(</mo>\n <mi>m</mi>\n <mo>+</mo>\n <mn>1</mn>\n <mo>)</mo>\n </mrow>\n <mo>·</mo>\n <msup>\n <mn>2</mn>\n <mi>n</mi>\n </msup>\n <mo>+</mo>\n <mn>2</mn>\n </mrow>\n <annotation>$(m+1) \\cdot 2^n+2$</annotation>\n </semantics></math>; 5) in contrast to currently distributed schemes, the DSA eliminates the need for classical queries; 6) how the DSA solves a specific Simon's problem (e.g., <span></span><math>\n <semantics>\n <mrow>\n <mi>s</mi>\n <mo>=</mo>\n <mn>1000</mn>\n </mrow>\n <annotation>$s =1000$</annotation>\n </semantics></math>) is also simulated using MindSpore Quantum, a quantum simulation software. The simulation results show that the DSA features a shallower quantum circuit, thereby demonstrating enhanced resistance to circuit noise. This characteristic makes it more feasible for implementation in the NISQ era.</p>","PeriodicalId":72073,"journal":{"name":"Advanced quantum technologies","volume":"8 5","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Distributed Quantum Algorithm for the NISQ Era: A Novel Approach to Solving Simon's Problem with Reduced Resources\",\"authors\":\"Xu Zhou, Yuchen Wang, Wenxuan Tao, Zhuojun Zhou, Le Luo\",\"doi\":\"10.1002/qute.202500067\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Distributed quantum computation has gained significant interest in the noisy intermediate-scale quantum (NISQ) era. This paradigm requires each computing node to possess a reduced number of qubits and quantum gates. In this study, a Distributed Simon's Algorithm (DSA) is designed to tackle Simon's problem, which entails the discovery of a hidden string <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>s</mi>\\n <mo>∈</mo>\\n <msup>\\n <mrow>\\n <mo>{</mo>\\n <mn>0</mn>\\n <mo>,</mo>\\n <mn>1</mn>\\n <mo>}</mo>\\n </mrow>\\n <mi>n</mi>\\n </msup>\\n </mrow>\\n <annotation>$s \\\\in \\\\lbrace 0,1\\\\rbrace ^n$</annotation>\\n </semantics></math> of a promised Boolean function <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>f</mi>\\n <mo>:</mo>\\n <msup>\\n <mrow>\\n <mo>{</mo>\\n <mn>0</mn>\\n <mo>,</mo>\\n <mn>1</mn>\\n <mo>}</mo>\\n </mrow>\\n <mi>n</mi>\\n </msup>\\n <mo>→</mo>\\n <msup>\\n <mrow>\\n <mo>{</mo>\\n <mn>0</mn>\\n <mo>,</mo>\\n <mn>1</mn>\\n <mo>}</mo>\\n </mrow>\\n <mi>m</mi>\\n </msup>\\n </mrow>\\n <annotation>$f: \\\\lbrace 0,1\\\\rbrace ^n \\\\rightarrow \\\\lbrace 0,1\\\\rbrace ^m$</annotation>\\n </semantics></math>, where <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>f</mi>\\n <mo>(</mo>\\n <mi>x</mi>\\n <mo>)</mo>\\n <mo>=</mo>\\n <mi>f</mi>\\n <mo>(</mo>\\n <mi>y</mi>\\n <mo>)</mo>\\n </mrow>\\n <annotation>$f(x)=f(y)$</annotation>\\n </semantics></math> if and only if <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>x</mi>\\n <mo>=</mo>\\n <mi>y</mi>\\n </mrow>\\n <annotation>$x=y$</annotation>\\n </semantics></math> or <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>x</mi>\\n <mi>⊕</mi>\\n <mi>y</mi>\\n <mo>=</mo>\\n <mi>s</mi>\\n </mrow>\\n <annotation>$x \\\\oplus y = s$</annotation>\\n </semantics></math>. Specifically, 1) our algorithm is capable of being partitioned into any <span></span><math>\\n <semantics>\\n <mi>t</mi>\\n <annotation>$t$</annotation>\\n </semantics></math> nodes, where <span></span><math>\\n <semantics>\\n <mrow>\\n <mn>2</mn>\\n <mo>≤</mo>\\n <mi>t</mi>\\n <mo>≤</mo>\\n <mi>n</mi>\\n </mrow>\\n <annotation>$2 \\\\le t \\\\le n$</annotation>\\n </semantics></math>; 2) the number of queries required by the DSA is <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>n</mi>\\n <mo>−</mo>\\n <mi>t</mi>\\n </mrow>\\n <annotation>$n-t$</annotation>\\n </semantics></math>, while that of the original Simon's algorithm (SA) is <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>n</mi>\\n <mo>−</mo>\\n <mn>1</mn>\\n </mrow>\\n <annotation>$n-1$</annotation>\\n </semantics></math>; 3) the maximum number of qubits required by our approach at a single node is <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>max</mi>\\n <mfenced>\\n <msub>\\n <mi>n</mi>\\n <mn>0</mn>\\n </msub>\\n <mo>,</mo>\\n <msub>\\n <mi>n</mi>\\n <mn>1</mn>\\n </msub>\\n <mo>,</mo>\\n <mi>⋯</mi>\\n <mo>,</mo>\\n <msub>\\n <mi>n</mi>\\n <mrow>\\n <mi>t</mi>\\n <mo>−</mo>\\n <mn>1</mn>\\n </mrow>\\n </msub>\\n </mfenced>\\n <mo>+</mo>\\n <mi>m</mi>\\n </mrow>\\n <annotation>$\\\\max \\\\left(n_0,n_1,\\\\dots,n_{t-1} \\\\right) + m$</annotation>\\n </semantics></math>, which is fewer than the qubits required by both the SA and existing distributed Simon's algorithm. Here, <span></span><math>\\n <semantics>\\n <msub>\\n <mi>n</mi>\\n <mi>j</mi>\\n </msub>\\n <annotation>$n_j$</annotation>\\n </semantics></math> denotes the number of computing qubits needed for the <span></span><math>\\n <semantics>\\n <mi>j</mi>\\n <annotation>$j$</annotation>\\n </semantics></math>-th node and satisfies <span></span><math>\\n <semantics>\\n <mrow>\\n <msubsup>\\n <mo>∑</mo>\\n <mrow>\\n <mi>j</mi>\\n <mo>=</mo>\\n <mn>0</mn>\\n </mrow>\\n <mrow>\\n <mi>t</mi>\\n <mo>−</mo>\\n <mn>1</mn>\\n </mrow>\\n </msubsup>\\n <msub>\\n <mi>n</mi>\\n <mi>j</mi>\\n </msub>\\n <mo>=</mo>\\n <mi>n</mi>\\n </mrow>\\n <annotation>$\\\\sum _{j=0}^{t-1} n_j =n$</annotation>\\n </semantics></math>; 4) the optimal circuit depth of the DSA is <span></span><math>\\n <semantics>\\n <mrow>\\n <mrow>\\n <mo>(</mo>\\n <mi>m</mi>\\n <mo>+</mo>\\n <mn>1</mn>\\n <mo>)</mo>\\n </mrow>\\n <mo>·</mo>\\n <msup>\\n <mn>2</mn>\\n <mfenced>\\n <mfrac>\\n <mi>n</mi>\\n <mi>t</mi>\\n </mfrac>\\n </mfenced>\\n </msup>\\n <mo>+</mo>\\n <mn>2</mn>\\n </mrow>\\n <annotation>$(m + 1) \\\\cdot 2^{\\\\left\\\\lceil \\\\frac{n}{t} \\\\right\\\\rceil } + 2$</annotation>\\n </semantics></math>, which is reduced compared to the circuit depth of the SA, <span></span><math>\\n <semantics>\\n <mrow>\\n <mrow>\\n <mo>(</mo>\\n <mi>m</mi>\\n <mo>+</mo>\\n <mn>1</mn>\\n <mo>)</mo>\\n </mrow>\\n <mo>·</mo>\\n <msup>\\n <mn>2</mn>\\n <mi>n</mi>\\n </msup>\\n <mo>+</mo>\\n <mn>2</mn>\\n </mrow>\\n <annotation>$(m+1) \\\\cdot 2^n+2$</annotation>\\n </semantics></math>; 5) in contrast to currently distributed schemes, the DSA eliminates the need for classical queries; 6) how the DSA solves a specific Simon's problem (e.g., <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>s</mi>\\n <mo>=</mo>\\n <mn>1000</mn>\\n </mrow>\\n <annotation>$s =1000$</annotation>\\n </semantics></math>) is also simulated using MindSpore Quantum, a quantum simulation software. The simulation results show that the DSA features a shallower quantum circuit, thereby demonstrating enhanced resistance to circuit noise. This characteristic makes it more feasible for implementation in the NISQ era.</p>\",\"PeriodicalId\":72073,\"journal\":{\"name\":\"Advanced quantum technologies\",\"volume\":\"8 5\",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-04-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced quantum technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/qute.202500067\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced quantum technologies","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/qute.202500067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
摘要
在嘈杂的中尺度量子(NISQ)时代,分布式量子计算得到了极大的关注。这种模式要求每个计算节点拥有减少数量的量子比特和量子门。在本研究中,分布式西蒙算法(DSA)被设计来解决西蒙问题,该问题需要发现一个隐藏字符串s∈{0,承诺布尔函数f的1} n $s \in \lbrace 0,1\rbrace ^n$:{0,1} n→{0},1米$f: \lbrace 0,1\rbrace ^n \rightarrow \lbrace 0,1\rbrace ^m$,其中f (x) = f (y) $f(x)=f(y)$当且仅当x = y $x=y$或x⊕Y = s $x \oplus y = s$。具体来说,1)我们的算法能够被划分为任意t个$t$节点,其中2≤t≤n $2 \le t \le n$;2) DSA的查询次数为n−t $n-t$,而原Simon算法(SA)的查询次数为n−1 $n-1$;3)我们的方法在单个节点上所需的最大量子比特数为Max n 0, n 1,⋯,n t−1 + m $\max \left(n_0,n_1,\dots,n_{t-1} \right) + m$,这比SA和现有的分布式西蒙算法所需的量子比特都要少。 这里,N j $n_j$表示第j $j$节点所需的计算量子比特数,满足∑j = 0T−1 n j = n $\sum _{j=0}^{t-1} n_j =n$;4) DSA的最佳电路深度为(m + 1)·2 n t + 2$(m + 1) \cdot 2^{\left\lceil \frac{n}{t} \right\rceil } + 2$,与SA的电路深度相比减小,(m + 1)·2 n + 2 $(m+1) \cdot 2^n+2$;5)与目前的分布式方案相比,DSA消除了对经典查询的需求;6) DSA如何解决特定的西蒙问题(例如,s = 1000 $s =1000$)也使用量子模拟软件MindSpore Quantum进行模拟。仿真结果表明,DSA具有较浅的量子电路,从而增强了对电路噪声的抵抗力。这一特点使其在NISQ时代的实现更加可行。
Distributed Quantum Algorithm for the NISQ Era: A Novel Approach to Solving Simon's Problem with Reduced Resources
Distributed quantum computation has gained significant interest in the noisy intermediate-scale quantum (NISQ) era. This paradigm requires each computing node to possess a reduced number of qubits and quantum gates. In this study, a Distributed Simon's Algorithm (DSA) is designed to tackle Simon's problem, which entails the discovery of a hidden string of a promised Boolean function , where if and only if or . Specifically, 1) our algorithm is capable of being partitioned into any nodes, where ; 2) the number of queries required by the DSA is , while that of the original Simon's algorithm (SA) is ; 3) the maximum number of qubits required by our approach at a single node is , which is fewer than the qubits required by both the SA and existing distributed Simon's algorithm. Here, denotes the number of computing qubits needed for the -th node and satisfies ; 4) the optimal circuit depth of the DSA is , which is reduced compared to the circuit depth of the SA, ; 5) in contrast to currently distributed schemes, the DSA eliminates the need for classical queries; 6) how the DSA solves a specific Simon's problem (e.g., ) is also simulated using MindSpore Quantum, a quantum simulation software. The simulation results show that the DSA features a shallower quantum circuit, thereby demonstrating enhanced resistance to circuit noise. This characteristic makes it more feasible for implementation in the NISQ era.