From b9daa75a077f05f88ad9d949b53cad904e80ddfb Mon Sep 17 00:00:00 2001 From: github-nakasho Date: Tue, 21 Jul 2020 11:52:03 +0900 Subject: [PATCH] Modify 4-Parameters_customize.md --- build/.DS_Store | Bin 0 -> 6148 bytes build/doctrees/1-Introduction.doctree | Bin 5458 -> 5438 bytes build/doctrees/2-Install.doctree | Bin 8750 -> 8730 bytes build/doctrees/3-Minimum_sample.doctree | Bin 4460 -> 4440 bytes build/doctrees/4-Parameters_customize.doctree | Bin 3778 -> 30363 bytes build/doctrees/5-benchmark.doctree | Bin 6882 -> 6862 bytes build/doctrees/6-HigherOrderModel.doctree | Bin 6061 -> 6041 bytes build/doctrees/7-hogehoge.doctree | Bin 0 -> 6017 bytes build/doctrees/apis/modules.doctree | Bin 2910 -> 2890 bytes build/doctrees/apis/openjij.doctree | Bin 6776 -> 6706 bytes build/doctrees/apis/openjij.model.doctree | Bin 76989 -> 76589 bytes .../apis/openjij.sampler.chimera_gpu.doctree | Bin 84613 -> 84303 bytes build/doctrees/apis/openjij.sampler.doctree | Bin 252753 -> 251733 bytes build/doctrees/apis/openjij.utils.doctree | Bin 77264 -> 76904 bytes build/doctrees/environment.pickle | Bin 187256 -> 195207 bytes build/doctrees/index.doctree | Bin 5496 -> 5476 bytes build/html/.DS_Store | Bin 0 -> 6148 bytes build/html/.buildinfo | 2 +- build/html/4-Parameters_customize.html | 166 +++++++++++++++++- build/html/_images/schedule.png | Bin 0 -> 7618 bytes build/html/_images/schedule_sqa.png | Bin 0 -> 6693 bytes build/html/_images/schedule_sqa_2.png | Bin 0 -> 10151 bytes build/html/_sources/.DS_Store | Bin 0 -> 6148 bytes .../_sources/4-Parameters_customize.md.txt | 160 ++++++++++++++++- build/html/_sources/7-hogehoge.md.txt | 34 ++++ build/html/searchindex.js | 2 +- source/4-Parameters_customize.md | 160 ++++++++++++++++- source/images/.DS_Store | Bin 0 -> 6148 bytes source/images/4/schedule.png | Bin 0 -> 7618 bytes source/images/4/schedule_sqa.png | Bin 0 -> 6693 bytes source/images/4/schedule_sqa_2.png | Bin 0 -> 10151 bytes 31 files changed, 498 insertions(+), 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When it is not found, a full rebuild will be done. -config: 1b6f940f8d9e59c2bc24c23404cb4d10 +config: b5f240d1de9c371a4bcfd8495307cfd4 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/build/html/4-Parameters_customize.html b/build/html/4-Parameters_customize.html index 341f32e..a42d4ab 100644 --- a/build/html/4-Parameters_customize.html +++ b/build/html/4-Parameters_customize.html @@ -91,7 +91,31 @@
  • Install
  • Minimum sample code
  • Parameters customize
  • Benchmark
    • @@ -163,6 +187,37 @@

    Parameters customize

    Customize annealing schedule

    +

    In this page, let us show you how to set the parameters for annealing.

    +
    +

    SASampler

    +

    SASampler has arguments as follows.

    +
    +

    beta_min (float)

    +

    The minimum value of inverse temperature. Default to None. This corresponds to the starting temperature for annealing.
    If you do not specify anything, OpenJij will set the appropriate temperature automatically.

    +
    +
    +

    beta_max (float)

    +

    The maximum value of inverse temperature. Default to None. This corresponds to the ending temperature for annealing.
    If you do not specify anything, OpenJij will set the appropriate temperature automatically.

    +
    +
    +

    num_reads (int)

    +

    The number of iteration of annealing. Default to 1. If num_reads is specified, response has numerical results for the number of num_reads.

    +
    +
    +

    num_sweeps (int)

    +

    The number of Monte-Carlo steps (hereafter we called MCsteps) during annealing. Default to 1000.

    +
    +

    -note- MCsteps means the number of times to flip all the spins in the system. e.g., the case of N spin system, during 1 MCstep, you will have to flip these spins N times.

    +
    +
    +
    +

    schedule (list)

    +

    You can set the annealing schedule as you like. Default to None. If you want to calculate “alpha” MCsteps for a given inverse temperature “beta”, you can set it as [beta, alpha].

    +
    +
    + +
    +

    Sample script 2

    +

    Here, let me show you an example how to specify an annealing schedule for user. The following figure shows an image of annealing schedule.

    +

    An example of SA schedule

    +

    At first, calculate 3 MCsteps with beta=0.5. Next, compute 5 MCsteps with beta=5. Finally, execute 6 MCsteps with beta=10.

    +

    A example of script that does this is following.

    +
    schedule = [[0.5, 3], [5, 5], [10, 6]]
 
 sampler = SASampler()
 response = sampler.sample_ising(h, J, schedule=schedule)
    +
    +

    SQASampler

    +

    SQASampler considers the total Hamiltonian with the following quantum terms when the Hamiltonian of the problem you want to solve is $\hat{H}_p$,

    +

    $ +\hat{H} = s\hat{H}p + (1-s) \Gamma \sum{i} \hat{\sigma}^x_i +$

    +

    and annealing is performed under the following statistical distribution,

    +

    $ +P(\hat{H}) \propto e^{-\beta \hat{H}} +$

    +

    SQASampler has arguments as follows.

    +
    +

    beta (float)

    +

    The inverse temperature. Default to 5.0.

    +
    +
    +

    Gamma (float)

    +

    The magnitude of the quantum term. Default to 1.0.

    +
    +
    +

    trotter (int)

    +

    The system Trotter decomposition number. Default to 4.

    +
    +
    +

    num_sweeps (int)

    +

    The number of MCsteps required between changing the annealing schedule parameter s from 0 to 1. Default to 1000.

    +
    +
    +

    num_reads (int)

    +

    The number of annealing iteration. Default to 1.

    +
    +
    +

    shcedule (list)

    +

    Set an annealing shcedule as you like. Default to None. For a given inverse temperature if you want to compute “alpha” MCsteps with the annealing schedule parameter “s”, you can set it as [s, alpha].
    If you want to specify the inverse temperature “beta” and compute “alpha” MCsteps with the annealing schedule parameter “s”, you can specify it as [s, beta, alpha].

    +
    +
    +
    +

    Sample script 1

    +

    The following example shows an annealing computation with beta=10, gamma=5, trotter=16, num_reads=1000, num_sweeps=100

    +
    import openjij as oj
+
+n = 10
+h, J = {}, {}
+for i in range(n-1):
+    for j in range(i+1, n):
+        J[i, j] = -1
+
+# set parameters 
+beta = 10
+gamma = 5
+trotter = 16
+nreads = 1000
+nsweeps = 100
+
+sampler = SQASampler()
+response = sampler.sample_ising(h, J, beta=beta, gamma=gamma, trotter=trotter, num_reads=nreads, num_sweeps=nsweeps)
+
    +
    +
    +
    +

    Sample script 2

    +

    Here we introduce an example of a user-specified annealing schedule. The following figure shows an image of annealing schedule.

    +

    An example of SQA schedule with constant beta

    +

    First, 5 MCsteps with s=0.0. Next, 10 MCsteps with s=0.5. Finally, 5 MCsteps with s=1.

    +
    schedule = [[0, 5], [0.5, 10], [1, 5]]
+
+sampler = SQASampler()
+response = sampler.sample_ising(h, J, schedule=schedule)
+
    +
    +
    +
    +

    Sample script 3

    +

    In this section, we show you an example of a user-defined annealing shcedule in more detail.

    +

    An example of SQA schedule including beta

    +

    First, 5 MCsteps with s=0.0, beta=0.5. Next, 10 MCsteps with s=0.5, beta=1.0. Finally, 5 MCsteps with s=1, beta=10.

    +
    schedule = [[0, 0.5, 5], [0.5, 1.0, 10], [1, 10, 5]]
+
+sampler = SQASampler()
+response = sampler.sample_ising(h, J, schedule=schedule)
+
    +
    +
    +
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Default to None. This corresponds to the starting temperature for annealing. +If you do not specify anything, OpenJij will set the appropriate temperature automatically. + +#### beta_max (float) + +The maximum value of inverse temperature. Default to None. This corresponds to the ending temperature for annealing. +If you do not specify anything, OpenJij will set the appropriate temperature automatically. + +#### num_reads (int) + +The number of iteration of annealing. Default to 1. If num_reads is specified, response has numerical results for the number of num_reads. + +#### num_sweeps (int) + +The number of Monte-Carlo steps (hereafter we called MCsteps) during annealing. Default to 1000. + +> -note- MCsteps means the number of times to flip all the spins in the system. e.g., the case of N spin system, during 1 MCstep, you will have to flip these spins N times. + +#### schedule (list) + +You can set the annealing schedule as you like. Default to None. If you want to calculate "alpha" MCsteps for a given inverse temperature "beta", you can set it as [beta, alpha]. + + +### Sample script 1 + +The following is an example of annealing with parameters `beta_min=0.1, beta_max=100, num_reads=1000, num_sweeps=100`. ```python import openjij as oj @@ -12,14 +46,124 @@ for i in range(n-1): for j in range(i+1, n): J[i, j] = -1 -# customized annealing schedule -# list of [beta, monte carlo steps in beta] -schedule = [ - [10, 3], - [ 5, 5], - [0.5, 10] -] +# set parameters +bmin = 0.1 +bmax = 100 +nreads = 1000 +nsweeps = 100 + +sampler = SASampler() +response = sampler.sample_ising(h, J, beta_min=bmin, beta_max=bmax, num_reads=nreads, num_sweeps=nsweeps) +``` + +### Sample script 2 + +Here, let me show you an example how to specify an annealing schedule for user. The following figure shows an image of annealing schedule. + +![An example of SA schedule](/images/4/schedule.png) + +At first, calculate 3 MCsteps with `beta=0.5`. Next, compute 5 MCsteps with `beta=5`. Finally, execute 6 MCsteps with `beta=10`. + +A example of script that does this is following. + +```python +schedule = [[0.5, 3], [5, 5], [10, 6]] sampler = SASampler() response = sampler.sample_ising(h, J, schedule=schedule) -``` \ No newline at end of file +``` + +### SQASampler + +SQASampler considers the total Hamiltonian with the following quantum terms when the Hamiltonian of the problem you want to solve is $\hat{H}_p$, + +$ +\hat{H} = s\hat{H}_p + (1-s) \Gamma \sum_{i} \hat{\sigma}^x_i +$ + +and annealing is performed under the following statistical distribution, + +$ +P(\hat{H}) \propto e^{-\beta \hat{H}} +$ + +`SQASampler` has arguments as follows. + +#### beta (float) + +The inverse temperature. Default to 5.0. + +#### Gamma (float) + +The magnitude of the quantum term. Default to 1.0. + +#### trotter (int) + +The system Trotter decomposition number. Default to 4. + +#### num_sweeps (int) + +The number of MCsteps required between changing the annealing schedule parameter `s` from 0 to 1. Default to 1000. + +#### num_reads (int) + +The number of annealing iteration. Default to 1. + +#### shcedule (list) + +Set an annealing shcedule as you like. Default to None. For a given inverse temperature if you want to compute "alpha" MCsteps with the annealing schedule parameter "s", you can set it as [s, alpha]. +If you want to specify the inverse temperature "beta" and compute "alpha" MCsteps with the annealing schedule parameter "s", you can specify it as [s, beta, alpha]. + +### Sample script 1 + +The following example shows an annealing computation with `beta=10, gamma=5, trotter=16, num_reads=1000, num_sweeps=100` + +```python +import openjij as oj + +n = 10 +h, J = {}, {} +for i in range(n-1): + for j in range(i+1, n): + J[i, j] = -1 + +# set parameters +beta = 10 +gamma = 5 +trotter = 16 +nreads = 1000 +nsweeps = 100 + +sampler = SQASampler() +response = sampler.sample_ising(h, J, beta=beta, gamma=gamma, trotter=trotter, num_reads=nreads, num_sweeps=nsweeps) +``` + +### Sample script 2 + +Here we introduce an example of a user-specified annealing schedule. The following figure shows an image of annealing schedule. + +![An example of SQA schedule with constant beta](/images/4/schedule_sqa.png) + +First, 5 MCsteps with `s=0.0`. Next, 10 MCsteps with `s=0.5`. Finally, 5 MCsteps with `s=1`. + +```python +schedule = [[0, 5], [0.5, 10], [1, 5]] + +sampler = SQASampler() +response = sampler.sample_ising(h, J, schedule=schedule) +``` + +### Sample script 3 + +In this section, we show you an example of a user-defined annealing shcedule in more detail. + +![An example of SQA schedule including beta](/images/4/schedule_sqa_2.png) + +First, 5 MCsteps with `s=0.0, beta=0.5`. Next, 10 MCsteps with `s=0.5, beta=1.0`. Finally, 5 MCsteps with `s=1, beta=10`. + +```python +schedule = [[0, 0.5, 5], [0.5, 1.0, 10], [1, 10, 5]] + +sampler = SQASampler() +response = sampler.sample_ising(h, J, schedule=schedule) +``` diff --git a/build/html/_sources/7-hogehoge.md.txt b/build/html/_sources/7-hogehoge.md.txt new file mode 100644 index 0000000..efda55f --- /dev/null +++ b/build/html/_sources/7-hogehoge.md.txt @@ -0,0 +1,34 @@ +# Higher order model + +If you want to handle higher order model as follows: + + ```math + H = \sum_{i}h_i\sigma_i + \sum_{i < j} J_{ij} \sigma_i \sigma_j + \sum_{i, j, k} K_{i,j,k} \sigma_i\sigma_j \sigma_k \cdots + ``` + +use ``.sample_hubo`` + +> HUBO: Higher order unconstraint binary optimization + +## Sample code +```python +import openjij as oj + +# Only SASampler can handle HUBO. +sampler = oj.SASampler() + +# make HUBO +h = {0: -1} +J = {(0, 1): -1} +K = {(0, 1, 2): 1} + +response = sampler.sample_hubo([h, J, K], var_type="SPIN") +response.states[0] +> [1, 1, -1] +``` + +## Note + +``.sample_hubo`` +- The first argument (``interactions``) must be in ascending order. +- Need ``var_type`` diff --git a/build/html/searchindex.js b/build/html/searchindex.js index 6547bd5..4ad3a7b 100644 --- a/build/html/searchindex.js +++ b/build/html/searchindex.js @@ -1 +1 @@ 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a/source/4-Parameters_customize.md b/source/4-Parameters_customize.md index aa9851a..5dba05e 100644 --- a/source/4-Parameters_customize.md +++ b/source/4-Parameters_customize.md @@ -2,6 +2,40 @@ ## Customize annealing schedule +In this page, let us show you how to set the parameters for annealing. + +### SASampler + +`SASampler` has arguments as follows. + +#### beta_min (float) + +The minimum value of inverse temperature. Default to None. This corresponds to the starting temperature for annealing. +If you do not specify anything, OpenJij will set the appropriate temperature automatically. + +#### beta_max (float) + +The maximum value of inverse temperature. Default to None. This corresponds to the ending temperature for annealing. +If you do not specify anything, OpenJij will set the appropriate temperature automatically. + +#### num_reads (int) + +The number of iteration of annealing. Default to 1. If num_reads is specified, response has numerical results for the number of num_reads. + +#### num_sweeps (int) + +The number of Monte-Carlo steps (hereafter we called MCsteps) during annealing. Default to 1000. + +> -note- MCsteps means the number of times to flip all the spins in the system. e.g., the case of N spin system, during 1 MCstep, you will have to flip these spins N times. + +#### schedule (list) + +You can set the annealing schedule as you like. Default to None. If you want to calculate "alpha" MCsteps for a given inverse temperature "beta", you can set it as [beta, alpha]. + + +### Sample script 1 + +The following is an example of annealing with parameters `beta_min=0.1, beta_max=100, num_reads=1000, num_sweeps=100`. ```python import openjij as oj @@ -12,14 +46,124 @@ for i in range(n-1): for j in range(i+1, n): J[i, j] = -1 -# customized annealing schedule -# list of [beta, monte carlo steps in beta] -schedule = [ - [10, 3], - [ 5, 5], - [0.5, 10] -] +# set parameters +bmin = 0.1 +bmax = 100 +nreads = 1000 +nsweeps = 100 + +sampler = SASampler() +response = sampler.sample_ising(h, J, beta_min=bmin, beta_max=bmax, num_reads=nreads, num_sweeps=nsweeps) +``` + +### Sample script 2 + +Here, let me show you an example how to specify an annealing schedule for user. The following figure shows an image of annealing schedule. + +![An example of SA schedule](/images/4/schedule.png) + +At first, calculate 3 MCsteps with `beta=0.5`. Next, compute 5 MCsteps with `beta=5`. Finally, execute 6 MCsteps with `beta=10`. + +A example of script that does this is following. + +```python +schedule = [[0.5, 3], [5, 5], [10, 6]] sampler = SASampler() response = sampler.sample_ising(h, J, schedule=schedule) -``` \ No newline at end of file +``` + +### SQASampler + +SQASampler considers the total Hamiltonian with the following quantum terms when the Hamiltonian of the problem you want to solve is $\hat{H}_p$, + +$ +\hat{H} = s\hat{H}_p + (1-s) \Gamma \sum_{i} \hat{\sigma}^x_i +$ + +and annealing is performed under the following statistical distribution, + +$ +P(\hat{H}) \propto e^{-\beta \hat{H}} +$ + +`SQASampler` has arguments as follows. + +#### beta (float) + +The inverse temperature. Default to 5.0. + +#### Gamma (float) + +The magnitude of the quantum term. Default to 1.0. + +#### trotter (int) + +The system Trotter decomposition number. Default to 4. + +#### num_sweeps (int) + +The number of MCsteps required between changing the annealing schedule parameter `s` from 0 to 1. Default to 1000. + +#### num_reads (int) + +The number of annealing iteration. Default to 1. + +#### shcedule (list) + +Set an annealing shcedule as you like. Default to None. For a given inverse temperature if you want to compute "alpha" MCsteps with the annealing schedule parameter "s", you can set it as [s, alpha]. +If you want to specify the inverse temperature "beta" and compute "alpha" MCsteps with the annealing schedule parameter "s", you can specify it as [s, beta, alpha]. + +### Sample script 1 + +The following example shows an annealing computation with `beta=10, gamma=5, trotter=16, num_reads=1000, num_sweeps=100` + +```python +import openjij as oj + +n = 10 +h, J = {}, {} +for i in range(n-1): + for j in range(i+1, n): + J[i, j] = -1 + +# set parameters +beta = 10 +gamma = 5 +trotter = 16 +nreads = 1000 +nsweeps = 100 + +sampler = SQASampler() +response = sampler.sample_ising(h, J, beta=beta, gamma=gamma, trotter=trotter, num_reads=nreads, num_sweeps=nsweeps) +``` + +### Sample script 2 + +Here we introduce an example of a user-specified annealing schedule. The following figure shows an image of annealing schedule. + +![An example of SQA schedule with constant beta](/images/4/schedule_sqa.png) + +First, 5 MCsteps with `s=0.0`. Next, 10 MCsteps with `s=0.5`. Finally, 5 MCsteps with `s=1`. + +```python +schedule = [[0, 5], [0.5, 10], [1, 5]] + +sampler = SQASampler() +response = sampler.sample_ising(h, J, schedule=schedule) +``` + +### Sample script 3 + +In this section, we show you an example of a user-defined annealing shcedule in more detail. + +![An example of SQA schedule including beta](/images/4/schedule_sqa_2.png) + +First, 5 MCsteps with `s=0.0, beta=0.5`. Next, 10 MCsteps with `s=0.5, beta=1.0`. Finally, 5 MCsteps with `s=1, beta=10`. + +```python +schedule = [[0, 0.5, 5], [0.5, 1.0, 10], [1, 10, 5]] + +sampler = SQASampler() +response = sampler.sample_ising(h, J, schedule=schedule) +``` diff --git a/source/images/.DS_Store b/source/images/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..732f16682d384cb2f09fda638be27cb960a7c7d1 GIT binary patch literal 6148 zcmeHK%}T>S5ZY^U&3yN5JD?`zeq@q5W+wurZUicBXV5ogrtmT4v@+@ zga_8E*9n7OJQ0nKX=FgZJIfpNI$rqs{S^vtG&Pf+ottMY%d(5vOQ$6UPVC10Ud8R6 z%iggYw>)E?2oZkRu6G(V23NV|eG$cO5H-3gIq(}0a&ZO&ElEE=NfkBBC zHj7SOv^!(B_O?Z}YIe+)A2yy3>o0rb5&wsgPV2jwuAOcaim0iwKP`eLGJp&q15?X@ zIXBtD)IK3T7&3qi{1pT8eBhuGZG(wMb#y?ZN&vt#xRrn{YYCad4cZ11jW7a2bt<4v zrKZH7IvxDn#MuTDjXIrBQ+!afGBp(nRjb4NT!j;AYs3~AKnA`tFr&LUdH)~$T>pPf z!X7e!4E!qwXu9fDE3hSVw@z$M-nAUG1}a6yB^p0cz))8)#LBC96;uiMxikQ6gNa7) QfZ&gSq5&IZ;8z)V2P|u0SpWb4 literal 0 HcmV?d00001 diff --git a/source/images/4/schedule.png b/source/images/4/schedule.png new file mode 100644 index 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