From 81ab575e526c572cc897e527fe81b3c5beaab0f4 Mon Sep 17 00:00:00 2001 From: Maxwell Kapral Date: Mon, 1 May 2023 22:16:09 -0700 Subject: [PATCH] v3 --- data/QPU.ipynb | 640 ++++++++++++++++++++++++++++++++++++++---------- data/qpu.v3.pdf | Bin 0 -> 175638 bytes 2 files changed, 505 insertions(+), 135 deletions(-) create mode 100644 data/qpu.v3.pdf diff --git a/data/QPU.ipynb b/data/QPU.ipynb index 3dcd9be..f05add7 100644 --- a/data/QPU.ipynb +++ b/data/QPU.ipynb @@ -9,13 +9,14 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## Initial Setup" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -44,7 +45,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -80,7 +80,7 @@ "source": [ "# linear GHZ container\n", "linear = {\n", - " 'circuit': None,\n", + " 'circuit': { 'draw': None, 'execute': None},\n", " 'transpiled': None,\n", " 'job': None,\n", " 'result': None,\n", @@ -90,7 +90,7 @@ "\n", "# logarithmic GHZ container\n", "log = {\n", - " 'circuit': None,\n", + " 'circuit': { 'draw': None, 'execute': None},\n", " 'transpiled': None,\n", " 'job': None,\n", " 'result': None,\n", @@ -101,16 +101,6 @@ "# ideal shots per state\n", "isps = 512\n", "\n", - "# parallel sections\n", - "init = [ 0, 1, 2 ]\n", - "i = len(init)\n", - "k = 1\n", - "while len(init) <= N:\n", - " init += [i] * 2**k\n", - " i += 1\n", - " k += 1\n", - "s = init[N]\n", - "\n", "# IBMQ Mock Backend\n", "if N > 0:\n", " backend['name'] = re.sub(r'(_|fake|v\\d)', ' ', backend['device'].backend_name.lower()).title()\n", @@ -128,11 +118,12 @@ ] }, { - "attachments": {}, "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "## Generate $|\\text{GHZ}_N\\rangle$ Circuits1" + "## Generate $|\\text{GHZ}_N\\rangle$ Circuits1" ] }, { @@ -148,7 +139,7 @@ "metadata": {}, "outputs": [], "source": [ - "def linear_complexity_GHZ(N: int) -> QuantumCircuit:\n", + "def linear_complexity_GHZ(N: int, measure: bool = True) -> QuantumCircuit:\n", " if not isinstance(N, int):\n", " raise TypeError(\"Only integer arguments accepted.\")\n", " if N < 1:\n", @@ -158,7 +149,8 @@ " c.h(0)\n", " for i in range(1, N):\n", " c.cx(i-1, i)\n", - " c.measure_active()\n", + " if measure:\n", + " c.measure_active()\n", " return c" ] }, @@ -169,9 +161,9 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "
" ] }, "execution_count": 5, @@ -180,8 +172,9 @@ } ], "source": [ - "linear['circuit'] = linear_complexity_GHZ(N)\n", - "linear['circuit'].draw(output='mpl', fold=-1, initial_state=True)" + "linear['circuit']['execute'] = linear_complexity_GHZ(N)\n", + "linear['circuit']['draw'] = linear_complexity_GHZ(N, False)\n", + "linear['circuit']['draw'].draw(output='mpl', fold=-1, initial_state=True)" ] }, { @@ -228,7 +221,7 @@ "metadata": {}, "outputs": [], "source": [ - "def log_complexity_GHZ(N: int) -> QuantumCircuit:\n", + "def log_complexity_GHZ(N: int, measure: bool = True) -> QuantumCircuit:\n", " if not isinstance(N, int):\n", " raise TypeError(\"Only an integer argument is accepted.\")\n", " if N < 1:\n", @@ -243,8 +236,9 @@ " qubits_affected = gate.qubits\n", " if all(old_circuit.find_bit(qubit).index < new_num_qubits for qubit in qubits_affected):\n", " new_circuit.append(gate[0], [old_circuit.find_bit(qubit).index for qubit in qubits_affected])\n", - " new_circuit.measure_active()\n", - " return new_circuit\n" + " if measure:\n", + " new_circuit.measure_active()\n", + " return new_circuit" ] }, { @@ -254,9 +248,9 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "
" ] }, "execution_count": 8, @@ -265,8 +259,9 @@ } ], "source": [ - "log['circuit'] = log_complexity_GHZ(N)\n", - "log['circuit'].draw(output='mpl', fold=-1, initial_state=True)" + "log['circuit']['execute'] = log_complexity_GHZ(N)\n", + "log['circuit']['draw'] = log_complexity_GHZ(N, False)\n", + "log['circuit']['draw'].draw(output='mpl', fold=-1, initial_state=True)" ] }, { @@ -277,25 +272,353 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Quantum Simulation" + "## Computational Cost Analysis" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### Device" + "### Cost of Hadamard (H) Gate2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, + "outputs": [], + "source": [ + "H_cost = 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cost of CNOT (CX) Gate2" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "CX_cost = 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Program Cost" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{equation*}72\\end{equation*}" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "T_cost = ((N - 1) * CX_cost) + H_cost\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{T_cost}\\\\end{{equation*}}\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Program Sections (`s`)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def program_sections() -> int:\n", + " init = [ 0, 1, 2 ]\n", + " i = len(init)\n", + " k = 1\n", + " while len(init) <= N:\n", + " init += [i] * 2**k\n", + " i += 1\n", + " k += 1\n", + " return init[N]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{equation*}5\\end{equation*}" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = program_sections()\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{s}\\\\end{{equation*}}\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cost per Section" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def cost_per_section() -> list:\n", + " init = [ H_cost ]\n", + " _N = N - 1\n", + " k = 0\n", + " while len(init) < (s - 1):\n", + " init.append(CX_cost * 2**k)\n", + " _N -= 2**k\n", + " k += 1\n", + " if _N > 0:\n", + " init.append(CX_cost * _N)\n", + " return init" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{equation*}[2, 5, 10, 20, 35]\\end{equation*}" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "section_cost_list = cost_per_section()\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{section_cost_list}\\\\end{{equation*}}\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parallel v. Sequential Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gates Per Section" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def gates_per_section() -> list:\n", + " if s == 1:\n", + " return [1]\n", + " elif s == 2:\n", + " return [1, 1]\n", + " else:\n", + " init = [1, 1]\n", + " for i in range(len(init), s):\n", + " init.append(int(section_cost_list[i] / CX_cost))\n", + " return init" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{equation*}[1, 1, 2, 4, 7]\\end{equation*}" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_gates_list = gates_per_section()\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{num_gates_list}\\\\end{{equation*}}\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Percent Sequential" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def add_sequential_portions() -> float:\n", + " seq = 0\n", + " for cost, gates in zip(section_cost_list, num_gates_list):\n", + " seq += (1 / gates) * (cost / T_cost)\n", + " return seq" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{equation*}30.555555555555557\\%\\end{equation*}" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sequential_portion = add_sequential_portions()\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{sequential_portion * 100}\\%\\\\end{{equation*}}\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Percent Parallel" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def add_parallel_portions() -> float:\n", + " par = 0\n", + " for cost, gates in zip(section_cost_list, num_gates_list):\n", + " par += ( (gates - 1) / gates ) * (cost / T_cost)\n", + " return par" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{equation*}69.44444444444444\\%\\end{equation*}" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parallel_portion = add_parallel_portions()\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{parallel_portion * 100}\\%\\\\end{{equation*}}\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Quantum Simulation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Device" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { @@ -308,7 +631,7 @@ "" ] }, - "execution_count": 9, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -324,29 +647,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Transpile Circuits" + "### Transpile Circuits3" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "linear['transpiled'] = transpile(linear['circuit'], backend['simulator'], scheduling_method=\"asap\", optimization_level=0)" + "linear['transpiled'] = transpile(linear['circuit']['execute'],\n", + " backend['simulator'],\n", + " scheduling_method=\"asap\",\n", + " optimization_level=0)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ - "log['transpiled'] = transpile(log['circuit'], backend['simulator'], scheduling_method=\"asap\", optimization_level=0)" + "log['transpiled'] = transpile(log['circuit']['execute'],\n", + " backend['simulator'],\n", + " scheduling_method=\"asap\",\n", + " optimization_level=0)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -355,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -364,7 +692,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -372,7 +700,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -381,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -390,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -398,7 +725,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -409,88 +735,76 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Error Analysis" + "## Speed-Up Analysis" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### Linear Error Percentage" + "### Run-Times" ] }, { - "attachments": {}, "cell_type": "markdown", - "metadata": { - "tags": [] - }, + "metadata": {}, "source": [ - "##### State $|0\\rangle$" + "#### Linear" ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [] - }, + "execution_count": 29, + "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "\\begin{equation*}70.703125\\%\\end{equation*}" + "\\begin{equation*}24.292431592941284\\space\\text{seconds}\\end{equation*}" ], "text/plain": [ "" ] }, - "execution_count": 16, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "try:\n", - " linear['error']['0'] = abs((linear['result'].get_counts()['0' * N] - isps) / isps)\n", - "except KeyError:\n", - " linear['error']['0'] = 1\n", - "Latex(f\"\"\"\\\\begin{{equation*}}{linear['error']['0'] * 100}\\%\\\\end{{equation*}}\"\"\")\n" + "linear['time'] = linear['result'].time_taken\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{linear['time']}\\\\space\\\\text{{seconds}}\\\\end{{equation*}}\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "##### State $|1\\rangle$" + "#### Log" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "\\begin{equation*}95.1171875\\%\\end{equation*}" + "\\begin{equation*}9.33341646194458\\space\\text{seconds}\\end{equation*}" ], "text/plain": [ "" ] }, - "execution_count": 17, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "try:\n", - " linear['error']['1'] = abs((linear['result'].get_counts()['1' * N] - isps) / isps)\n", - "except KeyError:\n", - " linear['error']['1'] = 1\n", - "Latex(f\"\"\"\\\\begin{{equation*}}{linear['error']['1'] * 100}\\%\\\\end{{equation*}}\"\"\")" + "log['time'] = log['result'].time_taken\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{log['time']}\\\\space\\\\text{{seconds}}\\\\end{{equation*}}\"\"\")" ] }, { @@ -498,236 +812,286 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Logarithmic Error Percentage" + "### Theoretical Max Speed-Up (Amdahl's Law4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### State $|0\\rangle$" + "$$\\lim_{F\\to\\infty} S_{\\text{latency}} = \\dfrac{1}{S_{\\text{eq}} + \\dfrac{P}{F}} = \\dfrac{1}{S_{\\text{eq}}}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- $S_{\\text{eq}}$ represents the portions of the program running sequentially\n", + "- $P$ represents the portions of the program running in parallel\n", + "- $F$ represents the level of concurrency (i.e. number of cores in classical computing)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "\\begin{equation*}62.3046875\\%\\end{equation*}" + "\\begin{equation*}3.2727272727272725\\end{equation*}" ], "text/plain": [ "" ] }, - "execution_count": 18, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "try:\n", - " log['error']['0'] = abs((log['result'].get_counts()['0' * N] - isps) / isps)\n", - "except KeyError:\n", - " log['error']['0'] = 1\n", - "Latex(f\"\"\"\\\\begin{{equation*}}{log['error']['0'] * 100}\\%\\\\end{{equation*}}\"\"\")" + "Latex(f\"\"\"\\\\begin{{equation*}}{1 / sequential_portion}\\\\end{{equation*}}\"\"\")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "#### State $|1\\rangle$" + "### Observed Speed-Up Factor ($S_{\\text{latency}}$)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "\\begin{equation*}84.1796875\\%\\end{equation*}" + "\\begin{equation*}2.602737346178599\\end{equation*}" ], "text/plain": [ "" ] }, - "execution_count": 19, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "try:\n", - " log['error']['1'] = abs((log['result'].get_counts()['1' * N] - isps) / isps)\n", - "except KeyError:\n", - " log['error']['1'] = 1\n", - "Latex(f\"\"\"\\\\begin{{equation*}}{log['error']['1'] * 100}\\%\\\\end{{equation*}}\"\"\")" + "S_latency = linear['time'] / log['time']\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{S_latency}\\\\end{{equation*}}\"\"\")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "---" + "### Approximated Level of Concurrency ($F$)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Speed-Up Analysis" + "$$F = \\dfrac{P\\cdot S_{\\text{latency}}}{1 - S_{\\text{eq}}\\cdot S_{\\text{latency}}}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{equation*}8.828956848467138\\end{equation*}" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "F = (parallel_portion * S_latency) / (1 - (sequential_portion * S_latency))\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{F}\\\\end{{equation*}}\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Run-Times" + "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Linear" + "## Error Analysis" ] }, { - "cell_type": "code", - "execution_count": 20, + "cell_type": "markdown", "metadata": {}, + "source": [ + "### Linear Error Percentage" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "##### State $|0\\rangle$" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { "text/latex": [ - "\\begin{equation*}30.28240442276001\\space\\text{seconds}\\end{equation*}" + "\\begin{equation*}63.8671875\\%\\end{equation*}" ], "text/plain": [ "" ] }, - "execution_count": 20, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "linear['time'] = linear['result'].time_taken\n", - "Latex(f\"\"\"\\\\begin{{equation*}}{linear['time']}\\\\space\\\\text{{seconds}}\\\\end{{equation*}}\"\"\")" + "try:\n", + " linear['error']['0'] = abs((linear['result'].get_counts()['0' * N] - isps) / isps)\n", + "except KeyError:\n", + " linear['error']['0'] = 1\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{linear['error']['0'] * 100}\\%\\\\end{{equation*}}\"\"\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Log" + "##### State $|1\\rangle$" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "\\begin{equation*}11.354429244995117\\space\\text{seconds}\\end{equation*}" + "\\begin{equation*}91.9921875\\%\\end{equation*}" ], "text/plain": [ "" ] }, - "execution_count": 21, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "log['time'] = log['result'].time_taken\n", - "Latex(f\"\"\"\\\\begin{{equation*}}{log['time']}\\\\space\\\\text{{seconds}}\\\\end{{equation*}}\"\"\")" + "try:\n", + " linear['error']['1'] = abs((linear['result'].get_counts()['1' * N] - isps) / isps)\n", + "except KeyError:\n", + " linear['error']['1'] = 1\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{linear['error']['1'] * 100}\\%\\\\end{{equation*}}\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Amdahl's Law" + "### Logarithmic Error Percentage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Parallel Portion" + "#### State $|0\\rangle$" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "\\begin{equation*}\n", - " P = \\dfrac{s\\left(1 - \\dfrac{1}{S_\\text{latency}}\\right)}{s - 1} = \\dfrac{5\\left(1 - \\dfrac{1}{2.667012473225644}\\right)}{4} = 66.96949529379455\\%\n", - " \\end{equation*}\n", - " " + "\\begin{equation*}63.671875\\%\\end{equation*}" ], "text/plain": [ "" ] }, - "execution_count": 22, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "S_latency = linear['time'] / log['time']\n", - "P = (N * (1 - (1 / S_latency))) / (N - 1)\n", - "Latex(f\"\"\"\\\\begin{{equation*}}\n", - " P = \\\\dfrac{{s\\\\left(1 - \\\\dfrac{{1}}{{S_\\\\text{{latency}}}}\\\\right)}}{{s - 1}} = \\\\dfrac{{{s}\\\\left(1 - \\\\dfrac{{1}}{{{S_latency}}}\\\\right)}}{{{s - 1}}} = {P * 100}\\%\n", - " \\\\end{{equation*}}\n", - " \"\"\")" + "try:\n", + " log['error']['0'] = abs((log['result'].get_counts()['0' * N] - isps) / isps)\n", + "except KeyError:\n", + " log['error']['0'] = 1\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{log['error']['0'] * 100}\\%\\\\end{{equation*}}\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Sequential Portion" + "#### State $|1\\rangle$" ] }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "tags": [] - }, + "execution_count": 37, + "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "\\begin{equation*}S_\\text{EQ} = 1 - P = 33.03050470620544\\%\\end{equation*}" + "\\begin{equation*}89.6484375\\%\\end{equation*}" ], "text/plain": [ "" ] }, - "execution_count": 23, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "S_EQ = 1 - P\n", - "Latex(f\"\"\"\\\\begin{{equation*}}S_\\\\text{{EQ}} = 1 - P = {S_EQ * 100}\\%\\\\end{{equation*}}\"\"\")" + "try:\n", + " log['error']['1'] = abs((log['result'].get_counts()['1' * N] - isps) / isps)\n", + "except KeyError:\n", + " log['error']['1'] = 1\n", + "Latex(f\"\"\"\\\\begin{{equation*}}{log['error']['1'] * 100}\\%\\\\end{{equation*}}\"\"\")" ] }, { @@ -749,7 +1113,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "1. [Cruz, Diogo, Romain Fournier, Fabien Gremion, Alix Jeannerot, Kenichi Komagata, Tara Tosic, Jarla Thiesbrummel, et al. (2018). Efficient Quantum Algorithms for $GHZ$ and $W$ States, and Implementation on the IBM Quantum Computer. ArXiv.](https://doi.org/10.48550/ARXIV.1807.05572)" + "1. [Cruz, Diogo, Romain Fournier, Fabien Gremion, Alix Jeannerot, Kenichi Komagata, Tara Tosic, Jarla Thiesbrummel, et al. (2018). Efficient Quantum Algorithms for $GHZ$ and $W$ States, and Implementation on the IBM Quantum Computer. ArXiv. 1-2.](https://doi.org/10.48550/ARXIV.1807.05572)\n", + "\n", + "2. [Lee, Soonchil & Lee, Seong-Joo & Kim, Taegon & Lee, Jae-Seung & Biamonte, Jacob & Perkowski, Marek. (2006). The cost of quantum gate primitives. Journal of Multiple-Valued Logic and Soft Computing. 12. 571.](https://www.researchgate.net/publication/216778423_The_cost_of_quantum_gate_primitives)\n", + "\n", + "3. [Scheduling Methods](https://qiskit.org/documentation/stable/0.42/tutorials/circuits_advanced/07_pulse_scheduler.html#Scheduler-methods)\n", + "\n", + "4. [Amdahl's Law Definition](https://en.wikipedia.org/wiki/Amdahl%27s_law#Definition)" ] } ], diff --git a/data/qpu.v3.pdf b/data/qpu.v3.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e514024368e380151912d6c84ad0962471c3a540 GIT binary patch literal 175638 zcmeFZ1yt4Twl0n!qNIvQ3o0Sq-AG9HqD#7^I~3`T1=8K!oeI)OEIOp6yX(HV_qqG~ z&e?mPeb0ZK{}^Z7yT)++Q097bzVm(NGoK}?tdIx|11$?O>GIz888Ra?F+H)>dsAd? zZaNVYb2|eYIuUbSI|CsDJu7_!I!OadBRgYaMkXc(US4EdI~xOC3uLFMS+&_vsv5NB zU4=fB!t`4rVwL-d@d(S7-c2Yrx+P0dK~h5_TFb{0;^`y~<)s_QYjmkS!z2s|tAD<;^AAdtLraNeOoNu=X*HcOj}o z<|VX%kN%0FJOpR6;^Cvdq9+ub!xDVn>=Y3-F47N*Mfj+m;S0c-qVY}Zk@3Za3zKJt z%B*tV?4YwtE8@8%Cx;Xkg&T%V^t`81@Mwc*jg6JL1$R>ktW-rlH~RMxf2)4-g%HGaJedky8q=e>T7v;20$j1Mi=M z6j8#8l%G3}=YQ-qc)Ii4?m2jUNp8MDN`Tjkh;*>L$lw+s4~`|1+&!LrJJV|%E;Y<3 znfl3^bA7Dr$y|1~3tgt58OsKjytiymW?BUz+u{ccZ2Itbeti+SB)je;kr2)Ml(Vo_ z@}Gk4`wSR`NA2Ga)(1zQxm@!~t(mywL8N$HDr$?>SV%F<9#ojWwBBS@lOKL-YgQ%T zinHcUWcB*P<%SNFn 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